Optimizing waste-to-energy conversion of banana peel biomass-based scheme using metaheuristic particle swarm optimization (PSO) algorithm
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 chal...
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          | Published in | Energy (Oxford) Vol. 327; p. 136427 | 
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| Main Authors | , , , , , , , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
            Elsevier Ltd
    
        01.07.2025
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| Subjects | |
| Online Access | Get full text | 
| ISSN | 0360-5442 | 
| DOI | 10.1016/j.energy.2025.136427 | 
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| Abstract | 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. | 
    
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| AbstractList | 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. 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.  | 
    
| ArticleNumber | 136427 | 
    
| Author | Abdullaev, Sherzod Bayhan, Zahra Fang, Wei Khalil, Sana Ahmed Farouk, Naeim Alhomayani, Fahad M. Abed, Azher M. Knani, Salah Khan, Mohammad Nadeem Afzal, Abdul Rahman  | 
    
| Author_xml | – sequence: 1 givenname: Wei surname: Fang fullname: Fang, Wei organization: Weifang University of Science and Technology, Weifang, 262700, China – sequence: 2 givenname: Naeim orcidid: 0000-0002-7368-658X surname: Farouk fullname: Farouk, Naeim email: n.mohammed@psau.edu.sa organization: Mechanical Engineering Department, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia – sequence: 3 givenname: Azher M. orcidid: 0000-0002-8411-6742 surname: Abed fullname: Abed, Azher M. email: azhermuhson@uomus.edu.iq organization: Air Conditioning and Refrigeration Techniques Engineering Department, College of Engineering and Technologies, Al-Mustaqbal University, Babylon, 51001, Iraq – sequence: 4 givenname: Sana Ahmed surname: Khalil fullname: Khalil, Sana Ahmed organization: Department of Chemistry, Alwajh College, University of Tabuk, Tabuk, Saudi Arabia – sequence: 5 givenname: Sherzod surname: Abdullaev fullname: Abdullaev, Sherzod organization: Faculty of Chemical Engineering, New Uzbekistan University, Tashkent, Uzbekistan – sequence: 6 givenname: Fahad M. surname: Alhomayani fullname: Alhomayani, Fahad M. organization: College of Computers and Information Technology, Taif University, Saudi Arabia – sequence: 7 givenname: Mohammad Nadeem surname: Khan fullname: Khan, Mohammad Nadeem email: mn.khan@mu.edu.sa organization: Department of Mechanical and Industrial Engineering, College of Engineering, Majmaah University, Al-Majmaah, 11952, Saudi Arabia – sequence: 8 givenname: Zahra surname: Bayhan fullname: Bayhan, Zahra organization: Department of Physics, College of Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia – sequence: 9 givenname: Salah surname: Knani fullname: Knani, Salah organization: Center for Scientific Research and Entrepreneurship, Northern Border University, Arar, 73213, Saudi Arabia – sequence: 10 givenname: Abdul Rahman surname: Afzal fullname: Afzal, Abdul Rahman organization: Department of Industrial Engineering, College of Engineering, University of Business and Technology, Jeddah, 21361, Saudi Arabia  | 
    
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| Keywords | Biomass to energy Banana peel Metaheuristic Multi-objective optimization Environmental protection  | 
    
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| SubjectTerms | algorithms Banana peel banana peels bananas bioenergy biomass Biomass to energy carbon dioxide cost effectiveness Environmental protection exergy heat Metaheuristic Multi-objective optimization  | 
    
| Title | Optimizing waste-to-energy conversion of banana peel biomass-based scheme using metaheuristic particle swarm optimization (PSO) algorithm | 
    
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