Advanced Optimization of Artificial Neural Networks for a Syngas Power Plant Using Bio-Inspired Optimization Algorithms: : Comparison Between PSO, GA and a Novel Mosquito Inspired Optimization Algorithm
The integration of renewable energies sources into hybrid distributed generation systems presents significant challenges, particularly when predicting critical. Syngas power plants are often integrated into hybrid microgrids to produce electricity instead of fossil fuel-based backup generators. This...
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| Vydané v: | IEEE Conference on Technologies for Sustainability (SusTech) (Online) s. 1 - 6 |
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IEEE
20.04.2025
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| ISSN: | 2640-6810 |
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| Abstract | The integration of renewable energies sources into hybrid distributed generation systems presents significant challenges, particularly when predicting critical. Syngas power plants are often integrated into hybrid microgrids to produce electricity instead of fossil fuel-based backup generators. This work proposes an advanced optimization of artificial neural network models applied to the prediction of key parameters for a syngas-based power plant. As part of the proposed approach, a novel Mosquito Mating Swarm Optimization (MMSO) algorithm is presented. The MMSO is compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in terms statistical performance indexes and linear regression. The results show that MMSO outperforms PSO and GA in biomass and air flow parameter prediction for the syngas power plant in accuracy and stability for variable operating conditions. The improvement in parameter prediction through the MMSO contributes to the development of robust and efficient tools for the adaptable management of microgrids and hybrid distributed generation systems. |
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| AbstractList | The integration of renewable energies sources into hybrid distributed generation systems presents significant challenges, particularly when predicting critical. Syngas power plants are often integrated into hybrid microgrids to produce electricity instead of fossil fuel-based backup generators. This work proposes an advanced optimization of artificial neural network models applied to the prediction of key parameters for a syngas-based power plant. As part of the proposed approach, a novel Mosquito Mating Swarm Optimization (MMSO) algorithm is presented. The MMSO is compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in terms statistical performance indexes and linear regression. The results show that MMSO outperforms PSO and GA in biomass and air flow parameter prediction for the syngas power plant in accuracy and stability for variable operating conditions. The improvement in parameter prediction through the MMSO contributes to the development of robust and efficient tools for the adaptable management of microgrids and hybrid distributed generation systems. |
| Author | Vega-Gomez, Carlos Lucero-Tenorio, Miriam Aguila-Leon, Jesus Vargas-Salgado, Carlos |
| Author_xml | – sequence: 1 givenname: Jesus surname: Aguila-Leon fullname: Aguila-Leon, Jesus email: jesus.aguila@ieee.org organization: University Center of Tonala - University of Guadalajara,Department of Water and Energy Studies,Guadalajara,Mexico – sequence: 2 givenname: Carlos surname: Vargas-Salgado fullname: Vargas-Salgado, Carlos email: carvarsa@upvnet.upv.es organization: Universitat Politècnica de València,Department of Electrical Engineering,Valencia,Spain – sequence: 3 givenname: Carlos surname: Vega-Gomez fullname: Vega-Gomez, Carlos email: carlos.vega@cutlajomulco.udg.mx organization: University Center of Tlajomulco - University of Guadalajara,Division of Technological Development and Engineering,Guadalajara,Mexico – sequence: 4 givenname: Miriam surname: Lucero-Tenorio fullname: Lucero-Tenorio, Miriam email: miri.lucero.tenorio@ieee.org organization: Universitat Politècnica de València,Industrial Electronic Systems Group,Department of Electronic Engineering,Valencia,Spain |
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| SubjectTerms | Artificial Neural Network Optimization Artificial neural networks Biomass Electricity Genetic algorithms Hybrid power systems Mosquito Mating Swarm Optimization (MMSO) Optimization Particle swarm optimization Prediction algorithms Renewable Energies Renewable Energy Prediction Sustainable development Syngas Syngas power plant |
| Title | Advanced Optimization of Artificial Neural Networks for a Syngas Power Plant Using Bio-Inspired Optimization Algorithms: : Comparison Between PSO, GA and a Novel Mosquito Inspired Optimization Algorithm |
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