智能化农业装备学报（中英文）2025，Vol.6Issue(1)：51-58,8.DOI:10.12398/j.issn.2096-7217.2025.01.005
基于BP神经网络—遗传算法的咖啡壳炭化工艺参数优化
Optimization of coffee shell carbonization parameters based on BP neural network-genetic algorithm
摘要
Abstract
Biochar is a multifunctional material efficiently developed from biomass.It is combined with fertilizers to prepare biochar-based fertilizers,which has excellent slow-release performance and minimal soil burden.The carbonization temperature,carbonization time,and heating rate during biomass carbonization process affect the physical and chemical properties of biochar.The biochar under different carbonization temperatures,carbonization times and heating rates have a significant impact on the slow-release performance of biochar-based fertilizers.In this study,BP neural network coupled with genetic algorithm was used to predict and optimize key process parameters during the carbonization process of coffee shell biochar in order to improve the slow-release performance of biochar-based fertilizers.Results had shown that based on BP neural network-genetic algorithm,rapid prediction and optimization of the slow-release performance of coffee shell biochar-based fertilizer had been achieved through experiments.The optimal process parameters were:carbonization time of 2.8 h,carbonization temperature of 780.7℃,and the heating rate of 15.1℃/min.The seven-day cumulative nutrient release rate of the biochar-based fertilizer prepared under this process parameter was 45.9%,and the slow-release performance was improved.The study proposed a new method for optimizing the parameters of biochar carbonization process,which provided new ideas for the development of high-performance biochar preparation processes and had certain reference significance for improving the performance of biochar-based fertilizers.关键词
生物炭/BP神经网络/遗传算法/炭基肥/工艺参数优化Key words
biochar/BP neural network/genetic algorithm/biochar-based fertilizer/parameter optimization分类
农业科技引用本文复制引用
张霞,苏盼杰,朱静哲,王伊洋,黄峻伟..基于BP神经网络—遗传算法的咖啡壳炭化工艺参数优化[J].智能化农业装备学报（中英文）,2025,6(1):51-58,8.基金项目
国家自然科学基金(52166010,51706195) （52166010,51706195）
云南省基础研究计划面上项目(202101AT070202) National Natural Science Foundation of China(52166010,51706195) （202101AT070202）
Yunnan Provincial Project Fund(202101AT070202) （202101AT070202）
