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Optimization of coffee shell carbonization parameters based on BP neural network-genetic algorithm
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Xia ZHANG1, 2, Panjie SU1, 2, Jingzhe ZHU1, 2, Yiyang WANG1, 2, Junwei HUANG1, 2
Journal of Intelligent Agricultural Mechanization | 2025, 6(1) : 51 - 58
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Journal of Intelligent Agricultural Mechanization | 2025, 6(1): 51-58
Optimization of coffee shell carbonization parameters based on BP neural network-genetic algorithm
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Xia ZHANG1, 2, Panjie SU1, 2, Jingzhe ZHU1, 2, Yiyang WANG1, 2, Junwei HUANG1, 2
Affiliations
  • 1.Yunnan Agricultural University, Kunming 650500, China
  • 2.Key Laboratory of Plateau Characteristic Modern Agriculture Energy Conservation and Carbon Reduction, Education Department of Yunnan Province, Kunming 650201, China

    • ZHANG Xia, E-mail:

About Author:

ZHANG Xia, E-mail:

Published: 2025-02-15 doi: 10.12398/j.issn.2096-7217.2025.01.005
Outline
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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.

biochar  /  BP neural network  /  genetic algorithm  /  biochar-based fertilizer  /  parameter optimization
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. 智能化农业装备学报（中英文）, 2025 , 6 (1) : 51 -58 . DOI: 10.12398/j.issn.2096-7217.2025.01.005
Xia ZHANG, Panjie SU, Jingzhe ZHU, Yiyang WANG, Junwei HUANG. Optimization of coffee shell carbonization parameters based on BP neural network-genetic algorithm[J]. Journal of Intelligent Agricultural Mechanization, 2025 , 6 (1) : 51 -58 . DOI: 10.12398/j.issn.2096-7217.2025.01.005
Funding
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  • National Natural Science Foundation of China(52166010; 51706195)
  • Yunnan Provincial Project Fund(202101AT070202)
Appendix
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Year 2025 volume 6 Issue 1
37
10
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Article Info
doi: 10.12398/j.issn.2096-7217.2025.01.005
  • Receive Date：2024-03-04
  • Online Date：2026-11-05
  • Published：2025-02-15
Article Data
Affiliations
History
  • Received：2024-03-04
  • Revised：2024-06-19
Funding
National Natural Science Foundation of China(52166010; 51706195)
Yunnan Provincial Project Fund(202101AT070202)
Affiliations
    1.Yunnan Agricultural University, Kunming 650500, China
    2.Key Laboratory of Plateau Characteristic Modern Agriculture Energy Conservation and Carbon Reduction, Education Department of Yunnan Province, Kunming 650201, China

Corresponding:

* HUANG Junwei, E-mail:
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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