Work

Multi-objective optimization of key reaction parameters over La-Ni based perovskite catalyzed CO2 biomass gasification based on response surface methodology and an integrated strategy

Year: 2025

Type: article

Abstract: CO 2 biomass catalytic gasification is a promising sustainable energy technology with significant potential for reducing global carbon emissions. This study investigated the multi-objective optimization of modified La-Ni based perovskite catalysts for CO 2 coconut shell gasification. An integrated optimization strategy combining Response Surface Methodology (RSM), Non-dominated Sorting Genetic Algorithm II (NSGA-II), K-means clustering, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to identify optimal gasification decisions. RSM was used to analyze complex interactions among key parameters (Temperature, flow rate, CO 2 concentration, and catalyst loading) and establish predictive models, while NSGA-II generated 85 Pareto-optimal solutions. K-means clustering was employed to grouped the solutions into 5 groups based on features to preserve decision diversity, and TOPSIS ranked them according to their closeness to an ideal solution. Under the optimal gasification conditions, CO yields ranged from 1514.93 mL/g to 2200.56 mL/g, H 2 yields from 433.18 mL/g to 479.77 mL/g, and carbon conversion rates from 91.08 % to 96.08 %, exceeding common benchmarks in similar studies. The integrated strategy improves solution diversity, interpretability, and decision-making accuracy compared to traditional single algorithm approaches. This work demonstrates both the optimal catalytic efficiency of the synthesized perovskite and the application of integrated optimization strategy in advancing CO 2 biomass gasification.

Source: Biomass and Bioenergy

DOI: 10.1016/j.biombioe.2025.108505