OPTIMIZATION OF XANTHAN BIOSYNTHESIS IN THE GRANULE MATRIX IN AEROBIC GRANULAR SLUDGE WASTEWATER TREATMENT SYSTEMS by Manveer Kaur B.Tech., Jaipur National University, 2019 THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE IN ENGINEERING UNIVERSITY OF NORTHERN BRITISH COLUMBIA April 2025 © Manveer Kaur, 2025 Abstract The aerobic granular sludge (AGS) biotechnology provides the opportunity to recover resources such as phosphorus, alginate-like exopolysaccharides, polyhydroxyalkanoates, tryptophan, etc. from wastewater. Recently, xanthan, a versatile biopolymer with applications in the food, oil, geotechnical, and biomedical industries, has been identified in the aerobic granule matrix. The production of xanthan from waste is encouraged because conventional processes for its production are costly and require specialized laboratories. This study aimed at determining the effect of organic loading rate (OLR), chemical oxygen demand (COD) to nitrogen (COD/N) ratio, and feeding strategy on xanthan biosynthesis in the aerobic granule matrix during wastewater treatment in AGS-based wastewater treatment systems. Using Taguchi fractional factorial design, nine experimental runs were conducted to evaluate the effects of these parameters on xanthan production, treatment performance, and AGS system stability. Results indicated that OLR showed a critical influence on xanthan production. Xanthan yield increased with increasing OLR, attaining an optimum yield of 41 ± 7 mg xanthan/g biomass. A significant positive correlation (r = 0.831) was found between OLR and xanthan yield (p = 0.006). A negative correlation (r = -0.512) was obtained between COD/N ratio and xanthan yield, which suggests that increasing the COD/N ratio results in decreased xanthan yield. This result was not statistically significant at 95% confidence level (p=0.158). The feeding strategy had a very weak positive correlation (r = 0.042) with xanthan yield. This result was not statistically significant at 95% confidence level (p = 0.915), implying that variations in the feeding regime has minimal effect on xanthan production. Taguchi mean effect analysis showed that OLR of 2.1 kg COD/m³∙d and C/N ratio of 10 were optimal for xanthan production (41 ± 7 mg xanthan/g biomass) in the aerobic granule matrix. Additionally, the AGS system achieved COD, ammonia-nitrogen, and phosphorus removal efficiencies of 95 ± 5%, 73 ± ii 23%, and 72 ± 18%, respectively. System stability was also maintained throughout the experimental period as both 5-min sludge volume index (SVI5) and SVI30 values ranged from 20 ± 2 – 30 ± 2 mL/g and the SVI30/SVI5 ratio was consistently between 0.9 and 1.0. These findings contribute to optimizing AGS systems for sustainable xanthan recovery from wastewater, offering an efficient alternative for biopolymer production along with efficient wastewater treatment. iii Table of Contents Abstract ........................................................................................................................................... ii Table of Contents ........................................................................................................................... iv List of Tables ................................................................................................................................ vii List of Figures .............................................................................................................................. viii Glossary ......................................................................................................................................... ix Acknowledgments........................................................................................................................... x Contributions.................................................................................................................................. xi Chapter 1 Introduction .................................................................................................................... 1 1.1 Background ..................................................................................................................... 1 1.2 Research rationale and questions .................................................................................... 3 1.3 Research aim and objectives ........................................................................................... 3 1.4 Organization of thesis ..................................................................................................... 4 Chapter 2 Literature Review ........................................................................................................... 6 2.1. Aerobic granular sludge .................................................................................................. 6 2.2. Circular economy in wastewater treatment..................................................................... 6 2.3. Xanthan ........................................................................................................................... 9 2.3.1. Structure of xanthan .................................................................................................... 9 2.3.2. Physical and chemical properties of xanthan ............................................................ 10 2.3.3. Industrial applications of xanthan ............................................................................. 12 iv 2.3.4. Conventional bioproduction of xanthan.................................................................... 13 2.4. Potential for xanthan recovery from AGS systems....................................................... 17 Chapter 3 Materials and Methods ................................................................................................. 22 3.1. Experimental set-up ...................................................................................................... 22 3.2. Feed wastewater ............................................................................................................ 24 3.3. Seed sludge ................................................................................................................... 24 3.4. Experimental design...................................................................................................... 24 3.5. Analytical methods ....................................................................................................... 27 3.5.1. Biomass ..................................................................................................................... 27 3.5.2. COD .......................................................................................................................... 28 3.5.3. Nitrogen .................................................................................................................... 29 3.5.4. Phosphorus ................................................................................................................ 30 3.5.5. Extraction of EPS ...................................................................................................... 31 3.5.6. Xanthan recovery and quantification ........................................................................ 32 3.5.7. Fourier Transform Infrared (FT-IR) spectroscopy ................................................... 32 3.5.8. Nuclear magnetic resonance (NMR) spectroscopy ................................................... 33 3.6. Statistical analysis ......................................................................................................... 34 Chapter 4 Results and Discussion ................................................................................................. 35 4.1. Biomass characteristics ................................................................................................. 35 4.2. Treatment Performance ................................................................................................. 39 v 4.2.1. Organic matter removal ............................................................................................ 39 4.2.2. Nitrogen removal ...................................................................................................... 42 4.2.3. Phosphorus removal .................................................................................................. 44 4.3. Xanthan recovery .......................................................................................................... 47 4.3.1 Xanthan yield and impact of the operating factors ....................................................... 47 4.3.2. Identification of xanthan extract .................................................................................. 52 4.4. Statistical analysis and optimization ............................................................................. 56 4.4.1. Pearson correlation.................................................................................................... 56 4.4.2. Optimization ............................................................................................................. 57 Chapter 5 Conclusions and Recommendations............................................................................. 60 5.1. Conclusions ................................................................................................................... 60 5.2. Limitations of the study ................................................................................................ 61 5.3. Recommendations ......................................................................................................... 62 References ..................................................................................................................................... 63 Appendices .................................................................................................................................. - 1 Appendix A. COD calibration curve ...................................................................................... - 1 Appendix B. Wet xanthan precipitate in the 50 mL vial ........................................................ - 2 Appendix C. Permission to include Published Paper in the Thesis ........................................ - 2 Appendix D. Permission to include Published Paper from the Co-authors ............................ - 3 - vi List of Tables Table 2.1 Optimal values of parameters for xanthan production.................................................. 15 Table 3.1 Dimensions of the AGS bioreactor ............................................................................... 22 Table 3.2 Experimental run combinations using Taguchi Design ................................................ 25 Table 3.3 Expected values of COD, ammonia, and phosphorus for each run .............................. 26 Table 4.1 Pearson correlation ...................................................................................................... 57 Table 4.2 Response for means and S/N ratio ................................................................................ 58 vii List of Figures Figure 2.1 Chemical structure of xanthan ..................................................................................... 10 Figure 3.1 Schematic representation of the AGS bioreactor ........................................................ 23 Figure 3.2 An experimental setup of AGS bioreactor in a laboratory .......................................... 23 Figure 3.3 FT-IR spectrometer for identification of extracted xanthan ........................................ 33 Figure 4.1 Biomass characteristics ............................................................................................... 37 Figure 4.2 Biomass settleability.................................................................................................... 38 Figure 4.2 COD removal efficiencies for all the experimental runs ............................................. 39 Figure 4.3 COD removal profiles for all experimental runs ......................................................... 41 Figure 4.4 Ammonia removal efficiencies for all experimental runs ........................................... 43 Figure 4.5 Nitrogen removal profiles for all experimental runs ................................................... 44 Figure 4.6 Phosphorus removal efficiencies in all experimental runs .......................................... 45 Figure 4.7 Phosphorus removal profiles for all experimental runs ............................................... 46 Figure 4.8: Xanthan yield a) on different days of 10, 20, and 30, b) average yield for the nine bioreactors at steady state ............................................................................................................. 47 Figure 4.9 FTIR of xanthan reference (dark color) and xanthan extract ...................................... 53 Figure 4.10 1HNMR spectra of xanthan reference (upper) and xanthan extract (lower), recorded in D2O at 400 MHz ........................................................................................................................... 55 Figure 4.11: Main effects plots for a) means, b) S/N ratio ........................................................... 