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Hybrid GA-PSO-optimized neural network for biogas production: Comparative evaluation of metaheuristic algorithms

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  • Ghadiri, Arezoo
  • Pazoki, Maryam
  • Erfani, Saeed

Abstract

Biogas derived from biomass is increasingly being recognized among researchers as a key component in clean and sustainable energy systems. To predict biogas production under various operating conditions, this study develops an artificial neural network (ANN) model that is separately optimized with four metaheuristic techniques—genetic algorithm (GA), particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and a hybrid GA-PSO approach. In this model, three process factors, including NaOH concentration, temperature, and reaction time, are used to predict four outputs: methane production, methane concentration, methane efficiency, and volatile solids removal. For methane concentration, the hybrid GA-PSO method regularly produced the best results: R ≈ 0.93 and RMSE ≈0.22; for volatile solids removal, R ≈ 0.96 and RMSE ≈0.15; other outputs showed R ≈ 0.89–0.90 with RMSE ≈0.28. Sensitivity analysis revealed that temperature and NaOH concentration are the main drivers of methane yield. These results demonstrate the effectiveness of hybrid optimization in improving ANN-based biogas models and provide practical recommendations on how to improve anaerobic digestion performance.

Suggested Citation

  • Ghadiri, Arezoo & Pazoki, Maryam & Erfani, Saeed, 2026. "Hybrid GA-PSO-optimized neural network for biogas production: Comparative evaluation of metaheuristic algorithms," Renewable Energy, Elsevier, vol. 262(C).
    • Handle: RePEc:eee:renene:v:262:y:2026:i:c:s0960148126002570
    DOI: 10.1016/j.renene.2026.125432
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