Evolutionary optimization of biogas production from food, fruit, and vegetable (FFV) waste

• Published in: Biomass conversion and biorefinery 2024, Vol.14 (11), p.12113-12125
• Main Authors: Olatunji, Obafemi O., Adedeji, Paul A., Madushele, Nkosinathi, Rasmeni, Zelda Z., van Rensburg, Nickey Janse
• Format: Article
• Language: English
• Subjects: Adaptive systems; Anaerobic digestion; Anaerobic processes; Artificial neural networks; Biogas; Biotechnology; Clustering; Effectiveness; Energy; Energy utilization; Evolutionary algorithms; Fuzzy logic; Genetic algorithms; Hydraulic retention time; Loading rate; Mathematical analysis; Mathematical models; Microorganisms; Optimization; Organic loading; Original Article; Performance measurement; Predictions; Renewable and Green Energy; Root-mean-square errors
• ISSN: 2190-6815; 2190-6823
• DOI: 10.1007/s13399-023-04506-0

The success of anaerobic digestion (AD) process for biogas production is contingent upon complex mix of operating factors, process conditions, and feedstock types, which could be affected by inadequate understanding of microbial, kinetic, and physicochemical processes. To address these limitations, efforts have been directed toward developing mathematical and intelligent models. Although mathematical models provide near-optimal solutions, they are time consuming, highly expensive, and demanding. Intelligent standalone models are also limited by their low predictive capability and inability to guarantee global optimal solution for the prediction of cumulative biogas yield for FFV waste. However, hyperparameter optimization of such models is essential to improve the prediction performance for cumulative biogas yield for FFV waste. Therefore, this study applies a genetic algorithm (GA) to optimize an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of cumulative biogas production. Seven (7) input variables, organic loading rate (OLR), volatile solids (VS), pH, hydraulic retention time (HRT), temperature, retention time, and reaction volume, were considered with cumulative biogas production as the output. The effect of varying clustering techniques was evaluated. The three (3) clustering techniques evaluated are fuzzy c-means and subtractive clustering and grid partitioning. The hybrid model was evaluated based on some verified statistical performance metrics. Optimal root mean squared error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), and standard deviation error (error STD) of 0.0529, 0.0326,7.6742, and 0.0474, respectively, were reported at the model testing phase for the subtractive clustering technique being the best-performing model. The results confirm the capacity of hybrid evolutionary (genetic) algorithm based on subtractive clustering technique to predict the biogas yield from FFV and serve as an effective tool for the upscaling of anaerobic digestion units as well as in techno-economic studies toward more efficient energy utilization. Graphical abstract