59 viii Glossary AGS Aerobic granular sludge ALE Alginate-like exopolysaccharide ANOVA Analysis of variance CE Circular economy COD Chemical oxygen demand CAS Conventional activated sludge DNA Deoxyribonucleic acid DO Dissolved oxygen EPS Extracellular polymeric substances MLSS Mixed liquor suspended solids MLVSS Mixed liquor volatile suspended solids OLR Organic loading rate ORP Oxidation reduction potential PHA Polyhydroxyalkanoate PS Polysaccharides PN Proteins RRS Resource recovery systems SEM Scanning electron microscopy SVI Sludge volume index SRT Solids retention time TSS Total suspended solids WRRF Water resource recovery facilities WWTP Wastewater treatment plant XG Xanthan gum ix Acknowledgments I would like to extend my deepest gratitude to my supervisor, Dr. Oliver Iorhemen, and members of my supervisory committee, Dr. Jianbing Li and Dr. Kalindi Morgan, for their invaluable support, insightful guidance, and the knowledge they have generously shared. Their expertise and dedication have profoundly shaped my understanding of this field, making my academic journey both enriching and inspiring. Additionally, I am deeply grateful to Stefan Smulik for his expertise in processing the instruments. I am also grateful to my friends and lab mates at the School of Engineering for their camaraderie and support. A special mention goes to Adedoyin Adekunle and Jibrael Odoom for their unwavering encouragement and collaboration, which have significantly contributed to my research progress and personal growth. I also sincerely thank the School of Engineering, its dedicated staff, and the University of Northern British Columbia for fostering an exceptional academic environment. The university’s support and resources have been instrumental in helping me achieve this significant milestone. Lastly, I am profoundly indebted to my wonderful family for their prayers, encouragement, and financial support. Their unwavering belief in me has been the foundation of my success, and I could not have reached this point without their love and steadfast support. x Contributions The following scholarly contributions resulted from this MASc work: 1. Kaur, M., Guo, B., Iorhemen, O.T. (2024). Potential for the recovery of xanthan from aerobic granular sludge wastewater systems–A review. Resources, Conservation and Recycling. 207: 107688. 2. Kaur, M., Vesuwe, N., dos Santos, A.B., Iorhemen, O.T. Effect of organic loading rate, carbon-to-nitrogen ratio, and feeding strategy on xanthan biosynthesis in the aerobic granule matrix in aerobic granular sludge wastewater treatment systems. (in preparation) Details of contributions from the candidate and co-authors are listed below: 1. In this publication, the candidate conducted an extensive review of related literature on circular economy in wastewater treatment, aerobic granular sludge (AGS), and the potential of xanthan recovery from AGS wastewater treatment systems. Dr. Bing Guo provided significant contributions in terms of advice and corrections during manuscript preparation. Dr. Oliver Iorhemen provided significant contributions in terms of advice and corrections during manuscript preparation. 2. In this contribution, the candidate designed and performed most of the experiments, collected data, performed data analysis, and wrote the manuscript. Rebecca Vesuwe aided in data analysis and guidance towards writing the manuscript draft. Dr. Andri Bezerra dos Santos assisted in the development of the xanthan recovery protocol and review of the draft. Dr. Oliver Iorhemen contributed with research guidance, funding acquisition, comments during manuscript writing, and proofreading. xi Chapter 1 Introduction 1.1 Background Due to rapid population growth and increased need for freshwater for a variety of uses, the amount of wastewater produced worldwide has been increasing dramatically in recent years (Jaffar Abdul Khaliq et al., 2017). Consequently, there is a greater need for efficient wastewater treatment technology and sludge management. Sludge produced every day by wastewater treatment plants (WWTPs) is a major environmental issue that poses severe risks to health. The composition of sludge, which includes both soluble and insoluble contaminants, accounts for half of WWTPs’ total operation and maintenances costs (Sharma et al., 2022; Wahaab et al., 2020). Therefore, efficient sludge management is essential for reducing pollution in the environment and increasing the treatment process's cost-effectiveness. There has been an evolution of WWTPs over the last several decades, transitioning them to water resource recovery facilities (WRRF) , converting wastewater from a pollutant to a useful resource (Guest et al., 2009). WRRF treat wastewater to produce clean water, nutrients, renewable energy, and other bio-based materials. The biomass generated from wastewater treatment is a vital resource for the production of chemical goods, biomaterials, and biofuels (Mohan et al., 2016). Recent advancements in sewage treatment technology make it possible to extract valuable products, increasing the possibility for resource recovery-focused novel treatment solutions. Among various technologies, the aerobic granular sludge (AGS) biotechnology is one of the cutting-edge technologies that simultaneously remove pollutants and recover resources from wastewater (de Sousa Rollemberg et al., 2020). Since the conventional activated sludge (CAS) process exhibit problems with solids-liquid separation, high aeration requirements resulting in significant energy consumption, and high cost 1 associated with managing and recycling large volumes of sludge (Kadam et al., 2023; Y. Nancharaiah & Reddy, 2018; Zhang & Liu, 2022). AGS biotechnology has evolved with immense potential to overcome these drawbacks. In comparison to CAS, AGS shows good biomass retention, provides the potential for resource recovery, and significantly lowers the land footprint (50–75%) and energy demand (20–25%) (Ferreira et al., 2021). Aerobic granules are characterized by a high concentration of extracellular polymeric substances (EPS), which are essential for the formation and stability of the granules (X. Liu et al., 2022). EPS is composed of polysaccharides (PS), proteins (PN), glycoproteins, lipids, nucleic acids, and humic acid (Lin et al., 2014). Therefore, waste aerobic granules containing EPS could serve as a valuable source for recovering various useful resources. Studies on recovering phosphorus, alginate-like exopolysaccharide (ALE), tryptophan, polyhydroxyalkanoates (PHAs), etc., from AGS biomass are already well advanced (X. Chen, J. Wang, Q. Wang, T. Yuan, et al., 2022; Dababat et al., 2023; de Carvalho et al., 2021; Ferreira et al., 2021). In addition to these resources, xanthan has been recently identified in the granule matrix (Ferreira et al., 2021). Xanthan is one of the polysaccharides produced from Xanthomonas spp. and has numerous uses in the food, chemicals, and oil sectors (Rashidi et al., 2023). The carbon sources (glucose, sucrose, etc) utilized in commercial xanthan production are expensive. Hence, the potential recovery of this valuable product from AGS is a path to cost savings. The recent identification of this by-product is worth highlighting since it is in high commercial demand. Thus, optimizing the biosynthesis of xanthan in AGS bioreactors and developing appropriate recovery techniques from waste aerobic granules will contribute to reducing the operational cost of the wastewater treatment plant. 2 1.2 Research rationale and questions Currently, there is no research on xanthan production and recovery from AGS wastewater treatment systems. Thus, it is worth exploring how changing process parameters would influence xanthan biosynthesis in the granule matrix during wastewater treatment. The optimization of key operational parameters such as organic loading rate (OLR), carbon source, carbon-to-nitrogen (COD/N) ratio, feeding strategy, pH, temperature, and aeration intensity for increased biosynthesis of xanthan in the granule matrix requires further studies. OLR indicates how much organic matter (typically measured as Chemical Oxygen Demand (COD)) is being fed into the biological treatment system per unit volume per day. Additionally, optimization needs to be done while maintaining the treatment performance and granule stability of AGS-based wastewater treatment systems. Understanding the important parameters governing xanthan production in AGS systems may result in a high yield in the aerobic granule matrix. As a result, the study aimed to respond to the following research questions: a) During wastewater treatment, does the rate of xanthan biosynthesis in the aerobic granule matrix change in response to different organic loading rates (OLR)? b) How do various feeding techniques affect xanthan production in AGS wastewater treatment systems? c) How is the production of xanthan in the aerobic granule matrix affected by the chemical oxygen demand (COD) to nitrogen (COD/N) ratio during wastewater treatment? 1.3 Research aim and objectives This research project aimed to optimize process parameters - OLR, COD/N ratio, and feeding strategy – that affect the biosynthesis of xanthan in the aerobic granule matrix during wastewater treatment. To achieve the aim, the specific objectives of the project were to: 3 a) Determine the influence of OLR on the rate of xanthan biosynthesis in the aerobic granule matrix during wastewater treatment. b) Determine the impact of different feeding strategies on xanthan biosynthesis in AGS wastewater treatment systems. c) Determine the effect of the COD/N ratio on the biosynthesis of xanthan in the aerobic granule matrix during wastewater treatment. 1.4 Organization of thesis This thesis comprises five chapters. Chapter 1, the current chapter, introduces the study, outlining the background, research rationale, key research questions, and objectives. It highlights the significance of optimizing process parameters to enhance xanthan biosynthesis in AGS-based wastewater treatment systems. Chapter 2 presents a comprehensive review of the existing literature on wastewater treatment technologies, the evolution of AGS systems, and resource recovery approaches. Special emphasis is placed on the role of extracellular polymeric substances (EPS) in AGS and the potential for xanthan recovery from AGS biosolids. Chapter 3 describes the materials and methods used in the study. It details the experimental setup, process parameters, analytical techniques, and data collection methods employed to investigate xanthan biosynthesis under different operational conditions. Chapter 4 presents the results and discussion, analyzing the impact of key process parameters such as organic loading rate (OLR), COD/N ratio, and feeding strategy on xanthan production. Findings are interpreted in relation to existing literature, providing insights into AGS system optimization for enhanced resource recovery. 4 Chapter 5 concludes the thesis by summarizing the key findings, discussing their implications for wastewater treatment and resource recovery, and providing recommendations for future research to further advance AGS-based biotechnologies. Additionally, some limitations encountered in the course of the study have been presented in this section. 5 Chapter 2 Literature Review 2.1. Aerobic granular sludge Aerobic granular sludge has gained growing favor within the scientific and engineering circles due to its superior performance in terms of treatability, compactness, and settling ability compared to conventional activated sludge systems. Moreover, the process design is made more straightforward by AGS, as it achieves both biological treatment and biomass separation from treated wastewater in one treatment tank (Y. V. Nancharaiah & Sarvajith, 2019). The tridimensional matrix of aerobic granular sludge results from the aggregation of microbial cells and even helps in the removal of resistant and toxic compounds such as heavy metals, nuclear waste, phenols, and pharmaceutical compounds, including nutrients from the complete-scale treatment of wastewater (Muñoz-Palazón et al., 2021; Muñoz-Palazon et al., 2019). Presently, due to its enormous advantages, Nereda® AGS technology is globally replacing numerous conventional full-scale systems for the treatment of urban and industrial wastewater (Guo et al., 2020; Niermans et al., 2014). 