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Article(id=1325193048115773989, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325193025059692970, articleNumber=null, orderNo=null, doi=10.12398/j.issn.2096-7217.2025.01.005, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1709481600000, receivedDateStr=2024-03-04, revisedDate=1718726400000, revisedDateStr=2024-06-19, acceptedDate=null, acceptedDateStr=null, onlineDate=1793880243566, onlineDateStr=2026-11-05, pubDate=1739548800000, pubDateStr=2025-02-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793880243566, onlineIssueDateStr=2026-11-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793880243566, creator=13701087609, updateTime=1793880243566, updator=13701087609, issue=Issue{id=1325193025059692970, tenantId=1146029695717560320, journalId=1319690209941618849, year='2025', volume='6', issue='1', pageStart='1', pageEnd='110', issueExtLink='null', onlineDate='null', pubDate='1739548800000', pubDateStr='2025-02-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793880238070, creator='13701087609', updateTime=1793943677543, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1325459110036112192, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325193025059692970, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1325459110036112193, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325193025059692970, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=51, endPage=58, ext={EN=ArticleExt(id=1325193048308711974, articleId=1325193048115773989, tenantId=1146029695717560320, journalId=1319690209941618849, language=EN, title=Optimization of coffee shell carbonization parameters based on BP neural network-genetic algorithm, columnId=null, journalTitle=Journal of Intelligent Agricultural Mechanization, columnName=null, runingTitle=null, highlight=null, articleAbstract=

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.

, authors=Xia ZHANG1, 2, Panjie SU1, 2, Jingzhe ZHU1, 2, Yiyang WANG1, 2, Junwei HUANG1, 2, authorsList=Xia ZHANG, Panjie SU, Jingzhe ZHU, Yiyang WANG, Junwei HUANG, authorCompany=null, correspAuthors=Junwei HUANG, authorNote=

ZHANG Xia, E-mail:

, correspAuthorsNote=
* HUANG Junwei, E-mail:
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生物炭是一种针对生物质能高效开发的多功能材料，随着对生物质能高效开发的关注，生物炭的应用范围逐渐扩展，其中炭基肥作为生物炭的一个重要应用方向，因其优良的缓释性能和对土壤负担小的特点，受到广泛关注。生物炭的理化性质受到制备过程中的炭化温度、炭化时间和升温速率等工艺参数的显著影响，不同炭化工艺不仅决定了生物炭的理化性质，还直接影响其作为炭基肥的缓释性能。传统的实验方法往往需要大量的时间和资源投入，因此，探索更加高效的优化方法成为了研究的热点。本研究采用了BP神经网络与遗传算法相结合的优化方法，针对咖啡壳生物炭的炭化过程中的炭化温度、炭化时间和升温速率3个关键工艺参数进行预测和优化。研究结果表明，采用BP神经网络—遗传算法优化后的炭基肥，其最佳工艺参数为炭化时间2.8 h、炭化温度780.7 °C和升温速率15.1 °C/min。在此工艺条件下制备的咖啡壳生物炭基肥，其7 d养分累计释放率为45.9%，表明缓释性能得到了显著提升。综上所述，本研究提出了一种基于BP神经网络和遗传算法的生物炭炭化工艺参数优化方法，能够有效提高炭基肥的缓释性能。该方法不仅为生物炭制备工艺的优化提供了新的技术路径，也为相关领域的研究提供了重要参考，对推动高性能炭基肥的发展具有积极意义。

, authors=张霞1, 2, 苏盼杰1, 2, 朱静哲1, 2, 王伊洋1, 2, 黄峻伟1, 2, *, *, authorsList=张霞, 苏盼杰, 朱静哲, 王伊洋, 黄峻伟, authorCompany=null, correspAuthors=黄峻伟, authorNote=

张霞，女，1972年生，云南蒙自人，硕士，教授；研究方向为生物质能源。E-mail：

, correspAuthorsNote=
* 黄峻伟，男，1983年生，吉林松原人，博士，副教授；研究方向为生物质能源。E-mail：
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ZHANG Xia, E-mail:

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ZHANG Xia, E-mail:

