STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Energy and Thermal Applications Original Research

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

  1. Asit Chatterjee Department of Civil Engineering, Suresh Gyan Vihar University, Jaipur
  2. Mahim Mathur Department of Civil Engineering, Suresh Gyan Vihar University, Jaipur
  3. Anil Pal Assistant Professor, Department of Computer Application, Suresh Gyan Vihar University, Jaipur
  4. Mukesh Kumar Gupta Department of Electrical Engineering, Suresh Gyan Vihar University, Jaipur
  5. Amit Tiwari Department of Mechanical Engineering, Suresh Gyan Vihar University, Jaipur
  6. Adamya Gupta Department of Computer Science and Engineering, Jaipur Engineering College & Research Centre, Jaipur

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

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