2.2. Circular economy in wastewater treatment The circular economy (CE) idea started gaining ground in the 1970s. CE is the economic system “that is restorative and regenerative by design, and which aims to keep products, components, and materials at their highest utility and value” (Kirchherr et al., 2023). This concept has attracted substantial attention nowadays as a feasible solution to the pressing concerns of resource depletion and environmental degradation. Wastewater treatment is a critical area where the CE concept can be implemented. Once regarded as a cumbersome waste product, wastewater is today recognized as a collection of precious resources that can be recovered and used effectively with innovative technologies (Kehrein et al., 2020). The conventional method of wastewater 6 treatment comprises collecting, treating, and disposing of wastewater, which frequently results in the waste of important resources and energy. The CE framework, on the other hand, strives to close the loop by promoting resource recovery, recycling, and reuse. As the global population keeps growing and resource stocks are exhausted as living standards and consumption rates rise, the CE concept is becoming more and more crucial. An increase in global resource consumption results in more waste resources with severe repercussions. P is an example of an essential raw material that is under threat of depletion. This rise in the global economy presents new opportunities, particularly in harnessing the abundance of material resources in wastewater. The usage of traditional sludge disposal techniques, such as landfill, direct land application, or incineration, is no longer encouraged because of reverse environmental impact, strict restrictions, pressure from environmental officials, and public outrage. Moreover, sludge disposal processes at WWTPs result in 40 % of the overall greenhouse gas (GHG) emissions; however, if the CE concept is adopted, this number could decrease (Brown et al., 2010; Pilli et al., 2015). The transition to WRRFs would enable the recovery of resources that can be produced in quantities and at prices that satisfy current market demand, in addition to addressing anticipated future resource scarcity. In previous studies, descriptive perspectives for energy recovery (Gherghel et al., 2019; Kundu et al., 2022) and GHG emission (Pahunang et al., 2021; Rufí-Salís et al., 2022) were discussed for CE in WWTPs. To increase CE, alternative treatment options are being pursued due to insufficient treatment and high energy costs in conventional WWTPs (Abinndan et al., 2018; Al-Jabri et al., 2020). With the advent of AGS biotechnology, the material resource recovery potential of wastewater to revolutionize the wastewater management industry and create sustainable development has received much interest (Semaha et al., 2023). AGS is ideally aligned with the CE's key objectives 7 of minimizing waste output, maximizing resource recovery, and closing material loops. In addition to wastewater reclamation and reuse, nutrients, biofuels, bioplastics, and other value-added products can be recovered from wastewater treatment facilities. The recovery of resources from WWTPs can assist in reducing price volatility created by inequalities in the availability and distribution of natural resources worldwide, such as P (Reijnders, 2014). Several studies have been conducted on the process of recovery of P, tryptophan, ALE, and PHA from AGS systems (Chen et al., 2022; de Carvalho et al., 2021; Ferreira et al., 2021). Additionally, granular sludge containing high P bioavailability is considered for the synthesis of phosphate biofertilizers (Zhao et al., 2019). Furthermore, if processing costs are omitted, ALE has a market value of US$ 80-140/kg (Dos Santos et al., 2022) and could provide potential yield in the range €1000–2000/t (Ferreira et al., 2021). If used before digestion, ALE extraction may be useful for managing sludge since it may be used as a pretreatment method to break down the EPS matrix and remove non-biodegradable EPS polysaccharides from the sludge (Nancharaiah, 2019). In the context of energy generation and resource recovery, approximately 100 biogas technology systems, 12 phosphate operations, and 2 cellulose recovery projects are currently functioning in the Netherlands (ERMF, 2017). The AGS biotechnology provides various other benefits by incorporating the CE idea into wastewater treatment, including reduced energy usage, lower carbon emissions, and less dependency on external resources (Philip, 2023). Therefore, chemical material and energy flows in the CE model can benefit from AGS technology in WRRFs. 8 2.3. Xanthan Xanthan gum (XG) is a prominent biopolymer used in various industrial sectors due to its specific physical properties. It exhibits high viscosity and pseudo-plasticity, even at lower concentrations (Bajić et al., 2014). Conventional XG production is costly because the process is energy-intensive, the raw materials (glucose or sucrose) are expensive, and the process requires specialized laboratories; as such, its production from waste is particularly encouraged (Palaniraj and Jayaraman, 2011). Thus, AGS could be considered a less expensive alternative source of xanthan that would reduce the cost of its production. Xanthan and other polysaccharides were already reported in the AGS systems (Ferreira et al., 2021). It can be used in the chemical (hydrocolloid manufacturing), food, medicine, and petroleum industries. 2.3.1. Structure of xanthan Xanthan is a naturally occurring anionic polysaccharide formed of a repetitive unit from a pentasaccharide with the molar proportions of D-mannose, D-glucose, and D-glucuronic acid as 2:2:1 as shown in Figure 2.1 (Elella et al., 2021). It is produced by the gram-negative phytopathogen bacteria named Xanthomonas campestris and is a suitable replacement for conventional gums derived from sea algae and plants (Jeeva et al., 2011). 9 Figure 0.1 Chemical structure of xanthan This extracellular heteropolysaccharide was developed in the late 1950s and its first industrial manufacturing was carried out in 1960. The Xanthan backbone is mostly composed of a linear a β-(1-4)-D-glucose chain like the cellulose chain. According to the bacterial strain's generation, chemical modification, and fermentation conditions, pyruvate, and acetate groups are connected to two units of D-mannose in non-stoichiometric quantities (Abbaszadeh et al., 2015). XG is non-sensitizing, non-toxic, and causes no eye or skin irritation, later in 1969, it was also authorized by the US Food and Drug Administration (FDA) (Kennedy and Bradshaw, 1984). 2.3.2. Physical and chemical properties of xanthan Xanthan is odorless and has a cream-colored appearance (Fiume et al., 2016) and has various unique characteristics, including non-toxicity, intrinsic ability, immunological agent, thermal stability, biocompatibility, and stability in acidic and alkaline circumstances (Kumar et al., 2018). Moreover, XG is likewise fairly soluble in hot and cold water; nevertheless, its aqueous solution, 10 like that of other hydrocolloids, requires vigorous agitation to prevent lump formation (Katzbauer and Stability, 1998). In addition to that, XG solutions are extremely pseudo-plastic in nature and exhibit behavior like non-Newtonian fluids. Its pseudo-plasticity enhances the sensory aspects of food items and ensures improved pourability, mixability, and pumpability functionality. The distribution of molecular weight of XG varies from 2 × 106 to 20 × 106 Da and it has also a high viscosity yield as well as a low susceptibility to salinity variations (Rosalam and England, 2006). The apparent viscosity of conformal polymeric chains of xanthan can be significantly altered by applying varied shear stresses. In terms of chemical properties, XG is a type of water-soluble polysaccharide with a unique helical structure that protects its structure from depolymerization. As a result, it has a more stable thermal behavior compared to other polysaccharides. XG solutions are also less affected by sterilization and can be used over a broad range of pH values (Kumar et al., 2018; Stokke & Christensen, 1996). Because of these properties, XG is a great stabilizer for creating suspensions and emulsions. The associated chains of XG form a three-dimensional network, that helps to create a stable product (Krstonošić et al., 2009; Badwaik et al., 2013). By incorporating O-acetyl and pyruvyl residues in the side chains and including two disaccharide units in the major chain, XG chains can form physical linkages with bivalent cations, resulting in intramolecular crosslinking and chain contraction. Furthermore, the presence of pyruvyl and acetyl also influences the XG aqueous solution behavior. In particular, a lesser pyruvyl level results in lower viscosity, whereas greater pyruvate content improves its gel-like behavior through better macromolecular interaction and a higher content of acetyl limits XG aqueous solution gelation (Bergmann et al., 2008; Dário et al., 2011). 11 2.3.3. Industrial applications of xanthan Xanthan XG is a highly versatile and commonly used ingredient in various industries. It has become a valuable component in a variety of products, such as beverages, food, medicines, personal care items, and industrial applications. Its outstanding ability to thicken, stabilize, and provide distinct rheological properties has transformed many industries, making it an essential ingredient for enhancing the quality, texture, and overall performance of countless consumer and industrial products. XG's main applications are in the food industry. Many of today's meals need the distinct texture, viscosity, style, flavoring release, and water-control qualities of polymers. All these qualities are improved by XG, which also regulates the fluidity of the final food item. In comparison to gums with more Newtonian characteristics, it has a lesser "gummy" mouthfeel. For better water binding during baking and storage and to lengthen the shelf life of baked goods and chilled doughs, XG is used in the production of bakery goods (Sharma et al., 2006). In drinks and squashes, xanthan is used as a bodying agent. Mixtures containing XG, galactomannans, carrageenan, guar, and locust bean gum are also good stabilizers for sherbet, ice cream, ice milk, and milkshakes (Palaniraj and Jayaraman, 2011). In the chemical sector, xanthan has several uses. Deodorant gels can include a combination of locust bean gum and xanthan. In the presence of borate, a unique xanthan gel can develop. This blend has been used to make explosives. To achieve the correct consistency of the toothpaste, the capacity of xanthan to provide the required minimal viscosity and dispersibility at rest during application is utilized (Rosalam and England, 2006). Xanthan's unique rheological qualities make it technologically appropriate for 'enhanced oil recovery' (EOR) applications. In the oil industry, XG is used for pipeline cleaning, fracturing, 12 and oil drilling operations (Katzbauer and Stability, 1998). Because of its favorable affinity with salt and resilience to heat deterioration, XG is beneficial as a component in drilling fluids. Xanthan has been used in agriculture to increase the flowability of herbicides, insecticides, and fungicides, mixtures by evenly suspending the solid element (Flickinger and Drew, 1999). Furthermore, perfect textures in ceiling-tile finishing and high-solids composition paints are made by the pseudoplastic properties of XG, which also provide in-can strength, ease of application, and textural finish longevity. In the pharmaceutical sector, complexed dextromethorphan, which is used to cure coughs, barium sulfate (application in X-ray diagnosis), and thiabendazole are just a few examples of the insoluble compounds that can be stabilized by XG (Palaniraj and Jayaraman, 2011). 2.3.4. Conventional bioproduction of xanthan Generally, Xanthomonas bacteria strains such as Xanthomonas campestris, Xanthomonas pelargonii, etc., generate XG via aerobic fermentation (Habibi and Khosravi-Darani, 2017; Niknezhad et al., 2016). Sucrose, molasses from sugarcane, and whey are examples of effective sources of carbohydrates used in its production (Silva et al., 2009). Carbon sources, specifically glucose and sucrose have been extensively employed in the commercial synthesis of XG (Krishna Leela and Sharma, 2000). For its conventional production, initially, preservation and preparation of inoculum for the high-capacity bioreactors are governed. The selected microbial strain for xanthan is retained for potential long-term preservation utilizing proven techniques that preserve the desired characteristics. To make the inoculum for large bioreactors, a small fraction of the stored culture is multiplied by growing on solid or liquid media, and then charged into the bioreactor. 13 Various parameters come into play for effective xanthan production as polysaccharide buildup begins during the first development phase and continues after growth. Many researchers have done different parameters study for optimal xanthan production which is presented in Table 2.1. For effective conversion of the carbon supply to desirable polysaccharide synthesis, a high carbon-to-nitrogen ratio is necessary. Carbon concentration enhances the XG production, while the nitrogen source has a negative impact. Although increased nitrogen source concentration leads to a rise in biomass concentration, an excessive concentration of nitrogen is not conducive, as it does not actively participate in the formation of the polysaccharide structure (Habibi and KhosraviDarani, 2017). The nitrogen source supports the growth of cells and facilitates the production of enzymes within bacterial cellular pathways (Farhadi et al., 2012). The amount of phosphorus is also kept low as its effect is the same as nitrogen, negatively influencing xanthan production. The culture pH is also a crucial factor in determining the overall fermentation productivity during exopolysaccharide production. In xanthan fermentation, the medium's pH declines as a result of the generation of acidic metabolites and XG, which includes acidic functional groups. When the pH dips below 5.0, the generation of xanthan is reduced. Thus, to maintain the microbial fermentation medium's optimal pH of 7.0, a buffer or base addition is necessary (Gumus et al., 2010; Kerdsup et al., 2011). Both the microbial strain and the experimental conditions, which include medium composition and environmental factors, play a role in influencing pH changes during the fermentation of X. campestris (Liakopoulou‐Kyriakides et al., 1997). Moreover, it is reported that the highest xanthan production occurs in the pH range between 7.0-7.5 and decreases further with an increase in pH (Krishna Leela and Sharma, 2000). 