Bayesian Optimization–Driven Operating Parameter Tuning for Maximizing Methane Yield in Anaerobic Digestion

Year : 2026 | Volume : 04 | Issue : 01 | Page : 1 8
    By

    Asit Chatterjee,

  • Mahim Mathur,

  • Anil Pal,

  • Mukesh Kumar Gupta,

  • Amit Tiwari,

  • Adamya Gupta,

  1. Research Scholar, Department of Civil Engineering, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
  2. Professor, Department of Civil Engineering, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
  3. Assistant Professor, Assistant Professor, Department of Computer Application, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
  4. Professor, Department of Electrical Engineering, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
  5. Assistant Professor, Department of Mechanical Engineering, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
  6. Research Scholar, Department of Computer Science and Engineering, Jaipur Engineering College & Research Centre, Jaipur, Rajasthan, India

Abstract

To achieve maximum methane production in an anaerobic digestion (AD) process, a combination of various operational parameters must be tuned nonlinearly in the digestion ecosystem. The conventional trial and error optimization methods are slow, resource consuming, and in most instances, cannot model the intricate parameter interaction in biogas production. The current work introduces a Bayesian Optimization-based model to optimize the set of conditions to maximize the level of methane produced using the agricultural residues, in terms of temperature, pH, organic loading rate (OLR), and carbon/Nitrogen (C/N) ratio. The surrogate models were trained on a structured and labeled AD dataset, and were used as fast evaluators in the optimization loop. Bayesian Optimization using Gaussian Process Regression (GPR) with upper confidence bound (UCB) acquisition was used to search and exploit the parameter space of operational constraints. Findings indicate a high increase in the yield of methane, a baseline figure of 260 mL CH 4/g VS to 315 mL CH 4/g VS, which shows an improvement of 21.2%. Stable convergence, a high level of surrogate accuracy, and uniform identification of biologically plausible parameter combinations are found in the optimization trajectories. This article reveals the potential of the Bayesian optimization (BO) as an efficient instrument of AD process optimization, which can allow making operational decisions that are data driven and minimize the number of experiments in waste-to-energy framework.

Keywords: Bayesian optimization, anaerobic digestion, increase in methane yield, surrogate modeling, constrained parameter search

[This article belongs to International Journal of Energy and Thermal Applications ]

How to cite this article:
Asit Chatterjee, Mahim Mathur, Anil Pal, Mukesh Kumar Gupta, Amit Tiwari, Adamya Gupta. Bayesian Optimization–Driven Operating Parameter Tuning for Maximizing Methane Yield in Anaerobic Digestion. International Journal of Energy and Thermal Applications. 2026; 04(01):1-8.
How to cite this URL:
Asit Chatterjee, Mahim Mathur, Anil Pal, Mukesh Kumar Gupta, Amit Tiwari, Adamya Gupta. Bayesian Optimization–Driven Operating Parameter Tuning for Maximizing Methane Yield in Anaerobic Digestion. International Journal of Energy and Thermal Applications. 2026; 04(01):1-8. Available from: https://journals.stmjournals.com/ijeta/article=2026/view=236321


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Regular Issue Subscription Original Research
Volume 04
Issue 01
Received 19/01/2026
Accepted 26/01/2026
Published 10/02/2026
Publication Time 22 Days
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