基于生物质中正己烷不溶物与煤共热解技术优化研究  

Research on the Optimization of Co-pyrolysis Technology of n-hexane Insoluble in Biomass and Coal

作　　者：吴永丽 孙国伟 杨忠萍 杨燕 于鑫 付妙妙 WU Yongli;SUN Guowei;YANG Zhongping;YANG Yan;YU Xin;FU Miaomiao(School of Chemical Engineering,Ordos Institute of Applied Technology,Ordos 017000,China;School of Finance and Taxation,Inner Mongolia University of Finance and Economics,Hohhot 010051,China;School of Accounting,Inner Mongolia University of Finance and Economics,Hohhot 010051,China;School of Mechanical and Automotive Engineering,Hengxing University,Qingdao 266100,China)

机构地区：[1]鄂尔多斯应用技术学院化学工程系,内蒙古鄂尔多斯017000 [2]内蒙古财经大学财政税务学院,内蒙古呼和浩特010051 [3]内蒙古财经大学会计学院,内蒙古呼和浩特010051 [4]青岛恒星科技学院机械与汽车工程学院,山东青岛266100

出　　处：《山东化工》2025年第1期27-30,38,共5页Shandong Chemical Industry

基　　金：2024年大学生创新创业训练计划国家级立项(编号:G202414532001)。

摘　　要：随着全球能源需求的增长和对可再生能源的追求,生物质与煤炭共热解作为一种潜在的能量转换技术备受关注。本文在数据分析的基础上对生物质与煤炭共热解过程中的关键因素分析及优化进行了研究,使用MATLAB进行数据预处理,包括检查和填充缺失值,并使用箱形图识别和消除三个极端异常值。对清洗后的数据进行描述性统计分析,以了解其基本特征。接下来,建立非线性回归模型,分析生物质中正己烷不溶物(INS)对焦油、水和焦渣收率的影响,确保结果准确。同时研究INS与混合比之间的关系,使用方差分析确定其交互作用效应。为了最大限度地提高产品利用率和能量转换效率,应用SLS QP算法对混合比进行优化。进一步,通过Kruskal-Wallis H检验和Tukey HSD检验分析实验值与理论值的差异,确保实验数据的可靠性。构建预测模型,采用线性回归、随机森林、支持向量机等方法进行预测,并采用加权优化模型和粒子群算法求解,以将预测误差降至最低。通过对这些关键因素的系统研究,本文全面深入地了解了正己烷与煤炭共热解技术的影响因素及其优化策略,推动了这一潜在能量转换技术的发展和应用。With the growth of global energy demand and the pursuit of renewable energy,co-pyrolysis of biomass and coal as a potential energy conversion technology has attracted widespread attention.This paper studies and optimizes the key factors in the co-pyrolysis process of biomass and coal based on data analysis.MATLAB was used for data preprocessing,including checking and filling missing values,and using box plots to identify and eliminate three extreme outliers.Descriptive statistical analysis was performed on the cleaned data to understand its basic characteristics.Subsequently,a nonlinear regression model was established to analyze the impact of n-hexane insoluble in biomass(INS)on the yields of tar,water,and char,ensuring the accuracy of the results.The relationship between INS and mixing ratio was also studied,and the interaction effect was determined using analysis of variance.To maximize product utilization and energy conversion efficiency,the SLS QP algorithm was applied to optimize the mixing ratio.Furthermore,the differences between experimental and theoretical values were analyzed using the Kruskal-Wallis H test and Tukey HSD test to ensure the reliability of the experimental data.Predictive models were constructed using linear regression,random forests,and support vector machines,and a weighted optimization model and particle swarm algorithm were used to solve for the minimum prediction error.Through systematic research on these key factors,this paper comprehensively and deeply understands the influencing factors and optimization strategies of n-hexane co-pyrolysis technology with coal,promoting the development and application of this potential energy conversion technology.

关 键 词：正己烷热解 煤共热解 数据清洗 非线性回归 交互效应 

分 类 号：TQ015.9[化学工程]