14 Table 0.1 Optimal values of parameters for xanthan production Parameters Optimum values Microbial Strain Reference pH, temperature, pH- 7 and agitation Temperature- 28 °C Agitation rate- 200 rpm Temperature, pH, Temperature range- 25 °C~ 30°C and carbon source pH- 6.0~8.0 Glucose concentration- 30 g/kg~ 40 g/kg Temperature, pH, Temperature range- 25 °C~ 30°C and carbon source pH- 7.0~7.5 Glucose concentration- 2~10% Temperature, pH, Temperature range- 28 °C~ 30°C and pH- 6.0~8.0 agitation Agitation rate- 387.4–500 rpm Agitation rate, Agitation rate- 600 rpm temperature, and Temperature of 30°C cultivation time Cultivation time- 72 h Xanthomonas campestris (Mudoi et al., 2013) Xanthomonas campestris (Lopes et al., 2015) Xanthomonas campestris GK6 (Krishna Leela & Sharma, 2000) Xanthomonas campestris (Rashidi 2023) et al., Xanthomonas (Psomas campestris ATCC 2007) 33913 et al., Temperature and Temperature range- 25~ 28 °C carbon source pH- 6.0~8.0 Glucose concentration- 40 g/L Agitation rate Agitation rate- 100–600 rpm (Higher production rates, but no significant effect on xanthan molecular weight) Agitation rate Agitation rate- 394.8 rpm Xanthomonas campestris Temperature, and Temperature- 32 °C agitation rate Agitation rate- 500 rpm Temperature, and Temperature- 30 °C agitation rate Agitation rate- 500 rpm Temperature Temperature- 24~27 °C pH, temperature and pH- 7~ 8 Temperature- 25~30 °C Temperature Temperature- 30 °C 15 (Habibi & Khosravi-Darani, 2017) Xanthomonas (Papagianni et al., campestris ATCC 2001) 1395 Xanthomonas (Moshaf et al., campestris 2014) PTCC1473 Xanthomonas (Gilani et al., 2011) campestris PTCC 1473 Xanthomonas (Zakeri et al., 2017) campestris IBRCM 10644 Xanthomonas (Shu & Yang, 1990) campestris NRRL B-1459 Xanthomonas (Esgalhado et al., campestris NRRL 1995) B-1459 Xanthomonas (Shu & Yang, 1991) campestris Effective agitation is needed to disperse the injected air through the medium. The rate at which nutrients are transported across the cell membrane and the rate of microorganism growth are accelerated by the medium's agitation. An observed outcome demonstrated a rise in pyruvate content as stirrer speeds increased. However, the molecular mass of xanthan exhibited no significant changes within the agitation range of 100 to 600 rpm (Papagianni et al., 2001). Another research investigation indicated that an optimal agitation rate of 394.8 rpm yielded the best results for xanthan production (Moshaf et al., 2014). On the other hand, several studies highlighted that the maximum xanthan yield was achieved with an agitation rate of 500 rpm (Gilani et al., 2011; Zakeri et al., 2017). Increasing the agitation rate to as high as 500 rpm also enhances oxygen mass transfer and safeguards against a drop-in aeration rate induced by the viscosity rise during fermentation. The impact of temperature on XG proves to be quite intriguing. Increasing the temperature results in a notable decrease in biomass production and the average molecular mass. Therefore, it is imperative to utilize a low temperature, around 25°C, to produce xanthan with a high average molecular mass (Shu and Yang, 1990). Moreover, as per many findings, cells exhibit superior growth at temperatures lower than 30°C, suggesting a positive relationship between enhanced mass production and increased XG yield (Esgalhado et al., 1995; Shu and Yang, 1991). After the fermentation process, the broth contains bacterial cells, xanthan, and a range of other substances. XG is often recovered after removing the cells, typically through centrifugation (e.g., 14,000 rpm for 10 min). The obtained supernatant undergoes additional XG recovery through solvent precipitation (Krishna Leela and Sharma, 2000) such as using isopropanol. Subsequently, the resulting mixture goes through centrifugation (e.g., 30 min, 4°C, 10,000 rpm). In the final 16 stage, the precipitated XG can be collected and subjected to a 48-h freeze-drying process before being weighed (Nejadmansouri et al., 2020). Since variation is a recognized characteristic of Xanthomonas species, effective management of X. campestris stock culture is essential for stability in XG production. In addition, the design of the bioreactor, mode of operation, medium components, agitation, substrate composition, and oxygen content also affect the synthesis of XG and the development of microorganisms (Borges et al., 2008; Nasr et al., 2007; Papagianni et al., 2001). However, the AGS process differs from conventional xanthan production as it contains diverse microbial species. Maintaining the critical conditions for convectional xanthan production, such as carbon and nitrogen concentration in the feed, pH, and temperature, would help grow Xanthomonas bacterial species in the granules. Therefore, further research on the application of the optimized conventional xanthan production parameters in AGS is needed in order to discover the best approach for the optimized recovery of xanthan from AGS systems. 2.4. Potential for xanthan recovery from AGS systems Conventional xanthan manufacturing relies mostly on commercialized fermentation procedures, which could be resource-intensive and less economical. AGS appears as a viable resource for xanthan recovery in this context. The content of biopolymers such as EPS, ALE, and others, as well as their recovery, have pointed in the direction of the feasibility of recovering this polysaccharide from the wastewater treatment process. In AGS systems, xanthan has been observed with other biopolymers (cellulose and curdlan) (Ferreira et al., 2021; Zhang et al., 2019). Wastewater generated by the confectionery industry is rich in readily biodegradable organic substances, making it an important substrate for xanthan production with high chemical oxygen demand and biochemical oxygen demand characteristics. It was reported that xanthan yields of 17 20.92 g/L and 30.64 g/L were obtained from blended wastewater originating from different phases of white and rose wine manufacturing processes employing Xanthomonas campestris species with 50 g/L of initial sugar concentration in the feed (Rončević et al., 2017). Researchers have also studied the production of xanthan, using Xanthomonas campestris in aerobic conditions, from confectionary wastewater. It was determined that the range of xanthan production was 4.28 g/L to 10.03 g/L. Moreover, brewery wastewater yielded the highest xanthan production of 15.56 g/L, achieved by enhancing the cultivation media with 1.5 % maltose and adding yeast extract and (NH4)2SO4 in a 2:1 ratio to reach a total nitrogen content of 0.02 % (Dodić et al., 2012). Thus, maintaining the substrate concentrations in different industrial wastewater streams has proven to be good for xanthan production. Wineries generate substantial wastewater due to multiple cleaning processes involved in various stages of wine production, including washing after crushing and pressing grapes, as well as rinsing fermentation tanks, barrels, and other equipment or surfaces (Rončević et al., 2015). The investigation revealed that the use of clarification wastewater as a cultivation medium resulted in the highest yield (10.67 g/L) of quality xanthan. Conversely, fermentation tank-washing wastewater from the winery industry produced xanthan of superior quality, albeit with a slightly lower yield (7.488 g/L). Beyond the aspect of xanthan production, the conversion of vital nutrients plays a crucial role. The values obtained indicate that sugar conversion is within the range of 62.74 % to 68.74 %, nitrogen conversion ranges from 44.12 % to 53.99 %, and phosphorus conversion ranges from 52.68 % to 67.98 % (Bajić et al., 2015). The recovery methods used for xanthan production from winery industries are the same as conventional xanthan manufacturing including pasteurization of cultivation broths to inactivate micro-organisms, ultracentrifugation, precipitation with ethanol, drying at 60°C and lastly 18 weighting the amount obtained. By utilizing winery wastewater as a substrate in AGS systems, the detrimental environmental consequences of winery wastewater, including the pollution of ground and surface water, soil deterioration, harm to vegetation, and the emergence of odors, could be minimized. Furthermore, it enables the recycling of waste generated by the wine industry (Chapman et al., 2001). EPS is crucial for sustaining various microbial communities within the AGS system which also demonstrates the potential of biopolymer recovery from AGS. As EPS is the main source of biopolymers such as ALE and PHA, parameters such as substrate type, carbon-to-nitrogen ratio, feeding strategy, OLR, hydrodynamic shear force, hydraulic retention time, solids retention time, and feast/famine period influence EPS production and the stability of granules which could also affect xanthan biosynthesis in aerobic granules (Chen et al., 2022; Sun et al., 2023; Xu et al., 2022). These parameters can be used to control the proliferation of various microbial populations, including xanthan producers once the mechanisms and impacts of operation conditions are revealed. Thus, gaining a deeper insight into the EPS matrix will result in the development of more effective approaches for biopolymer recovery from AGS. Moreover, operational parameters influence the microbial growth environment within the AGS to produce various resources. The organic loading rate (OLR) has been shown to affect the granulation rate, EPS generation, and microbial community structure of AGS because of the variety of microbial metabolisms (X. Chen, J. Wang, Q. Wang, Z. Li, et al., 2022). Moreover, the formation of granules is a lengthier process at low and moderate OLRs, ensuring the development of stable granules. On the other hand, high OLRs accelerate granulation but come at the cost of compromising the stability of aerobic granules (Iorhemen & Liu, 2021). Furthermore, it is stated that, when utilizing a sucrose substrate, aerobic granules with a compact, rounded structure were 19 formed at an OLR of 6 kg COD/m3*d (Zheng et al., 2006). Thus, to maintain the granule's stability as well as the species diversity, OLR plays an important role. The stability of AGS over the long term is influenced by feeding strategies. Moreover, a feeding technique like anaerobic slow feeding makes it possible to use slow-growing bacteria, which helps to maintain steady AGS systems (Iorhemen et al., 2020). It is reported that when subjected to anaerobic mixing conditions during feeding, the granules exhibited a more stable growth, leading to an optimal equilibrium between heterotrophic and autotrophic microorganism growth, despite lower biomass concentrations being attained (Rocktäschel et al., 2013). Furthermore, among the various approaches, anaerobic stirring following fast feeding and anaerobic plug-flow slow feeding demonstrated superior settling performance and structural stability for AGS, accompanied by a higher PS content compared to the direct aeration after the fast feeding strategy (Sun et al., 2023). Therefore, it is indicated that the contents and features of EPS were prone to change when subjected to various feeding strategies which will be helpful for biopolymers recovery from AGS. The amount of COD/N affects the nutrients removal efficiency, biomass yield as well as EPS synthesis in AGS. With the deficiency of nitrogen composition in the feed, the content of PN and PS also fluctuated (Xu et al., 2022). Moreover, In the treatment of high-strength organic wastewater using aerobic granular sludge, the optimal COD: N:P ratio was determined as 100:2.8:0.4, resulting in the highest removal efficiency. This ratio facilitated 98.8% organic removal, 100% total nitrogen removal, and 99.3% phosphorus removal (Hamza et al., 2019). On the other hand, the treatment of brewery wastewater with AGS at different organic loading rates reveals that lower COD/N ratios are conducive to the development of stable aerobic granules and exert an influence on the protein-to-polysaccharides ratio within EPS (di Biase et al., 2020). While 20 there is a contradiction between the studies, more research on the influence of nutrient concentration on EPS formation is required. Additionally, it may be worth exploring QS and QQ approaches for enhanced xanthan production in the aerobic granule matrix. Since QS has a known effect on EPS production in AGS bioreactors (Ren et al., 2010), it is clear if this could also impact xanthan biosynthesis in the aerobic granule matrix. Similarly, QQ serves as an effective regulatory mechanism for community QS signaling. The QQ mechanism could also show an impact on xanthan biosynthesis. However, due to the complex microbial communities in AGS systems, the impact of both QS and QQ mechanisms on wastewater treatment and xanthan biosynthesis requires further exploration. Further research is also needed on the development of xanthan extraction protocols from aerobic granules as well as the purification protocols to ensure consistent and high-quality xanthan output. Addressing these issues is crucial to achieving the maximum potential of xanthan recovery from AGS. 21 Chapter 3 Materials and Methods 3.1. Experimental set-up The experiments were performed in 5 L AGS bioreactors with a working volume of 4.5 L. The sequencing batch reactor (SBR) mode was used to run the bioreactors. Table 3.1 shows the dimensions of the AGS bioreactor. Peristaltic pumps (BT100-2J, Longer Pump, China) were used for feed inlet and effluent withdrawal from the bioreactors. At the base of the bioreactors, Pentair ceramic air diffusers (AS4, Pentair, Sanford, USA) were positioned to create air bubbles at a superficial up-flow velocity of 2 cm/s. Impact-resistant panel-mount air flowmeters (5079K27, McMaster-Carr, Cleveland, USA) were used to measure the air velocity. With a volumetric exchange ratio (VER) of 44%, wastewater was removed via port 3 (Figure 3.2) located at the reactor's mid-height. The experimental setup is schematically depicted in Figure 3.1 below. During granule formation, the bioreactors were run in 6-h cycles containing five phases: 60 min of feeding, 2 h and 20 min of aeration, 30 min of settling (shortened to 20 min, 15 min, 10 min, and eventually 5 min as the granules developed), 8 min for decanting, and 2 min of idle time. The bioreactors were operated at room temperature (21±2 °C). Table 0.1 Dimensions of the AGS bioreactor Measurement Values Height of the bioreactor 880 mm Inner diameter 90 mm Outer diameter 100 mm Height/ Diameter (H/D) ratio 8.8 22 Effluent Aerobic granules Air Feed Figure 0.1 Schematic representation of the AGS bioreactor Figure 0.2 An experimental setup of AGS bioreactor in a laboratory 23 3.2. Feed wastewater All the experiments were conducted using synthetic wastewater. To produce the synthetic wastewater, sodium acetate and sodium propionate were used as carbon sources while ammonium chloride (NH4Cl) was used as a source of nitrogen. Phosphorus was supplied via monobasic potassium phosphate (KH2PO4) and dibasic potassium phosphate (K2HPO4). The composition of the wastewater at 1000 mg/L COD concentration was as follows: 0.9375 g/L of sodium acetate, 0.2085 g/L of sodium propionate, 0.191 g/L of NH4Cl, 0.033 g/L and 0.025 g/L of K2HPO4, 0.015 g/L of CaCl2·2H2O, 0.0125 mg/L of MgSO4·7H2O, 0.01 mg/L of FeSO4·7H2O, and 1 mL/L of micronutrients stock solution. A stock solution of micronutrients was used which includes: MnSO4.H2O(NH4)6, 0.05; Mo7O24.4H2O, 0.05; AlCl3, 0.05; CoCl2.6H2O, 0.05; NiCl2, 0.05; ZnCl2, 0.05; CuCl2, 0.03; and H3BO3, 0.05 in 1g/L formulation (Tay et al., 2002b). 