Jiaojiao Zhang, Minghang Lv, Fuli Li, Zhe Wang, Xinyu Liu
{"title":"Research on the co-pyrolysis of biomass and coal","authors":"Jiaojiao Zhang, Minghang Lv, Fuli Li, Zhe Wang, Xinyu Liu","doi":"10.1088/1742-6596/2838/1/012019","DOIUrl":null,"url":null,"abstract":"Co-pyrolysis of biomass and coal has attracted much attention as a potential energy conversion technology. In this paper, the significant effects of n-hexane insoluble substance (INS) on tar, water, and coke residue yields were analyzed in depth using statistical analysis. With INS and mixing ratio as independent variables, multivariate analysis of variance (ANOVA) was used to comprehensively investigate the effects of the interaction between the two on the yields of different pyrolysis products, revealing the significant interaction between different pyrolysis products. The genetic algorithm was used to establish an optimization model to optimize the mixing ratios of the co-pyrolysis, and the considerable differences between the experimental and theoretically calculated values of the product yields of the co-pyrolysis compounds were analyzed by statistical tests, and subgroup analyses were carried out to determine the specific differences under different mixing ratios. CNN prediction model was established, and model optimization confirmed the differences between the experimental and theoretically calculated values for different pyrolytic coupling ratio-specific ratios by data validation and subgroup analysis. The further extension and optimization of the model provide a new theoretical basis and idea for further research on the co-pyrolysis of biomass and coal, which can help to improve the energy conversion efficiency and product quality.","PeriodicalId":16821,"journal":{"name":"Journal of Physics: Conference Series","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Physics: Conference Series","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/1742-6596/2838/1/012019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Co-pyrolysis of biomass and coal has attracted much attention as a potential energy conversion technology. In this paper, the significant effects of n-hexane insoluble substance (INS) on tar, water, and coke residue yields were analyzed in depth using statistical analysis. With INS and mixing ratio as independent variables, multivariate analysis of variance (ANOVA) was used to comprehensively investigate the effects of the interaction between the two on the yields of different pyrolysis products, revealing the significant interaction between different pyrolysis products. The genetic algorithm was used to establish an optimization model to optimize the mixing ratios of the co-pyrolysis, and the considerable differences between the experimental and theoretically calculated values of the product yields of the co-pyrolysis compounds were analyzed by statistical tests, and subgroup analyses were carried out to determine the specific differences under different mixing ratios. CNN prediction model was established, and model optimization confirmed the differences between the experimental and theoretically calculated values for different pyrolytic coupling ratio-specific ratios by data validation and subgroup analysis. The further extension and optimization of the model provide a new theoretical basis and idea for further research on the co-pyrolysis of biomass and coal, which can help to improve the energy conversion efficiency and product quality.
生物质和煤的联合热解作为一种潜在的能源转换技术备受关注。本文采用统计分析方法深入分析了正己烷不溶物(INS)对焦油、水和焦炭残渣产量的显著影响。以 INS 和混合比为自变量,采用多元方差分析(ANOVA)全面考察了两者之间的交互作用对不同热解产物产率的影响,揭示了不同热解产物之间的显著交互作用。利用遗传算法建立了优化模型,对共热解的混合比进行了优化,通过统计检验分析了共热解化合物产物产率的实验值与理论计算值之间存在的较大差异,并进行了分组分析,确定了不同混合比下的具体差异。建立了 CNN 预测模型,并通过数据验证和分组分析对模型进行了优化,确认了不同热解耦合比特定比例下实验值和理论计算值之间的差异。该模型的进一步扩展和优化为生物质与煤协同热解的进一步研究提供了新的理论依据和思路,有助于提高能源转化效率和产品质量。