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张霞，女，1972年生，云南蒙自人，硕士，教授；研究方向为生物质能源。E-mail：

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张霞，女，1972年生，云南蒙自人，硕士，教授；研究方向为生物质能源。E-mail：

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基于BP神经网络—遗传算法的咖啡壳炭化工艺参数优化
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张霞 1, 2 , 苏盼杰 1, 2 , 朱静哲 1, 2 , 王伊洋 1, 2 , 黄峻伟 1, 2, *, *
智能化农业装备学报（中英文） | 2025,6(1): 51-58
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智能化农业装备学报（中英文） | 2025 , 6 (1) : 51 -58
基于BP神经网络—遗传算法的咖啡壳炭化工艺参数优化
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张霞1, 2 , 苏盼杰1, 2, 朱静哲1, 2, 王伊洋1, 2, 黄峻伟1, 2, *, *
作者信息
  • 1.云南农业大学机电工程学院，云南 昆明，650500
  • 2.云南省教育厅高原特色现代农业节能减碳重点实验室，云南 昆明，650201
通讯作者:
* 黄峻伟，男，1983年生，吉林松原人，博士，副教授；研究方向为生物质能源。E-mail：
作者简介:

张霞，女，1972年生，云南蒙自人，硕士，教授；研究方向为生物质能源。E-mail：

Optimization of coffee shell carbonization parameters based on BP neural network-genetic algorithm
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
    • Corresponding Author:
    * HUANG Junwei, E-mail:

    ZHANG Xia, E-mail:

出版时间: 2025-02-15 doi: 10.12398/j.issn.2096-7217.2025.01.005
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摘要
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生物炭是一种针对生物质能高效开发的多功能材料，随着对生物质能高效开发的关注，生物炭的应用范围逐渐扩展，其中炭基肥作为生物炭的一个重要应用方向，因其优良的缓释性能和对土壤负担小的特点，受到广泛关注。生物炭的理化性质受到制备过程中的炭化温度、炭化时间和升温速率等工艺参数的显著影响，不同炭化工艺不仅决定了生物炭的理化性质，还直接影响其作为炭基肥的缓释性能。传统的实验方法往往需要大量的时间和资源投入，因此，探索更加高效的优化方法成为了研究的热点。本研究采用了BP神经网络与遗传算法相结合的优化方法，针对咖啡壳生物炭的炭化过程中的炭化温度、炭化时间和升温速率3个关键工艺参数进行预测和优化。研究结果表明，采用BP神经网络—遗传算法优化后的炭基肥，其最佳工艺参数为炭化时间2.8 h、炭化温度780.7 °C和升温速率15.1 °C/min。在此工艺条件下制备的咖啡壳生物炭基肥，其7 d养分累计释放率为45.9%，表明缓释性能得到了显著提升。综上所述，本研究提出了一种基于BP神经网络和遗传算法的生物炭炭化工艺参数优化方法，能够有效提高炭基肥的缓释性能。该方法不仅为生物炭制备工艺的优化提供了新的技术路径，也为相关领域的研究提供了重要参考，对推动高性能炭基肥的发展具有积极意义。

生物炭  /  BP神经网络  /  遗传算法  /  炭基肥  /  工艺参数优化

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
基金
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  • 国家自然科学基金(52166010; 51706195)
  • 云南省基础研究计划面上项目(202101AT070202)
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doi: 10.12398/j.issn.2096-7217.2025.01.005
  • 接收时间：2024-03-04
  • 首发时间：2026-11-05
  • 出版时间：2025-02-15
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  • 收稿日期：2024-03-04
  • 修回日期：2024-06-19
基金
National Natural Science Foundation of China(52166010; 51706195)
国家自然科学基金(52166010; 51706195)
Yunnan Provincial Project Fund(202101AT070202)
云南省基础研究计划面上项目(202101AT070202)
作者信息
    1.云南农业大学机电工程学院，云南 昆明，650500
    2.云南省教育厅高原特色现代农业节能减碳重点实验室，云南 昆明，650201

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* 黄峻伟，男，1983年生，吉林松原人，博士，副教授；研究方向为生物质能源。E-mail：
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2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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