3.3. Seed sludge Return activated sludge (RAS) from a WWTP in Western Canada was used to seed the AGS bioreactors for granule formation. The sludge had the following characteristics: 30-minute sludge volume index (SVI30) of 274 mL/g, 5-minute sludge volume index (SVI5 of 393mL/g, and mixed liquor suspended solids (MLSS) concentration was 2450 mg/L. Prior to starting the system, the RAS was acclimated in a batch mode for 10 d. After formation and maturation, the mature granules were used to start experiment design runs. 3.4. Experimental design Taguchi fractional factorial experimental design – Taguchi orthogonal array (L9) –was employed for the optimization studies. Both the Taguchi fractional factorial experimental design and Pearson correlation analysis were performed in Minitab 21 software. This determined the effect of specific parameters (independent variables) that have been identified to be significant in 24 the production of EPS (and, therefore, to affect xanthan biosynthesis) on xanthan biosynthesis in the aerobic granule matrix. These parameters were COD/N ratio, OLR, and feeding strategy. The responses or dependent variables were xanthan concentration (mass of xanthan in biomass, measured in mg/g), and wastewater treatment performance. Three levels of each factor: COD/N ratio (10, 20, and 30), OLR (0.8, 1.5, and 2.1 kg COD/m³∙d), and feeding strategy (60 min of feeding, 30 min of feeding followed by 30 min stationary phase, and 10 min of pulse feeding followed by 52 min of stationary phase) condition were used. The adopted solids retention time (SRT) was 10 d for each experimental run. All the experimental runs were used to determine the signal-to-noise (S/N) ratio to obtain optimum conditions for the given factors. The “main effect plot for S/N ratio” was used to obtain optimal conditions for xanthan production in AGS systems. The experimental runs and their corresponding operational conditions (COD/N ratio, OLR, and feeding strategy) performed in the current study are summarized in Table 3.2. Table 0.2 Experimental run combinations using Taguchi Design Experimental COD/N Ratio OLR (kg COD/m³∙d) Feeding strategy Run 1 20 2.1 60 min feeding 2 30 1.5 60 min feeding 3 10 2.1 4 30 2.1 5 10 1.5 6 20 1.5 7 10 0.8 30 min feeding + 30 min stationary phase 10 min feeding + 50 min stationary phase 10 min feeding + 50 min stationary phase 30 min feeding + 30 min stationary phase 60 min feeding 8 20 0.8 9 30 0.8 10 min feeding + 50 min stationary phase 30 min feeding + 30 min stationary phase 25 Some indications of granulation are when the SVI5 approaches SVI30 (De Kreuk et al., 2005; Y. Nancharaiah & Reddy, 2018) and mature granules have SVI30 between 30 and 80 mL/g (Derlon et al., 2016). After granule formation and maturation, the cycle time was changed to 4 h comprising 5 phases: 60 min feeding phase (adjusted to 10 or 20 min depending on the experimental run), 2 h 40 min aeration, 10 min settling time, 8 min decanting, and 2 min idle time. Each experimental run had a duration of 30 d, corresponding to 3 x SRT (10 d). Xanthan was extracted on days 10, 20, and 30 of the experiments. On each day that xanthan was extracted, biomass analyses were also conducted to evaluate the yield of xanthan extracted per gram of biomass. The COD/N ratio was adjusted by changing the feed wastewater composition. The OLR was also modified by altering the feed COD concentration. The feeding strategy was adjusted by changing the timers and feed rate of the connected feed pump. Table 3.3 presents the expected values of COD, ammonia, and phosphorus for the feed for each run. For the optimization of xanthan biosynthesis, each of the nine experimental runs was analyzed based on xanthan production and the treatment performance. Table 0.3 Expected values of COD, ammonia, and phosphorus for each run Experimental run COD (mg/L) NH3-N (mg/L) P (mg/L) R1 (2.1; 20; 60 min feeding) 800 40.0 8.0 R2 (1.5; 30; 60 min feeding) 550 18.3 5.5 R3 (2.1; 10; 30 min feeding + 30 min stationary phase) R4 (2.1; 30; 10 min pulse feeding + 50 min stationary phase) R5 (1.5; 10; 10 min pulse feeding + 50 min stationary phase) R6 (1.5; 20; 30 min feeding + 30 min stationary phase) R7 (0.8; 10; 60 min stationary feeding) 800 80.0 8.0 800 26.6 8.0 550 55.0 5.5 550 27.5 5.5 300 30.0 3.0 (OLR; COD/N ratio; Feeding strategy) 26 R8 (0.8; 20; 10 min pulse feeding + 50 min stationary phase) R9 (0.8; 30; 30 min feeding + 30 min stationary phase) 300 15.0 3.0 300 10.0 3.0 *OLR: Organic loading rate (2.1, 1.5 and 0.8 kg COD/m³∙d); COD/N ratio: carbon-to-nitrogen ratio (10,20, 30). 3.5. Analytical methods Wastewater analyses were performed twice a week in terms of COD, ammonia nitrogen (NH3-N), nitrite-nitrogen (NO2-N), nitrate-nitrogen (NO3-N), and phosphorus to determine the treatment performance. The biomass analyses were performed once every week. The procedures are described as follows: 3.5.1. Biomass The bioreactor mixed liquor suspended solids (MLSS) concentration was determined using Standard Method 2540 D (Lipps et al., 2023). A 10 mL well-mixed composite sample from multiple ports on the bioreactor, representing the mixed liquor, was filtered through a pre-weighed 0.45-micron glass-fiber standard filter. The wet biomass retained on the filter was then dried in the oven () at 105o C for 24 h. After drying, the final biomass residue on the filter paper is weighed. After 24 h, the crucible was allowed to cool at room temperature in a desiccator and weighed. The MLSS concentration was calculated in mg/L using equation 3.1 MLSS (mg/L) = Filter Dry Weight (g)–Filter Tare Weight (g) x 106 Sample Volume (mL) (3.1) Standard Method 2540 E was used to determine the bioreactor mixed liquor volatile suspended solids (MLVSS) (Lipps et al., 2023). The dried sample from the MLSS step was subsequently ignited in a muffle furnace at 550°C for 40 min to burn off the organic portion (volatile solids). Following the burning, the residue left on the filter paper was allowed to cool to 27 room temperature before its weight was determined. The MLVSS concentration was calculated in mg/L using equation 3.2. MLVSS (mg/L) = Filter Dry Weight (g) – Filter Ash Weight (g) x106 Sample Volume (mL) (3.2) The sludge volume index (SVI) was determined using Method 2710D (Lipps et al., 2023). SVI refers to the amount of sludge (milliliters) that has settled, and the SVI subscript refers to the settling time (min). Both SVI5 and SVI30 were determined to evaluate the sludge settling efficiency over different time intervals of 5 min and 30 min, respectively. 1 L of the sludge biomass during the aeration phase was withdrawn in a measuring cylinder, and the settled volume (mL) of the biomass was measured after 5- and 30-min, respectively. The SVI values were calculated in mL/g of biomass using equation 3.3. SVI (mL/g) = Settled sludge volume (mL/L) x 1000 mg/g Suspended solids concentration (mg/L) (3.3) 3.5.2. COD The organic matter content of the wastewater was determined by the COD test. Standard Method 5220 D was used to determine the COD concentration – both high range (100-1500 mg/L) and low range (90 mg/L or less) concentrations (Lipps et al., 1926; Lipps et al., 2023). 2 mL of wastewater sample was added to the prepared COD digestion solution vial and mixed thoroughly to give a homogenized mixture. A Hach digester (DRB200, Hach, Canada) was pre-heated to a temperature of 150oC. The COD mixture was placed in the pre-heated digester and heated at 150oC for 120 min. The Cr2O72- in the digestion solution oxidizes the COD in the sample, resulting in chromium changes from the hexavalent to the trivalent state. This chemical reaction results in a color change that is measured by a Hach spectrophotometer (DR3900, Hach, Canada) at 420 nm wavelength for low range concentration and 600 nm wavelength for high range concentration. The 28 COD concentration was calculated following the calibration standard curve which was derived from a known COD concentration. Prior to the determination of the absorbance, the COD vails were allowed to cool to 120oC in the digester and thereafter, placed in a rack to cool to room temperature for result consistency. A calibration curve that correlates absorbance with known COD concentrations was compared to the absorbance measured by the spectrophotometer. This curve calculates the sample's COD value (in mg/L). 3.5.3. Nitrogen Ammonia-nitrogen (NH₃-N) concentration in both influent and effluent samples was determined using the Hach Ammonia Nessler Method 8038. The Hach Ammonia Nessler test kit comprises the Nessler reagent, mineral stabilizer, and polyvinyl alcohol dispersing agent. This method measures ammonia concentrations between 0.02 and 2.50 mg/L NH₃-N. For the analysis, a 25 mL sample was prepared by diluting 0.5 mL of the filtered (filtered through 0.45 μm filter) sample with 24.5 mL of deionized water to achieve a dilution factor of 50. 3 drops of mineral stabilizer were added to the sample vials to stabilize the samples for result accuracy. The solution was mixed several times. Then, 3 drops of polyvinyl alcohol dispersing agent were added to the mixed sample vial to prevent precipitation of interfering substances and mixed several times. Using a pipette, 1 mL of Nessler reagent was added to the sample mixture to enable a reaction with the ammonia present in the sample. The solution was mixed several times and allowed to react for 1 min. After 1 min, 10 mL of the reacted sample mixture was transferred into a sample cell to determine the NH3-N concentration at 380 using a spectrophotometer at the wavelength of 425 nm. Nitrite-nitrogen (NO₂-N) concentrations in the effluent were determined using Hach NitriVer®3 powder pillows for low-range nitrite (0.002 - 0.300 mg/L NO₂-N). A 10 mL sample 29 was prepared by adding 1 mL of the sample to 9 mL of deionized water to achieve a dilution factor of 10. The content of one NitriVer 3 reagent powder pillow was added to the 10 mL vial to activate the reaction of the nitrite present in the solution. The vial content was mixed thoroughly to dissolve the powder pillows completely. The mixture was allowed to stand for 20 min for reaction. This mixture created a pink azo product when the sample's nitrite ions reacted with sulfanilamide and N-(1-naphthyl) ethylenediamine dihydrochloride. After 20 min, the content was poured into a sample cell, and the results were taken in mg/L of NO2-N using a spectrophotometer at the wavelength of 507 nm. Nitrate-nitrogen (NO₃-N) in the effluent was analyzed using the Cadmium Reduction Method (Method 8039), employing a high-range colorimetric test kit with NitraVer® 5 Nitrate Reagent Powder Pillows, designed for concentrations between 0.3 to 30 mg/L NO₃-N. This method reduces nitrate to nitrite by cadmium, forming a diazonium salt by reacting with sulfanilic acid in acidic media. A 10 mL sample was prepared by combining 1 mL of the sample with 9 mL of deionized water. The vial was mixed thoroughly to completely dissolve the content of the powder pillows. The mixture was allowed to stand for 5 min to allow for reaction. If nitrate was present, an amber-colored solution was created when salt and gentisic acid combined. After 5 min, the content was poured into a sample cell, and the results were taken in mg/L of NO3-N using a spectrophotometer at the wavelength of 500 nm. 3.5.4. Phosphorus PO₄³⁻-P concentrations in the influent and effluent were analyzed using the PhosVer®3 reagent (Method 8048), employing powder pillows for reactive phosphorus (orthophosphate) in the range 0.02 to 2.50 mg/L PO₄³⁻-P. To achieve a dilution factor of 10, 0.5 mL of filtered (through a 0.45 μm filter) samples was added to 9 mL of deionized water. The content of one PhosVer®3 30 reagent powder pillow was added to the 10 mL vial to initiate the reaction with phosphate compounds present in the solution. The vial was mixed thoroughly to dissolve the powder pillow contents completely. The mixture was allowed to stand for 2 min for reaction. This method created a phosphate-molybdate complex when orthophosphate and molybdate reacted in an acidic solution. Ascorbic acid then reduced the complex to generate an intense molybdenum blue color. After 2 min, the content was poured into a sample cell, and the concentration of PO43—P in mg/L was determined using a spectrophotometer at the wavelength of 880 nm. 3.5.5. Extraction of EPS The NaCl with heating method was used for EPS extraction. Approximately 10 mL of mixed liquor, collected during the last 10 min of the aeration phase, was centrifuged at 4000 g for 5 min. The supernatant liquor was then filtered through a 0.22 µm polyether sulfone (PES) sterile filter to obtain the soluble EPS (sEPS) or soluble microbial products (SMP). The bottom residue (pellet) was re-suspended in 10 mL of pre-heated 0.05% NaCl solution (pre-heated to 80°C) to ensure that the sludge suspension reached an immediate warm temperature of 50°C. The sludge suspension was immediately sheared using a vortex mixer for 1 minute, followed by centrifugation at 4°C and 4000 g for 10 min. The supernatant was then collected and filtered through a 0.22 µm PES sterile filter to obtain the loosely bound EPS (LB-EPS). The sludge pellet was subsequently re-suspended in pre-heated 0.05% NaCl solution to its original volume of 10 mL and incubated in a water bath at 60°C for 30 min. After incubation, the suspension was centrifuged at 4°C and 4000 g for 15 min. Finally, the supernatant was collected and filtered through a 0.22 µm PES sterile filter to obtain the tightly bound EPS (TB-EPS). 