Advanced control-oriented modeling of two-stage anaerobic digesters for resilient and energy-efficient biogas production

Authors

Article ID: 797
46 Views

DOI:

https://doi.org/10.18686/cest797

Keywords:

clean energy; anaerobic digestion; biogas; model predictive control; dynamic stability; ADM1; renewable methane

Abstract

Anaerobic Digestion (AD) is a key technology in the circular bioeconomy due to increasing demand for sustainable energy and efficient organic waste management. Advanced configurations, such as the Two-Stage Anaerobic Digestion (TSAD) systems, yield higher methane production and allow for superior operational flexibility. But tight biochemical couplings between stages cause extreme dynamic instability in the face of real-world disturbances. In this study, a reduced-order dynamic model based on Anaerobic Digestion Model No. 1 (ADM1) was employed to study stability behavior and assess advanced control strategies for resilient and energy-efficient biogas generation. Five real disturbance scenarios were simulated: organic loading shock (3–7 kg VS·m⁻3·d⁻1), acidification (pH 6.2 → 5.5), thermal shock (35 → 25 °C), Volatile Fatty Acids (VFA)/ammonia inhibition and feed interruption (48 h). Three control strategies, Proportional Integral Derivative (PID), Fuzzy Logic Control (FLC) and Model Predictive Control (MPC), were examined comparatively under open- and closed-loop operation. Uncontrolled disturbances caused methane yield losses of 40–60% and recovery times >72 h. PID reduced loss to 20–35%, FLC to 10–25%, whereas MPC restricted methane loss to <10% with up to 60% reduction in recovery time. MPC also achieved an improvement in pH compared to PID. The results show that the use of MPC significantly improves the energy recovery efficiency and operational resilience. The proposed reduced-order ADM1 is innovative in that it establishes a computationally efficient framework for modeling TSAD systems, integrating predictive control with energy performance metrics, thus providing stability and scalability attributes.

Downloads

Published

2026-06-15

How to Cite

Al Shehihi, J., & Raut, N. (2026). Advanced control-oriented modeling of two-stage anaerobic digesters for resilient and energy-efficient biogas production. Clean Energy Science and Technology, 4(3). https://doi.org/10.18686/cest797

References

1. Shen L, Elshkaki A, Zhong S, et al. Global Energy Transition and Low Carbon Technology Pathways. Energies. 2025; 18(21): 5701. doi: 10.3390/en18215701 DOI: https://doi.org/10.3390/en18215701

2. ur Rehman A, Sanjari MJ, Elavarasan RM, et al. Sustainability-aligned pathways for energy transition: A review of low-carbon energy network solutions. Renewable and Sustainable Energy Reviews. 2026; 226: 116428. doi: 10.1016/j.rser.2025.116428 DOI: https://doi.org/10.1016/j.rser.2025.116428

3. Ani OI, Aniokete TC, Agbo AO. Assessing potential of biogas: Harnessing sustainable energy from biomass for renewable solutions. Al Rafidain Journal of Engineering Sciences. 2024; 2(1): 330–349. doi: 10.61268/q0b72g38 DOI: https://doi.org/10.61268/q0b72g38

4. Zheng X, Li R. Critical Review on Two-Stage Anaerobic Digestion with H2 and CH4 Production from Various Wastes. Water. 2024; 16(11): 1608. doi: 10.3390/w16111608 DOI: https://doi.org/10.3390/w16111608

5. Ding L, Chen Y, Xu Y, et al. Improving treatment capacity and process stability via a two-stage anaerobic digestion of food waste combining solid-state acidogenesis and leachate methanogenesis/recirculation. Journal of Cleaner Production. 2021; 279: 123644. doi: 10.1016/j.jclepro.2020.123644 DOI: https://doi.org/10.1016/j.jclepro.2020.123644

6. Harirchi S, Wainaina S, Sar T, et al. Microbiological insights into anaerobic digestion for biogas, hydrogen or volatile fatty acids (VFAs): A review. Bioengineered. 2022; 13(3): 6521–6557. doi: 10.1080/21655979.2022.2035986 DOI: https://doi.org/10.1080/21655979.2022.2035986

7. Shinde R, Hackula A, Marycz M, et al. Dynamic anaerobic digestion-based biorefineries for on-demand renewable energy and bioproducts in a circular bioeconomy. Trends in Biotechnology. 2025; 43(5): 1140–1165. doi: 10.1016/j.tibtech.2025.01.005 DOI: https://doi.org/10.1016/j.tibtech.2025.01.005

8. Shah PJ. Advances in Hybrid Modeling: Parameter Estimation and Control for Chemical and Biochemical Processes [PhD Thesis]. Texas A&M University; 2025.

