Optimizing waste-to-energy conversion of banana peel biomass-based scheme using metaheuristic particle swarm optimization (PSO) algorithm - Fang, Wei; Farouk, Naeim; Abed, Azher M.; Khalil, Sana Ahmed; Abdullaev, Sherzod; Alhomayani, Fahad M.; Khan, Mohammad Nadeem; Bayhan, Zahra; Knani, Salah; Afzal, Abdul Rahman (Energy (Oxford), 2025)
The optimization of biomass to energy systems using metaheuristic particle swarm optimization (PSO) technique holds great promise for advancing the efficiency, sustainability, and competitiveness of bioenergy production. The importance of this research field lies in its potential to address key challenges in biomass utilization and contribute to achieving global energy and environmental goals. This study employs the metaheuristic PSO technique to optimize banana peel-based combined heat and power systems. Various regression models are developed for response variables and their accuracy is evaluated. R-squared values exceeding 93 % strongly suggests that the independent variables account for a significant portion of the variability in the dependent variables, indicating robust predictive capabilities and high reliability in the models. Different Pareto-front solutions are derived to reach near-optimal conditions to achieve maximum exergy and electrical efficiencies and minimize emission levels (the ratio of the carbon dioxide emission to the outputs of the system). The optimum outcome measures include an electrical efficiency of 27.29 %, exergy efficiency of 19.56 %, and emissions level of 1052 kg/MW. By addressing these research areas, it is possible to advance the development and deployment of efficient, cost-effective, and sustainable biomass-based energy systems to meet the growing global demand for renewable energy by focusing on metaheuristic optimization technique. •A banana peel biomass-based energy system is optimized via metaheuristic technique.•Electrical and exergy efficiencies and emission levels are optimized.•Regression models with R-squared values exceeding 93 % are achieved.•Particle swarm optimization results in electrical efficiency of 27.3 %.•Optimum exergy efficiency of 19.6 % and emission of 1052 kg/MW are achieved.