Asit Chatterjee,
Mahim Mathur,
Anil Pal,
Mukesh Kumar Gupta,
Amit Tiwari,
Adamya Gupta,
- Research Scholar, Department of Civil Engineering, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
- Professor, Department of Civil Engineering, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
- Assistant Professor, Department of Computer Application, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
- Professor, Department of Electrical Engineering, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
- Assistant Professor, Department of Mechanical Engineering, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
- Research Scholar, Department of Computer Science and Engineering, Jaipur Engineering College & Research Centre, Jaipur, Rajasthan, India
Abstract
The correct validation and realistic application of optimized anaerobic digestion (AD) models are essential steps in transferring biogas production systems to real-life. This paper outlines an experimental validation and deployment pipeline of an AI-optimized model of the methane yield prediction model based on the application of more advanced machine learning and Bayesian optimization methods. Others The validated surrogate-assisted optimization model was tested with controlled laboratory-scale AD experiments at optimized operating conditions, such as temperature, pH, organic loading rate (OLR), and carbon-to-nitrogen (C/N) ratio. Surrogate predictions were compared to experimental yields of methane to evaluate the level of accuracy, deviation, pattern of residuals and behavior of uncertainty. Findings indicate that the predicted and experimental values are in high agreement and the error is less than 5 percent with a correlation coefficient of more than 0.96 which supports the soundness and ability to generalize the optimized model. Also, implementation preparedness was evaluated based on the major dimensions like scalability, cost impact, operational feasibility, environmental benefit, and monitoring requirements. It is shown in the analysis that the optimized AD model can be used to support real-time decision-making and improve the operation in biogas plants. The proposed validation system will guarantee reliability of the model, facilitate evidence-based parameter tuning, and make field implementation of AI-based systems in the context of AD optimization practical.
Keywords: Anaerobic digestion, experimental validation, model implementation, optimization framework, prediction of the yield of methane
[This article belongs to International Journal of Environmental Noise and Pollution Control ]
Asit Chatterjee, Mahim Mathur, Anil Pal, Mukesh Kumar Gupta, Amit Tiwari, Adamya Gupta. Experimental Validation and Implementation Framework for Optimized Methane Yield Prediction in Anaerobic Digestion. International Journal of Environmental Noise and Pollution Control. 2026; 04(01):25-32.
Asit Chatterjee, Mahim Mathur, Anil Pal, Mukesh Kumar Gupta, Amit Tiwari, Adamya Gupta. Experimental Validation and Implementation Framework for Optimized Methane Yield Prediction in Anaerobic Digestion. International Journal of Environmental Noise and Pollution Control. 2026; 04(01):25-32. Available from: https://journals.stmjournals.com/ijenpc/article=2026/view=236349
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| Volume | 04 |
| Issue | 01 |
| Received | 19/01/2026 |
| Accepted | 26/01/2026 |
| Published | 10/02/2026 |
| Publication Time | 22 Days |
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