9. Yadav GD, Magadum DB. Kinetic modelling of enzyme catalyzed biotransformation involving activations and inhibitions. In: Senturk M (editor). Enzyme Inhibitors and Activators. InTech; 2017. doi: 10.5772/67692 DOI: https://doi.org/10.5772/67692

10. Abawalo M, Pikoń K, Landrat M, et al. Hydrogen production from biowaste: A systematic review of conversion technologies, environmental impacts, and future perspectives. Energies. 2025; 18(17): 4520. doi: 10.3390/en18174520 DOI: https://doi.org/10.3390/en18174520

11. Komulainen TM, Jonassen KR, Antonsen SG. Estimation and Control of WRRF Biogas Production. Energies. 2024; 17(23): 5922. DOI: https://doi.org/10.3390/en17235922

12. Velasquez-Pinas JA, Wedgwood C, Maya DMY, et al. Advances in agricultural crop residue utilization for biogas production and sustainable energy solutions. Journal of Agriculture and Food Research. 2026; 27: 102864. doi: 10.1016/j.jafr.2026.102864 DOI: https://doi.org/10.1016/j.jafr.2026.102864

13. Azúa-Poblete M, Cedeño AL, Agüero JC, et al. Enhancing anaerobic digestion performance with offset-free model predictive control. Journal of Water Process Engineering. 2025; 78: 108785. doi: 10.1016/j.jwpe.2025.108785 DOI: https://doi.org/10.1016/j.jwpe.2025.108785

14. Nkambule MS, Hasan AN, Shongwe T. A review of intelligent control strategies for energy management systems in microgrids. Energy Conversion and Management: X. 2025; 28: 101323. doi: 10.1016/j.ecmx.2025.101323 DOI: https://doi.org/10.1016/j.ecmx.2025.101323

15. Wang S, Dang Q, Gao Z, et al. An innovative square root-untraced Kalman filtering strategy with full-parameter online identification for state of power evaluation of lithium-ion batteries. Journal of Energy Storage. 2024; 104: 114555. doi: 10.1016/j.est.2024.114555 DOI: https://doi.org/10.1016/j.est.2024.114555

16. Wang S, Wang C, Takyi-Aninakwa P, et al. An improved parameter identification and radial basis correction–differential support vector machine strategies for state-of-charge estimation of urban-transportation-electric-vehicle lithium-ion batteries. Journal of Energy Storage. 2024; 80: 110222. doi: 10.1016/j.est.2023.110222 DOI: https://doi.org/10.1016/j.est.2023.110222

17. Kil H, Li D, Xi Y, et al. Model predictive control with on-line model identification for anaerobic digestion processes. Biochemical Engineering Journal. 2017; 128: 63–75. doi: 10.1016/j.bej.2017.08.004 DOI: https://doi.org/10.1016/j.bej.2017.08.004

18. Weinrich S, Nelles M. Systematic simplification of the Anaerobic Digestion Model No. 1 (ADM1): Model development and stoichiometric analysis. Bioresource Technology. 2021; 333: 125124. doi: 10.1016/j.biortech.2021.125124 DOI: https://doi.org/10.1016/j.biortech.2021.125124

19. García-Diéguez C, Bernard O, Roca E. Reducing the Anaerobic Digestion Model No. 1 for its application to an industrial wastewater treatment plant treating winery effluent wastewater. Bioresource Technology. 2013; 132: 244–253. doi: 10.1016/j.biortech.2012.12.166 DOI: https://doi.org/10.1016/j.biortech.2012.12.166

20. Economou CN, Manthos G, Zagklis D, et al. ADM1-based modeling of biohydrogen production through anaerobic co-digestion of Agro-industrial wastes in a continuous-flow stirred-tank reactor system. Fermentation. 2024; 10(3): 138. doi: 10.3390/fermentation10030138 DOI: https://doi.org/10.3390/fermentation10030138

