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To address the issue of soil-borne pathogens accumulating in crop stubble, this study conducted combustion experiments on high-moisture, soil-attached wheat stubble to provide a technical basis for in-field harmless disposal of residues and the prevention of soil-borne diseases. Thermogravimetric analysis (TGA) and a self-designed dual-grate reverse combustion system were employed to investigate the effects of excess air ratio (λ), moisture content (MC), and rhizospheric soil content (RSC) on combustion characteristics and pollutant emissions. A response surface model was developed using the Box-Behnken design, and the statistical importance of each is assessed by means of the analysis of variance (ANOVA), and the models are expressed in quadratic polynomial forms. A comprehensive comparison was conducted among the improved constrained multi-objective particle swarm optimization (CMOPSO), non-dominated sorting genetic algorithm II (NSGA-II), genetic algorithm (GA), and particle swarm optimization (PSO). The NSGA-II algorithm demonstrated superior performance in terms of Pareto front diversity, combustion efficiency, and control of NOx and particulate matter (PM) emissions. Based on this, multi-objective optimization was performed using NSGA-II, yielding the optimal operating conditions (λ = 1.64, MC = 8.54 %, RSC = 3.09 %), which achieved a combustion efficiency of 85.43 % while maintaining NOx and PM emissions at 99.25 mg/m3 and 184.31 mg/m3, respectively, providing valuable guidance for sustainable energy utilization and environmental management.
Li et al. (Mon,) studied this question.
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