31 3.5.6. Xanthan recovery and quantification To 10 mL of LB-EPS and TB-EPS extracted as described in section 3.5.6, 0.1 mL of saturated KCl solution was added. Then, 30 mL of 96 – 99.5% cold ethanol (3:1 v/v ratio) was added to the solution. The solution was kept at 4 °C for 24 h to allow the xanthan to precipitate (insolubilize) and was subsequently centrifuged at 10,000 g for 30 min at 4 °C (Sampaio et al., 2020). After centrifugation, the supernatant was discarded, and the precipitate was washed 2 times with 30 ml of cold ethanol at 10,000 g for 15 minutes at 4 °C; then, the wet precipitate was collected and dried in a hot air oven at 30-40°C for 24 h. Finally, the dry content from L-EPS and T-EPS was combined, and the amount of recovered xanthan was measured gravimetrically. 3.5.7. Fourier Transform Infrared (FT-IR) spectroscopy Fourier Transform Infrared (FT-IR) spectroscopy was used to acquire FT-IR spectra of both pure xanthan and dry xanthan extract. This method compares the xanthan extract with pure xanthan by analyzing the molecular vibration of both samples, generating a unique spectrum of xanthan. The FT-IR spectra were recorded by Brucker Alpha II spectrometer (Brucker Inc., Switzerland) (Figure 3.3). About 20 mg of the sample was placed on the diamond crystal plate. The compression tip was released onto the sample on the diamond crystal plate to allow the infrared light/radiation in and out of the sample. The compression tip was carefully lowered onto the sample to establish uniform contact with the diamond crystal surface. This step is essential as it facilitates the penetration of infrared radiation into the sample, allowing interaction with its molecular bonds and producing distinctive absorption spectra. The spectral data were acquired in the mid-infrared range (4000–400 cm⁻¹) with a resolution of 4 cm⁻¹, ensuring precise detection of molecular vibrations. A background spectrum was recorded before measuring the sample to maintain accuracy and eliminate potential 32 environmental interferences, such as atmospheric CO₂ and moisture. The obtained FT-IR spectra were analyzed to identify characteristic functional groups by comparing the peak positions and intensities of the extracted xanthan with those of the pure xanthan sample. Figure 0.3 FT-IR spectrometer for identification of extracted xanthan 3.5.8. Nuclear magnetic resonance (NMR) spectroscopy The xanthan extract was compared with pure xanthan (Sigma Aldrich, USA) using Proton NMR (1H NMR) spectroscopy to determine their similarity. This spectroscopy provides molecular-level insights by analyzing protons of organic constituents in a sample. To prepare the 1 H NMR samples, 5–15 mg of pure and extracted xanthan were dissolved in 0.6 mL of deuterium oxide (D₂O-d6). The solutions were transferred into 5 mm NMR tubes and carefully sealed to prevent contamination or evaporation. The 1H NMR spectra were recorded using a Bruker Evo Avance 400 MHz spectrometer equipped with a Smart Probe for enhanced sensitivity. The experiments were conducted at 70°C, and the spectrum was acquired with a spectral width of 10 ppm. 33 The acquired NMR spectra were processed and analyzed using TopSpin 4.4 software. The chemical shifts (δ values) of the ¹H signals for pure and extracted xanthan were assigned and compared to analyze the molecular structure of xanthan. 3.6. Statistical analysis Pearson correlation was performed using the Minitab 21 software to determine the linear relationship between the variables and xanthan production. Pearson correlation is a statistical tool used to measure the strength and direction of the linear relationship between different variables. It is expressed through the correlation coefficient, r, which ranges from -1 to +1. An r = +1 indicates a perfect positive correlation where both variables increase at the same rate. An r = -1 indicates a perfect negative correlation, where one variable increases as the other decreases. An r = 0 implies no linear relationship, meaning changes in one variable do not predict changes in the other. Understanding the correlation between factors affecting xanthan yield helps maximize production in bioreactors. 34 Chapter 4 Results and Discussion 4.1. Biomass characteristics The AGS stability, in terms of biomass concentration and sludge settleability, was monitored throughout the experimental duration (experimental runs 1 – 9). The MLSS, MLVSS, SVI5, and SVI30 are shown in Figures 4.1. The biomass in all the nine experimental runs exhibited stability, showing steady increase in MLSS and MLVSS concentrations in some runs. All the nine experimental runs stabilized in terms of MLSS and MLVSS within the 30 d duration of each run. The MLSS concentration in all the nine runs were in the range 8.5± 2 – 15± 2 g/L indicating high biomass retention, which is a major characteristic of stable AGS systems (Y. Nancharaiah et al., 2019; Wang et al., 2019). The lower MLSS concentration values are for runs with OLR of 0.8 kg COD/m³∙d (COD concentration of 300 mg/L). The biomass concentration in the AGS system in the present study are within the typical range for AGS systems: 6 – 15 g/L (Y. Nancharaiah & Reddy, 2018; Peng et al., 2022; Setianingsih et al., 2024). The sludge settleability as shown in Figures 4.1 and 4.2, indicated by SVI5 and SVI30, was stable. In all the nine experimental runs, the SVI values remained below 40 mL/g, indicating outstanding settleability and compact granule formation. Additionally, the SVI30/SVI5 ratio was consistently in the range 0.9 – 1.0 in all the nine experimental runs. A full granular sludge system is attained when SVI5 is comparable to SVI30 or when SVI30/SVI5 ratio approaches 0.9 systems (Nancharaiah et al., 2019; Wang et al., 2019). This is a strong indicator of successful granule formation. This ratio is commonly used to assess sludge compactness, with values close to 1 signifying well-developed granules. The biomass concentration and settleability data obtained from the present study confirm stable sludge characteristics throughout the experimental period as shown by other studies (Giesen et al., 2013; Y.-Q. Liu et al., 2011; Ni et al., 2009; We et al., 2021). 35 Granule stability was also influenced by operational conditions such as feast-famine cycles and COD concentrations (Corsino et al., 2017; Sguanci et al., 2019). 36 10 5 0 16 20 24 28 32 4 10 2 0 4 8 12 16 / 5 S / 30 S 10 15 8 10 S 6 4 5 2 0 0 12 16 20 24 28 S / 5 S 5 0 0 4 8 12 20 ML SS 4 5 2 0 0 20 24 28 32 Run 14 MLSS & ML SS (g/L) 10 6 24 28 32 S / / 5 S 20 8 15 6 10 4 2 5 0 0 12 16 5 S 30 MLSS ML SS 32 20 24 S 28 32 / 5 S / 30 S 30 25 12 15 10 20 10 8 6 4 5 2 0 0 4 8 12 16 20 24 28 32 Time (d) MLSS ML SS S / / 5 S 30 S 12 35 10 30 25 8 20 6 15 4 10 2 5 0 0 0 4 8 12 16 20 24 28 32 Time (d) Time (d) S 28 14 30 30 10 8 24 16 0 25 4 20 ML SS Run S 12 0 Time (d) ML SS 16 16 Run 10 MLSS (mL/g) 15 8 MLSS 12 S 5 30 S MLSS & ML SS (g/L) 10 16 / 15 0 20 12 12 8 MLSS 20 / 14 8 0 4 30 10 25 4 S 15 32 16 0 / 5 Time (d) ML SS Run S 25 Time (d) MLSS 5 0 MLSS & ML SS (g/L) 8 10 5 Time (d) ML SS Run 20 12 4 15 10 32 20 (mL/g) MLSS & ML SS (g/L) MLSS 25 14 0 28 MLSS & ML SS (g/L) S Run 16 24 20 15 Time (d) ML SS MLSS & ML SS (g/L) MLSS 20 25 0 0 0 Time (d) S (mL/g) 20 6 (mL/g) 12 8 S 8 30 (mL/g) 4 10 S 0 12 / S 15 40 / 20 (mL/g) 20 Run 50 S 25 / S 30 / (mL/g) 14 Run S 35 MLSS & ML SS (g/L) 16 (mL/g) 40 MLSS & ML SS (g/L) / (mL/g) / S MLSS & ML SS (g/L) Run 18 16 14 12 10 8 6 4 2 0 S 5 S 30 MLSS ML SS S 5 S 30 Figure 0.1 Biomass characteristics *(COD/N ratio/OLR/feeding strategy), S: Stationary phase, F: feeding, OLR: organic loading rate, COD: chemical oxygen demand, MLSS: Mixed liquor suspended solids, MLVSS: Mixed liquor volatile suspended solids, SVI5: Sludge volume index (5 min), SVI30: Sludge volume index (30 min). 37 Figure 0.2 Biomass settleability *Run 1 (C/N-20, 60 min feeding, OLR-2.1 kg COD/m³∙d); Run 2 (C/N-30, 60 min feeding, OLR-1.5 kg COD/m³∙d); Run 3 (C/N-10, 30 min feeding/30 min stationary phase, OLR-2.1 kg COD/m³∙d); Run 4 (C/N-30, 10 min pulse feeding/50 min stationary phase, OLR-2.1 kg COD/m³∙d); Run 5 (C/N-10, 10 min pulse feeding/50 min stationary phase, OLR-1.5 kg COD/m³∙d); Run 6 (C/N-20, 30 min feeding/30 min stationary phase, OLR-1.5 kg COD/m³∙d); Run 7 (C/N-10; 60 min feeding, OLR-0.8 kg COD/m³∙d); Run 8 (C/N-20; 10 min pulse feeding/50 min stationary phase, OLR-0.8 kg COD/m³∙d); Run 9 (C/N-30; 30 min feeding/30 min stationary phase, OLR-0.8 kg COD/m³∙d). 38 4.2. Treatment Performance 4.2.1. Organic matter removal Figure 4.3 below presents the COD removal efficiencies in all nine experimental runs. The bioreactors consistently achieved COD removal efficiencies ranging from 91± 3 to 99 ± 1%. This indicates that the AGS bioreactors exhibited high organic matter removal, regardless of variations in operational conditions. The long period of the aeration phase of the SBR cycle (2 h 40 min) allowed for sufficient contact time and interaction between wastewater and the granules, facilitating effective biodegradation of organic carbon, as corroborated by previous studies (Franca et al., 2018; Iorhemen et al., 2022). This stability highlights effective microbial adaptation with consistent removal efficiency, demonstrating efficient wastewater treatment processes. Figure 0.3 COD removal efficiencies for all the experimental runs The high COD degradation achieved in the present study aligns with the treatment performance of AGS reported for both municipal and industrial wastewaters (Chen et al., 2024; 39 Giesen et al., 2013; L. Liu et al., 2011; Mady et al., 2024; Pronk, De Kreuk, et al., 2015; Schwarzenbeck et al., 2005; Su et al., 2012). The high biomass concentration, the dense nature of the AGS, and the good settling properties of the biomass in this study allowed for a high biomass retention, which enhanced the performance of the bioreactors (Świątczak and CydzikKwiatkowska, 2018; Iorhemen et al., 2022). 40 / Run / Run / / S 80 800 80 800 80 600 60 600 60 600 60 400 40 400 40 400 40 200 20 200 20 200 20 0 0 0 0 4 8 12 16 20 24 28 32 0 4 8 12 Time (d) COD feed COD eff. Run / COD feed Removal (%) / 20 24 COD eff. Run S / 28 0 0 32 8 12 16 Time (d) COD feed Removal (%) / 4 S 20 24 COD eff. Run / 28 Removal (%) / S 100 800 80 800 80 800 80 600 60 600 60 600 60 400 40 400 40 400 40 200 20 200 20 200 20 0 0 0 0 8 12 16 20 24 28 32 0 4 8 12 Time (d) COD feed COD eff. Run / 16 20 24 28 Removal (%) COD feed COD eff. Run / / Removal (%) / 80 800 80 600 60 600 60 400 40 400 40 200 20 200 20 0 0 0 0 COD feed 12 16 Time (d) COD eff. 20 24 28 Removal (%) COD (mg/L) 800 Removal (%) 1,000 8 8 12 4 8 COD feed 12 16 Time(d) COD eff. 20 24 28 32 24 28 Run / 32 Removal (%) / S 1,000 100 800 80 600 60 400 40 200 20 0 0 0 Removal (%) Figure 0.4 COD removal profiles for all experimental runs *(COD/N ratio/OLR/feeding strategy), S: Stationary phase, F: feeding, OLR: organic loading rate, COD: chemical oxygen demand 41 20 COD eff. 100 0 16 Time (d) COD feed 100 4 4 S 1,000 0 0 0 Time (d) COD (mg/L) 4 0 Removal (%) 0 COD (mg/L) 1,000 Removal (%) 100 COD (mg/L) 1,000 Removal (%) 100 1,000 COD (mg/L) 16 Time (d) 0 Removal (%) 100 800 COD (mg/L) 1,000 Removal (%) 100 COD (mg/l) 1,000 Removal (%) 100 COD (mg/l) 1,000 0 COD (mg/L) / Removal (%) / 4 COD feed 8 12 16 Time (d) COD eff. 20 24 Removal (%) 28 Removal (%) Run 4.2.2. Nitrogen removal The ammonia removal profile during each run is presented in Figure 4.5 below. Ammonia removal efficiency was highest in runs 2–6 and 8, ranging from 91± 2 – 96 ± 2%, indicating efficient nitrification. These runs maintained stable influent ammonia levels and consistently low effluent values. For instance, run 3 had a high influent concentration (~80 mg/L) but achieved 99 ± 1% removal, showing strong nitrifying activity. Run 6 also demonstrated excellent performance with gradual nitrate increases and low nitrite accumulation, confirming complete nitrification with sufficient oxygen availability. Runs 1, 4, 5, and 7 showed slightly lower but still effective ammonia removal (82 ± 2 –90 ± 2%). n run 1, removal efficiency fluctuated between 80± 2 –90± 3%, while nitrate levels varied and nitrite remained low, indicating limited denitrification. Run 4 appeared more balanced, with stable ammonia, nitrate, and nitrite levels, suggesting an equilibrium between nitrification and denitrification. Runs 5 and 7 showed consistent ammonia removal with nitrate fluctuations likely influenced by oxygen availability, and low nitrite concentrations, pointing to successful oxidation without intermediate build-up. Run 8 also maintained effective ammonia removal, though slightly lower than the preceding runs, likely due to a lower influent ammonia concentration (~14 mg/L). The steady low nitrite levels and increasing nitrate imply limited denitrification. Notably, run 9 had the lowest ammonia removal efficiency (42 ±3 –58± 2%), which can be attributed to the combined effects of the lowest OLR of 0.8 kg COD/m³·d and the highest COD/N ratio of 30, which hindered microbial activity. Overall, literature on AGS systems typically reports ammonia removal efficiencies between 80% and 99% under optimal conditions (D. Chen et al., 2022; Giesen et al., 2013; J. Li et al., 2014; Pronk, De Kreuk, et al., 2015; Zou et al., 2024). Except for run 9, all the other runs achieved 42 ammonia removal ≥ 82%, confirming the occurrence of nitrification. The study indicates