21. Sidek NF, Harun N. A review of process simulation & modeling approach in anaerobic digestion process for biogas production. AIP Conference Proceedings. 2023; 2792(1): 020007. doi: 10.1063/5.0148557 DOI: https://doi.org/10.1063/5.0148557

22. Hagos K, Zong J, Li D, et al. Anaerobic co-digestion process for biogas production: Progress, challenges and perspectives. Renewable and Sustainable Energy Reviews. 2017; 76: 1485–1496. doi: 10.1016/j.rser.2016.11.184 DOI: https://doi.org/10.1016/j.rser.2016.11.184

23. Liao-McPherson D, Nicotra MM, Kolmanovsky I. Time-distributed optimization for real-time model predictive control: Stability, robustness, and constraint satisfaction. Automatica. 2020; 117: 108973. DOI: https://doi.org/10.1016/j.automatica.2020.108973

24. Batstone DJ, Virdis B. The role of anaerobic digestion in the emerging energy economy. Current Opinion in Biotechnology. 2014; 27: 142–149. doi: 10.1016/j.copbio.2014.01.013 DOI: https://doi.org/10.1016/j.copbio.2014.01.013

25. Meegoda JN, Li B, Patel K, et al. A Review of the Processes, Parameters, and Optimization of Anaerobic Digestion. International Journal of Environmental Research and Public Health. 2018; 15(10): 2224. doi: 10.3390/ijerph15102224 DOI: https://doi.org/10.3390/ijerph15102224

26. Angelidaki I, Treu L, Tsapekos P, et al. Biogas upgrading and utilization: Current status and perspectives. Biotechnology Advances. 2018; 36(2): 452–466. doi: 10.1016/j.biotechadv.2018.01.011 DOI: https://doi.org/10.1016/j.biotechadv.2018.01.011

27. Yan W, Xu H, Lu D, et al. Effects of sludge thermal hydrolysis pretreatment on anaerobic digestion and downstream processes: Mechanism, challenges and solutions. Bioresource Technology. 2022; 344: 126248. doi: 10.1016/j.biortech.2021.126248 DOI: https://doi.org/10.1016/j.biortech.2021.126248

28. Ram NR, Nikhil GN. A critical review on sustainable biogas production with focus on microbial-substrate interactions: Bottlenecks and breakthroughs. Bioresource Technology Reports. 2022; 19: 101170. doi: 10.1016/j.biteb.2022.101170 DOI: https://doi.org/10.1016/j.biteb.2022.101170

29. Yang J, Zhang J, Du X, et al. Ammonia inhibition in anaerobic digestion of organic waste: A review. International Journal of Environmental Science and Technology. 2025; 22(5): 3927–3942. doi: 10.1007/s13762-024-06029-1 DOI: https://doi.org/10.1007/s13762-024-06029-1

30. Wu G, Yin Q, Wang Z. Anaerobic Digestion Under Environmentally Stressed Conditions. In: Wu G (editor). Anaerobic Digestion. Green Energy and Technology. Springer; 2024. doi: 10.1007/978-3-031-69378-6_5 DOI: https://doi.org/10.1007/978-3-031-69378-6_5

31. Duan Y, Wang Z, Ganeshan P, et al. Anaerobic digestion in global bio-energy production for sustainable bioeconomy: Potential and research challenges. Renewable and Sustainable Energy Reviews. 2025; 208: 114985. doi: 10.1016/j.rser.2024.114985 DOI: https://doi.org/10.1016/j.rser.2024.114985

32. Sun J, Pan F, Zhu H, et al. Enhancing low-temperature anaerobic digestion of low-strength organic wastewater through bio-electrochemical technology. International Journal of Hydrogen Energy. 2024. 58: 1062–1074. doi: 10.1016/j.ijhydene.2024.01.255 DOI: https://doi.org/10.1016/j.ijhydene.2024.01.255

33. Jia R, Song YC, An Z, et al. Unraveling Anaerobic Digestion Instability: A Simple Index Based on the Kinetic Balance of Biochemical Reactions. Processes. 2023; 11(10): 2852. doi: 10.3390/pr11102852 DOI: https://doi.org/10.3390/pr11102852

