Optimization of Life Cycle Cost and Environmental Impact Functions of NiZn Batteries by Using Multi-Objective Particle Swarm Optimization (MOPSO)

Ashwani Kumar Malviya, Mehdi Zarehparast Malekzadeh, Francisco Enrique Santarremigia (), Gemma Dolores Molero, Ignacio Villalba Sanchis, Pablo Martínez Fernández and Víctor Yepes ()
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Ashwani Kumar Malviya: AITEC, Research and Innovation Department, Parque Tecnológico, C/Charles Robert Darwin, 20, Paterna, 46980 Valencia, Spain
Mehdi Zarehparast Malekzadeh: AITEC, Research and Innovation Department, Parque Tecnológico, C/Charles Robert Darwin, 20, Paterna, 46980 Valencia, Spain
Francisco Enrique Santarremigia: AITEC, Research and Innovation Department, Parque Tecnológico, C/Charles Robert Darwin, 20, Paterna, 46980 Valencia, Spain
Gemma Dolores Molero: AITEC, Research and Innovation Department, Parque Tecnológico, C/Charles Robert Darwin, 20, Paterna, 46980 Valencia, Spain
Ignacio Villalba Sanchis: Transport and Territory Research Institute, School of Civil Engineering, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
Pablo Martínez Fernández: Transport and Territory Research Institute, School of Civil Engineering, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
Víctor Yepes: Institute of Concrete Science and Technology (ICITECH), School of Civil Engineering, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain

Sustainability, 2024, vol. 16, issue 15, 1-28

Abstract: This study aims to optimize the Environmental Life Cycle Assessment (LCA) and Life Cycle Cost (LCC) of NiZn batteries using Pareto Optimization (PO) and Multi-objective Particle Swarm Optimization (MOPSO), which combine Pareto optimization and genetic algorithms (GA). The optimization focuses on the raw material acquisition phase and the end-of-life phase of NiZn batteries to improve their sustainability Key Performance Indicators (KPIs). The optimization methodology, programmed in MATLAB, is based on a formulation model of LCC and the environmental LCA, using data available from the Ecoinvent database, the OpenLCA software (V1.11.0), and other public databases. Results provide insights about the best combination of countries for acquiring raw materials to manufacture NiZn and for disposing of the waste of NiZn batteries that cannot be recycled. These results were automatically linked to some sustainability KPIs, such as global warming and capital costs, being replicable in case of data updates or changes in production or recycling locations, which were initially considered at Paris (France) and Krefeld (Germany), respectively. These results provided by an AI model were validated by using a sensitivity analysis and the Analytical Hierarchy Process (AHP) through an expert panel. The sensitivity analysis ensures the robustness of mathematical parameters and future variations in the market; on the other hand, the AHP validates the Artificial Intelligence (AI) results with interactions of human factors. Further developments should also consider the manufacturing and use phases in the optimization model.

Keywords: LCCA; LCA; MOPSO; genetic algorithms; AHP; sustainability KPIs; AI; NiZn batteries (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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