that aerobic granulation benefits from higher OLRs (2.5–15 kg COD/m3.d) as they enhance biological activity and treatment efficiency (Thanh et al., 2009). Also, maintaining a proper carbon, nitrogen, and phosphorus balance, particularly a COD: N:P ratio of 100:10:2 with a COD/N of 10, is vital for effective biological (Xu et al., 2022). Figure 0.5 Ammonia removal efficiencies for all experimental runs 43 Figure 0.6 Nitrogen removal profiles for all experimental runs *(COD/N ratio/OLR/feeding strategy), S: Stationary phase, F: feeding, OLR: organic loading rate, COD: chemical oxygen demand, NH3: Ammonia 4.2.3. Phosphorus removal Figure 4.6 below presents the phosphorus removal efficiencies in all the experimental runs. The phosphorus removal efficiency observed across the experiments ranged from 69 ± 7 to 90 ± 7%, apart from run 9, which had a removal efficiency of 54 ± 11%. Runs 3, 5, and 6 exhibited the highest phosphorus removal efficiencies of 87± 7, 90 ± 10, and 81 ± 5%, respectively. In these three runs, there was a stationary phase with no oxygen supply. Runs 3 and 6 had a stationary phase of 30 min, while run 5 had a stationary phase of 50 min, which supports the growth and activity of polyphosphate accumulating organisms (PAOs), a crucial microbial community for 44 phosphorus removal (Nancharaiah & Reddy, 2018; Purba et al., 2020). This indicates that the feeding strategy (10 min pulse feeding followed by 50 min stationary phase, and 30 min feeding with a 30 min stationary phase) contributed to the removal of phosphorus. Research findings on phosphorus removal in AGS systems reveal a variety of removal efficiencies, frequently ranging from 70% to 100% (Chen et al., 2024; da Silva et al., 2023; Marcińczyk et al., 2022; Y. Nancharaiah & Sarvajith, 2023). In addition, the unique structure of the aerobic granule, including aerobic, anaerobic and anoxic layers, plays an important role in phosphorus removal. This granule structure provides both aerobic and anaerobic conditions due to oxygen diffusion limitation, which enables enhanced biological phosphorus removal (Iorhemen et al., 2022). Run 9 which exhibited phosphorus removal efficiencies of 54% had a low OLR and high COD/N ratio of 30, which implied a higher carbon supply for possible excessive growth of glycogen-accumulating organisms (GAOs), which may have competed with PAOs for carbon, thereby contributing to the low phosphorus removal observed (Tsertou et al., 2024). Figure 0.7 Phosphorus removal efficiencies in all experimental runs 45 20 2 0 4 8 12 16 20 24 28 20 0 32 4 8 12 Time (d) P feed P eff. Run / P feed Removal (%) / Run 60 4 40 2 20 0 0 4 8 12 16 P feed Run P (mg/L) 20 24 Time (d) P eff. / 28 0 P feed / 25 100 20 80 60 10 40 5 20 16 Time (d) 20 24 / 28 32 0 0 4 8 12 16 20 24 28 Time (d) P feed P eff. Removal (%) 32 0 0 4 8 12 16 20 24 28 32 P eff. Removal (%) / / S 100 10 100 80 8 80 6 60 4 40 20 2 20 0 0 0 0 4 Removal (%) 8 12 16 / P feed 20 24 P eff. Run S 28 32 / ML SS1 / S 12 100 15 100 10 80 12 80 9 60 8 60 6 40 4 2 0 20 Time (d) P eff. Run 5 Run 2 12 40 S 40 Removal (%) 15 / 60 8 60 10 Time (d) 6 4 15 P feed 8 0 80 Removal (%) 10 32 100 20 32 4 P (mg/L) 0 / 28 12 P (mg/lL) 80 6 Removal (%) 8 24 14 Removal (%) P (mg/L) 100 20 P eff. S 10 16 Time (d) S 0 0 0 / Removal (%) 40 P (mg/lL) 5 4 3 2 1 0 0 60 / 25 0 0 4 8 P feed 12 16 Time (d) P eff. 20 24 28 32 6 40 20 3 20 0 0 0 0 4 Removal (%) 8 P feed 12 16 Time (d) P eff. Figure 0.8 Phosphorus removal profiles for all experimental runs *(COD/N ratio/ OLR/feeding strategy), S: Stationary phase, F: feeding, OLR: organic loading rate, COD: chemical oxygen demand, P: Phosphorus 46 Removal (%) 4 7 6 P (mg/L) 40 80 P (mg/L) 60 6 Run 100 Removal (%) 8 / Removal (%) 80 / 10 9 8 Removal (%) 10 P (mg/L) 100 20 24 28 Removal (%) 32 Removal (%) Run / Removal (%) / P (mg/L) Run 12 4.3. Xanthan recovery 4.3.1 Xanthan yield and impact of the operating factors The xanthan yield for each experimental run is shown in Figure 4.8 (a). The xanthan yield for each experimental run at a steady state is shown in Figure 4.8 (b). Figure 0.9: Xanthan yield a) on different days of 10, 20, and 30, b) average yield for the nine bioreactors at steady state OLR plays a role in shaping the microbial community structure, granulation rate, EPS production, and morphology of AGS, driven by diverse microbial metabolic activities (Feng et al., 2021; A.-j. Li et al., 2008). The results from Figure 4.8 (a) indicated that higher OLR values generally led to increased xanthan yield. For instance, on days 20 and 30 of the experiments, run 3 (C/N-10, 30 min feeding/30 min stationary phase, OLR-2.1 kg COD/m³∙d) with the highest OLR produced xanthan yields ranging from 41 ± 8 to 41 ± 7 mg/g biomass. In contrast, runs 1 (C/N-20, 60 min feeding, OLR-2.1 kg COD/m³∙d) also had consistent and favorable conditions for xanthan 47 yield values (29~35 mg/g biomass) throughout the 10, 20, and 30 days. Also, moderate OLR yielded a higher production level (48 ± 8 mg/g biomass) in run 5 (C/N-10, 10 min pulse feeding/50 min stationary phase, OLR-1.5 kg COD/m³∙d) on day 10, while on day 20 and day 30, the xanthan yield decreased down to 35 ±10 and 28 ±5 mg/g biomass, respectively. The specific days cited (20 and 30) in runs 3 and 5 probably indicate when the microbial communities had successfully adapted to the higher OLR, which enabled optimal conditions for polysaccharide formation. The research highlights that with an increase in OLR (1.2) led to a gradual rise in extractable polysaccharide (ALE) levels in algal-bacterial AGS, peaking at day 60 (experiment days: 49~70) (X. Chen, J. Wang, Q. Wang, Z. Li, et al., 2022). This pattern suggests that time is necessary for microbial activity to reach full efficiency in producing polysaccharides after OLR increases. In contrast, a decline in OLR in run 5 showed the opposite trend, supporting these findings. The lowest OLR during the experiment (0.8 kg COD/m3⋅d) resulted in the low xanthan yield in runs 7 (C/N-10, 60 min feeding, OLR-0.8 kg COD/m³∙d), 8 (C/N-20, 10 min feeding + 50 min stationary phase, OLR-0.8 kg COD/m³∙d) and 9 (C/N-30, 30 min feeding + 30 min stationary phase, OLR-0.8 kg COD/m³∙d). These runs achieved a yield of 15 ± 4, 11 ± 6, and 8 ± 3 mg xanthan/g biomass for runs 7, 8, and 9, respectively. While there is no previous study on xanthan recovery from AGS systems, findings from the current research align with the literature where a sudden rise in OLR to 0.9 kg COD/m3⋅d for both bacterial and algal-bacterial AGS increased EPS secretion which in turn affect xanthan biosynthesis (Z. Liu et al., 2023). Previous studies have also shown that higher OLR enhances substrate availability, promoting microbial activity that could influence polysaccharide biosynthesis (Pronk, Abbas, et al., 2015). The results of these studies 48 corroborate with the results, indicating that optimizing OLR is critical for maximizing xanthan yields in AGS systems. The results at steady state condition of Figure 4.8 b) align with Figure 4.8 a), indicating the highest xanthan yield obtained from run 3, run 1 and run 5 at 2.1, 2.1 and 1.5 OLR with average values of 41± 7, 35± 9, 28±5, respectively; however, the lower yields production occurred in run 7, 8, and 9 at 0.8 OLR with the average values of 15 ± 4, 11 ± 6, and 8 ±3, respectively. The xanthan yield at steady state from Figure 4.8 b) further illustrated that the highest xanthan yields were achieved at an OLR of 2.1 kg COD/m³∙d, particularly in a run 3 (C/N-10, 30 min feeding/30 min stationary phase, OLR-2.1 kg COD/m³∙d) with a COD/N ratio of 10. This suggests that the availability of nutrients at higher OLR levels supports microbial growth, which is essential for xanthan biosynthesis. In contrast, lower OLR values runs 7 (C/N-10, 60 min feeding, OLR-0.8 kg COD/m³∙d), 8 (C/N-20, 10 min feeding + 50 min stationary phase, OLR-0.8 kg COD/m³∙d) and 9 (C/N-30, 30 min feeding + 30 min stationary phase, OLR-0.8 kg COD/m³∙d) were associated with nutrient limitations, leading to suboptimal xanthan production. The COD/N ratio also impacted xanthan yield over the experimental period. Figure 4.8 (a) illustrates that the COD/N ratio also impacted xanthan yields over the experimental period. On day 10, run 5 (C/N-10, OLR-1.5 kg COD/m³∙d, 10 min feeding + 50 min stationary phase) produced the highest xanthan yields, peaking at approximately 48± 8 mg/g biomass, whereas the xanthan yield was lower (28~35 mg/g biomass) on days 20 and 30. In contrast, run 3 (C/N-10, 30 min feeding + 30 min stationary phase, OLR-2.1 kg COD/m³∙d) produced the highest xanthan yields (~41± 8 mg/g biomass) on days 20 and 30 rather than day 10, which had a 25± 5 mg/g biomass yield. A prior study on the optimized operational parameters indicated that an influent COD 49 concentration of 600 mg/L with a COD/N ratio of 10 is most conducive to ALE production, which aligns with the findings observed in this study (Chen et al., 2022). Both runs, 5 and 3, had a COD/N ratio 10, indicating the optimum level. However, the OLR changed, which affected the COD concentration in both runs. The initial phase of each run takes time to stabilize, affecting the production of xanthan yield. The study indicated that EPS secretion varies over time, showing a decrease following the initial microbial adaptation phase, possibly due to changes in nutrient levels (Long et al., 2015). Additionally, another investigation demonstrated that biopolymer (PHA) production rose by 2.6 times as COD concentration increased from 800 to 1600 mg/L (Yuan et al., 2024). Elevated COD levels are known to stress microbial cells, triggering the synthesis of exopolymers such as ALE (Yang et al., 2014). This trend is evident in run 3, where higher COD levels than run 5 led to greater xanthan production on days 20 and 30. However, the highest COD/N ratio of 30 resulted in lower xanthan production across runs 2 (C/N-10, 60 min feeding, OLR-0.8 kg COD/m³∙d), 4 (C/N-20, 10 min feeding + 50 min stationary phase, OLR-0.8 kg COD/m³∙d) and 9 (C/N-30, 30 min feeding + 30 min stationary phase, OLR0.8 kg COD/m³∙d) with yields often falling below 22 mg/g biomass, particularly in run 9 (OLR = 0.8), which had the lowest yield of approximately 8± 3mg/g biomass. The data indicate that xanthan production decreases when the COD/N ratio exceeds 20, likely due to nitrogen limitation. This finding is consistent with previous research, which reported that elevated COD/N ratios lead to nitrogen deficiency, overgrowth of filamentous organisms, and impaired carbon diffusion— factors that can compromise the stability of AGS granules and, consequently, reduce xanthan production (Franca et al., 2018; Kocaturk & Erguder, 2016). The xanthan yield at steady state from Figure 4.8 b) further supports these findings, illustrating that xanthan production is maximized at lower COD/N ratios. The highest yield was 50 consistently observed at a COD/N ratio of 10 in run 3, confirming that a balanced nutrient supply is essential for enhancing microbial activity and xanthan synthesis, while a higher COD/N ratio of 30 is not influential for xanthan production in run 4, 9 and 2. Regarding the feeding strategy, Figure 4.8 a) shows that the pulse-feeding and 30-minute feeding strategies are the most effective and yield the highest xanthan production across the experimental runs. Specifically, run 5 (C/N-10, 10 min feeding + 50 min stationary phase, OLR1.5 kg COD/m³∙d) demonstrated peak xanthan yields of approximately 48 ±8 mg/g biomass using a pulse feeding strategy on day 10, and on days 20 and 30, the yield decreased (28~35 mg/g biomass). In contrast, the 30-minute feeding strategy resulted in high xanthan production in run 3 (COD/N = 10, OLR = 2.1 kg COD/m³∙d) on days 20 and 30, achieving around ~41± 8 mg/g biomass, while less xanthan yield (~25 ± 5 mg/g biomass) obtained on day 10. It is reported that the anaerobic plug-flow slow feeding and anaerobic stirring after fast feeding constitute the higher PS content than direct aeration after fast feeding with PN/PS ratios of 1.8, and 2.0, respectively (Sun et al., 2023). The increased PS levels are associated with the dominance of slow-growing microbes like Candidatus_Competibacter, which are known for producing PS with strong gel characteristics. This was also evident in run 3, which showed higher xanthan production on day 30 compared to run 5. Furthermore, longer feeding (40 min) resulted in greater PS content (PN/PS = 4.9) than the faster 20-minute feeding (PN/PS = 5.2). Also, it is well established that EPS is primarily produced during the feast phase and subsequently consumed during the famine phase (da Silva et al., 2021). Additionally, the 60 min feeding strategy showed a moderate trend, with run 1 (C/N-20, 60 min feeding, OLR-2.1 kg COD/m³∙d) achieving around 35 ± 9 mg/g biomass. In contrast, run 2 (C/N-30, 60 min feeding, OLR-1.5 kg COD/m³∙d) yielded only about 15 ± 6 mg/g biomass with 51 the same feeding strategy. Overall, the 30-minute feeding strategy effectively suggests that the intermediate feeding intervals may enhance microbial metabolism and xanthan synthesis, particularly at lower COD/N ratios. The xanthan yield at steady state from Figure 4.8 b) collaborates with the findings and reports that for run 3, 30 min of feeding works best and gives a higher xanthan yield of 41 ± 7 mg/g biomass at OLR (2.1 kg COD/m³∙d) than the other runs. The lower results were obtained from run 7, run 8, and run 9, which indicate 60-minute feeding, 10-minute feeding, and 30-minute feeding with a low OLR (0.8 kg COD/m³∙d), respectively. However, the data indicates the variations in xanthan yield among different feeding strategies; the balanced feeding phase with the highest OLR still promotes optimal xanthan production, as seen from the results of run 3. 