34. Wang S, Xu C, Song L, et al. Anaerobic Digestion of Food Waste and Its Microbial Consortia: A Historical Review and Future Perspectives. International Journal of Environmental Research and Public Health. 2022; 19(15): 9519. doi: 10.3390/ijerph19159519 DOI: https://doi.org/10.3390/ijerph19159519

35. Kelif Ibro M, Ramayya Ancha V, Beyene Lemma D. Biogas production optimization in the anaerobic codigestion process: A critical review on process parameters modeling and simulation tools. Journal of Chemistry. 2024; 2024(1): 4599371. doi: 10.1155/2024/4599371 DOI: https://doi.org/10.1155/2024/4599371

36. Paladino O. Data Driven Modelling and Control Strategies to Improve Biogas Quality and Production from High Solids Anaerobic Digestion: A Mini Review. Sustainability. 2022; 14(24): 16467. doi: 10.3390/su142416467 DOI: https://doi.org/10.3390/su142416467

37. Simeonov I, Hubenov V. Application of Artificial Intelligence for Prediction, Monitoring, Optimization and Control of Anaerobic Digestion Processes—A Review. Processes. 2025; 13(12): 3812. doi: 10.3390/pr13123812 DOI: https://doi.org/10.3390/pr13123812

38. Hellmann S, Frontzek J, Zarate DM, et al. Multi-stage model predictive control of agricultural anaerobic digestion plant with uncertain substrate characterization. Bioresource Technology. 2025; 441: 133568. doi: 10.1016/j.biortech.2025.133568 DOI: https://doi.org/10.1016/j.biortech.2025.133568

39. Zenani S, Obileke K, Ndiweni O, et al. A Review of the Application of Fuzzy Logic in Bioenergy Technology. Processes. 2025; 13(7): 2251. doi: 10.3390/pr13072251 DOI: https://doi.org/10.3390/pr13072251

40. Li X, Wang Z, He Y, et al. A Comprehensive Review of the Strategies to Improve Anaerobic Digestion: Their Mechanism and Digestion Performance. Methane. 2024; 3(2): 227–256. doi: 10.3390/methane3020014 DOI: https://doi.org/10.3390/methane3020014

41. Pino Santana A, Garcia-Gen S, Dewasme L, et al. Model predictive control of anaerobic digestion processes using a long short-term memory network predictor. Applied Sciences. 2025; 92(7): 1063–1076. doi: 10.2166/wst.2025.139 DOI: https://doi.org/10.2166/wst.2025.139

42. Li Y, Chen Y, Wu J. Enhancement of methane production in anaerobic digestion process: A review. Applied Energy. 2019; 240: 120–137. doi: 10.1016/j.apenergy.2019.01.243 DOI: https://doi.org/10.1016/j.apenergy.2019.01.243

43. Rajagopal R, Massé DI, Singh G. A critical review on inhibition of anaerobic digestion process by excess ammonia. Bioresource Technology. 2013; 143: 632–641. doi: 10.1016/j.biortech.2013.06.030 DOI: https://doi.org/10.1016/j.biortech.2013.06.030

44. Xue T, Yan X, Li W, et al. Synergistic effect and microbial community structure of waste-activated sludge and kitchen waste solids residue mesophilic anaerobic co-digestion. Water Science & Technology. 2024; 89(12): 3163–3177. doi: 10.2166/wst.2024.186 DOI: https://doi.org/10.2166/wst.2024.186

45. Nuhu SK, Gyang JA, Kwarbak JJ. Production and optimization of biomethane from chicken, food, and sewage wastes: The domestic pilot biodigester performance. Cleaner Engineering and Technology. 2021; 5: 100298. doi: 10.1016/j.clet.2021.100298 DOI: https://doi.org/10.1016/j.clet.2021.100298

46. Moradvandi A, Heegstra S, Ceron-Chafla P, et al. Model predictive control of feed rate for stabilizing and enhancing biogas production in anaerobic digestion under meteorological fluctuations. Journal of Process Control. 2025; 147: 103375. doi: 10.1016/j.jprocont.2025.103375 DOI: https://doi.org/10.1016/j.jprocont.2025.103375