4.3.2. Identification of xanthan extract To identify the extract from AGS, a detailed analysis of its physical and chemical properties was conducted using FT-IR and NMR spectroscopies. i. FT-IR spectroscopy FT-IR spectroscopy is a valuable tool for analyzing biopolymers. As shown in Figure 4.9, the FTIR spectra for the xanthan reference and extract exhibit key peaks typical of xanthan gum, such as the broad O-H stretching vibrations around 3283.94 cm⁻¹, indicative of hydroxyl groups. Both also show C=O stretching between 1600-1650 cm⁻¹, pointing to carbonyl groups in xanthan's structure. 52 Figure 0.10 FTIR of xanthan reference (dark color) and xanthan extract It has also been reported that the stretching vibrational peak of the CO group appears between 1059–1113 cm−1 (Pooja et al., 2014; Salah et al., 2010). This peak shows high intensity at 1022.74 cm-1 for the reference sample; however, in the extract, the peak is present with low intensity at 1073.24 cm⁻¹. Additionally, the CH2 scissors vibration was observed between 14001450 cm-1 in both samples (Faria et al., 2011). These similarities confirm that the core chemical structure of xanthan is present in the extracted sample. Notably, a broad and intense peak between 3100–3450 cm⁻¹ corresponds to the stretching vibration of hydrogen-bonded –OH groups (Mohsin et al., 2018). The C=O group was identified at wavelengths between 1600-1700 cm-1 (Pawlicka et al., 2019). The range from 880 to 900 cm−1 contains the peak, which relates directly to the glycosidic linkages in the polysaccharide backbone of xanthan gum at 893.02 cm-1 (Soni & Mahmoud, 2015). This peak appears in both samples, but the intensity varies. 53 However, there are some differences in peak intensity and definition between the reference and extract. Minor peak differences appearing at close wavelengths could be linked to shifts in the chemical structure of the gum, potentially due to the influence of various Xanthomonas strains or the experimental production conditions (Miranda et al., 2020). The reference XG spectrum suggests a higher purity with sharper, more distinct peaks. In contrast, the extract might display broader peaks due to impurities. These variations can affect peak clarity and intensity, suggesting differences in the concentration or purity of the sample. While the extracted sample preserves xanthan's essential structural features, it shows some spectral differences that could be due to the involvement of different bacterial strains of xanthan production in AGS systems or impurities present in the sample as the sample has not gone through the further purification process. ii. HNMR As shown in Figure 4.10, the 1HNMR spectra for xanthan reference (A) and extract (B) reveal crucial structural insights. Both show proton peaks from 1 to 5 ppm, which is characteristic of xanthan gum. The proton peaks around 1-2 ppm likely indicate alkyl protons, as noted in the 1H NMR analysis where the signals of the pyruvate and acetyl groups are observed at around 1.2 ppm and 2.0-2.1 ppm, respectively (Kang et al., 2023), while those between 3-5 ppm relate to sugar ring protons, highlighting its polysaccharide nature. 54 Figure 0.11 1HNMR spectra of xanthan reference (upper) and xanthan extract (lower), recorded in D2O at 400 MHz The reference displays sharper, clearer peaks, suggesting higher purity than the extract. In contrast, the extract shows broader peaks, possibly due to impurities or concentration variations. The weakly developed proton peaks around 5.5 and 5.8 ppm are ascribed to the protons of Vinyl groups (CH=CH2), which can be seen in both spectra (Hamcerencu et al., 2007). Peaks beyond 6 ppm in the extract might also signal impurities not seen in the reference. While both maintain xanthan's core structure, the extract may have additional elements affecting its spectral properties. Additionally, acetyl and pyruvyl peaks are key structural indicators of the presence of xanthan gum (Hassler & Doherty, 1990). The protons of the acetyl groups generally appear around 2.5 ppm, while pyruvyl groups appear near 1.5-2 ppm in the proton spectra. It is reported that proton peaks around 1.94 and 2.67 ppm indicate pyruvate and acetate content in the xanthan gum, 55 which can be seen in both spectra (Amaral et al., 2021). These peaks confirm the presence of functional groups essential for xanthan's viscosity and gel-forming abilities (Jadav et al., 2023). The sharper peaks in the reference indicate higher purity and defined acetyl content, whereas more peaks and broader peaks in the extract suggest impurities in the sample. 4.4. Statistical analysis and optimization 4.4.1. Pearson correlation Pearson correlation analysis was performed on the xanthan yield data to show the relationship between each factor (OLR, COD/N ratio, and feeding strategy) and xanthan yield. Pearson correlation evaluated the strength of correlation between the variables. The Pearson correlation coefficient (r) ranges from -1 to +1, where a positive value indicates a positive correlation (as one variable increases, the other also increases), 0 indicates no correlation (no linear relationship), and negative values indicate negative correlation (as one variable increases, the other decreases). Table 4.1 shows a moderate negative correlation (-0.512) between the COD/N ratio and xanthan yield, indicating that higher COD/N ratios reduce xanthan production. The p-value for the relationship between COD/N and xanthan yield was 0.158, which indicated that the relationship was not statistically significant at the 0.05 significance level. Conversely, OLR had a strong positive correlation (0.831) with xanthan yield, suggesting that increasing OLR boosts xanthan yield. The p-value for the relationship between OLR and xanthan yield was 0.006, indicating that the relationship was statistically significant at the 0.05 significance level. In comparison, the feeding strategy shows a very weak positive correlation (0.042) with xanthan yield, implying that variations in feeding duration have minimal effect on production levels. The p-value for the relationship between feeding strategy and xanthan yield was 0.915, which indicated that the 56 relationship was not statistically significant at the 0.05 significance level. Overall, this analysis highlights that focusing mainly on OLR is the most effective approach to improving xanthan production in the bioreactor. Table 0.1 Pearson correlation COD/N OLR OLR 0.000 Feeding strategy 0.000 -0.000 – 0.512 (p = 0.158) 0.831 (p = 0.006) Xanthan yield (mg/g biomass) Feeding strategy 0.042 (p = 0.915) 4.4.2. Optimization Table 4.2 shows the response for means and S/N ratio while Figure 4.11 shows the main effect plots for the mean a) and S/N ratio b). In Table 4.2, using a "larger is better" approach, OLR was identified as the most influential factor, with the most significant impact and a delta of 9. Similarly, the means in Table 4.2 show that OLR yielded the highest average output with a delta of 21, highlighting its importance in optimization. The S/N ratio in Table 4.2 shows indicates that COD/N was the second most crucial factor with a delta value of 5. Additionally, COD/N ratio ranked second, with a delta of 13. Further, the S/N ratio in Table 4.2 demonstrate that feeding strategy is less critical, with a delta value of 1. Moreover, the feeding strategy had the least effect, with a delta of 3 in the mean Table 4.2. From the main effects plot, shown in Figure 4.11 a), the OLR has a strong positive trend, with higher rates significantly boosting xanthan yield, likely due to increased substrate availability. The main effects plot for S/N ratios shown in Figure 4.11 b), illustrated that the OLR displayed a notable upward trend, with the highest S/N ratio at 2.1, underscoring its key role in enhancing yield consistency by providing higher nutrient concentration. The main effects plot for means 57 (Figure 4.11 b), illustrates that the COD/N ratio showed a decline in xanthan yield as it increased from 10 to 30, indicating that lower ratios favored production by providing a better nutrient balance. The main effects plot for S/N ratios (Figure 4.11 b) illustrated that as the COD/N ratio increased from 10 to 30, there was a decline in the mean S/N ratios, suggesting that lower ratios led to more consistent and higher yields. The main effects plot for means, shown in Figure 4.11 (a), show that the feeding strategy had minimal impact across the different levels of COD/N ratios (10, 30, and 60 min), suggesting it was less influential than COD/N and OLR. Additionally, the main effects plot for S/N ratios (Figure 4.11 b), illustrated that the feeding strategy showed little variation across its levels (10, 30, 60 min), indicating a lesser impact on production robustness than COD/N ratio and OLR. Table 0.2 Response for means and S/N ratio Level COD/N OLR Feeding strategy Means 1 28 12 21 2 22 21 23 3 15 33 22 Delta 13 21 3 Rank 2 1 3 S/N Ratio 1 28 21 26 2 26 26 26 3 23 30 26 Delta 5 9 1 Rank 2 1 3 58 Figure 0.12: Main effects plots for a) means, b) S/N ratio 59 Chapter 5 Conclusions and Recommendations 5.1. Conclusions With the emergence of the wastewater biorefinery concept, WWTPs are currently viewed as WRRF that treat wastewater for reclamation as well as for the recovery of renewable energy, nutrient, and other high value products which can contribute to a circular economy. The AGS biotechnology has emerged with strong potential for simultaneous wastewater treatment and resource recovery. In this study, the effect of OLR, COD/N ratio, and feeding strategy on xanthan biosynthesis in the aerobic granule matrix during wastewater treatment was determined. The main conclusions drawn from this research are outlined below: ▪ Xanthan was identified in the aerobic granule matrix ▪ Conventional method for xanthan recovery was modified and applied for xanthan recovery from waste aerobic granules ▪ The AGS systems achieved COD, NH3-N, and PO4–P removal efficiencies of 95 ± 5%, 73 ± 23%, 72 ± 18%, respectively. ▪ The AGS system achieved stability throughout the experimental period as both S S 30 values were in the range 20 ± 2 mL/g – 30 ± 2 mL/g and the S 30/S 5 and 5 ratio was consistently between 0.9 and 1.0 throughout the experimental duration. ▪ OLR was identified as the most influential factor affecting xanthan production. Xanthan yield increased with increasing OLR, attaining an optimum yield of 41 ± 7 mg xanthan/g biomass. Pearson correlation analysis revealed a significant positive correlation (r = 0.831) between OLR and xanthan yield; and, this was statistically significant at 95% confidence level (p=0.006). This observation demonstrates the critical influence of OLR on xanthan production in AGS systems. 60 ▪ A moderate negative correlation (r = - 0.512) was obtained between the COD/N ratio and xanthan yield (p=0.158). This result was not statistically significant at 95% confidence level (p=0.158). This finding indicate operating AGS systems at higher COD/N ratios would result in reduced xanthan biosynthesis. ▪ The feeding strategy had a very weak positive correlation (r = 0.042) with xanthan yield. This result was not statistically significant at 95% confidence level (p=0.915), implying that variations in the feeding regime has minimal effect on xanthan production. ▪ Taguchi mean effect analysis showed that OLR of 2.1 kg COD/m³∙d and C/N ratio of 10, were optimal for xanthan production in the aerobic granule matrix in AGS wastewater treatment systems. 5.2. Limitations of the study This research had some limitations. These include: ▪ The study was limited to the use of synthetic municipal wastewater in a laboratorycontrolled environment, which may not fully represent the complexity of real wastewater. Commercial and domestic wastewater contains diverse contaminants, and fluctuating nutrient compositions that could influence xanthan biosynthesis in AGS bioreactors. The controlled conditions ensured consistency in experimental parameters but may not accurately reflect operational challenges in full-scale wastewater treatment plants. ▪ The study focused on OLR, COD/N ratio, and feeding strategy, but other key parameters such as sludge age, substrate type, temperature, hydrodynamic shear force, feast-famine period ratio, hydraulic retention time, and volumetric exchange ratio were not studied. These factors may also play a crucial role in xanthan biosynthesis and 61 should be considered in future research to provide a more comprehensive optimization framework. 5.3. Recommendations Based on the results obtained from this thesis, it is imperative to further investigate the following research areas: (i) Future studies should explore the impact of other key operational factors such as sludge age, substrate type, temperature, hydrodynamic shear force, feast-famine period ratio, hydraulic retention time, and volumetric exchange ratio on xanthan biosynthesis in AGS systems. These factors are known to impact EPS production and can influence xanthan yield as well. More comprehensive studies will help optimize conditions for higher xanthan yields and long-term AGS stability. (ii) Since the present study used synthetic municipal wastewater in a controlled laboratory environment, future research should validate the findings using real wastewater. This will provide insights into the effects of variable wastewater compositions and loads as well as operational challenges in full-scale treatment systems. 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COD calibration curve COD calibration curve 0.25 y = 0.0004x + 0.0118 R² = 0.9874 Absorbance 0.2 0.15 0.1 0.05 0 0 100 200 300 Known COD (mg/L) 400 500 Linear (Known COD (mg/L)) -1- 600 Appendix B. Wet xanthan precipitate in the 50 mL vial Appendix C. Permission to include Published Paper in the Thesis -2- Appendix D. Permission to include Published Paper from the Co-authors -3- -4-