基于机器学习的空气源热泵干燥能耗回归预测  

Regression prediction of the energy consumption of air source heat pump drying based on machine learning

作　　者：杨仕[1,2] 陈维汉 杨明金 张原[1] 李守太 蒲应俊 杨玲 宋卫东 YANG Shi;CHEN Weihan;YANG Mingjin;ZHANG Yuan;LI Shoutai;PU Yingjun;YANG Ling;SONG Weidong(College of Engineering and Technology,Southwest University,Chongqing 400715,China;Chongqing Key Laboratory of Agricultural Equipment for Hilly and Mountainous Regions,Chongqing 400715,China;Nanjing Institute of Agricultural Mechanization,Ministry of Agricultural and Rural Affairs,Nanjing 210014,China)

机构地区：[1]西南大学工程技术学院,重庆400715 [2]丘陵山区农业装备重庆市重点实验室,重庆400715 [3]农业农村部南京农业机械化研究所,南京210014

出　　处：《农业工程学报》2024年第2期41-51,共11页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(31301575);农业农村部现代农业装备重点实验室开放基金项目(HT20200705);重庆市教育委员会项目(KJCX2020002)。

摘　　要：为了降低空气源热泵干燥过程能耗,研究了空气源热泵干燥能耗特性,采用多元线性回归模型(multivariate linear regression model, MLRM)和BP神经网络(back propagation neural network, BPNN)模型来预测干燥工艺能耗。在分析干燥能耗影响特征参数的基础上,提出将干燥工艺过程进行切分处理的方法以降低数据获取难度。选取烘房设定温度、烘房设定湿度、烘房初始温度、烘房初始湿度、环境平均温度、环境平均湿度、物料质量和初始含水率8个特征参数作为模型输入,能耗和物料结束含水率作为模型输出。使用MLRM模型、BPNN模型和其他机器学习模型进行能耗预测,MLRM模型对能耗拟合的决定系数为0.739,对物料结束含水率拟合的决定系数为0.931;BPNN模型使用Sigmoid函数作为激活函数时对能耗拟合的决定系数最高,为0.828,使用Identity函数作为激活函数时对物料结束含水率拟合的决定系数最高,为0.942,拟合效果优于其他机器学习模型,能够满足实际生产需求。以复水豌豆为干燥对象设计加载物料65 kg、持续时间4 h的完整变温变湿干燥工艺进行验证试验,结果表明:试验总能耗为15.066 kW·h,MLRM模型和BPNN模型的预测总能耗分别为14.476 kW·h、15.183 kW·h,预测精度分别为96.08%、99.23%;试验结束含水率为8.541%,MLRM模型和BPNN模型的预测结束含水率分别为9.560%、8.889%,预测精度分别为88.07%、95.93%。该研究提出了一种使用MLRM模型和BPNN模型对空气源热泵干燥能耗进行分段精准预测的有效手段,对于优化干燥工艺和降低干燥能耗具有实际意义。In order to reduce the energy consumption of air source heat pump drying,a prediction method of energy consumption was proposed to optimize the drying process by means of multivariate linear regression model(MLRM)and back propagation neural network(BPNN)model.On the basis of analyzing characteristics of the energy consumption and factors that affect it,the drying process was proposed to split into sections of equal time to reduce the difficulty of data acquisition.Eight characteristic parameters were set as input,and two parameters were set as output.Input parameters were set temperature in drying room,set humidity in drying room,initial temperature in drying room,initial humidity in drying room,mean ambient temperature,mean ambient humidity,weight of material,and initial moisture content of material.Output parameters were energy consumption and end moisture content of material.Before build predictive models,the variation of the drying power of the air source heat pump was analysed.From the air source heat pump control principle and the drying power curve,the drying process was mainly divided into a heating stage and a insulation stage.The insulation stage consisted of a number of circulating insulation units.As the weight of pea increased,the duration of the heating stage increased,the duration of the insulation stage shortened.Meanwhile,the time of the insulation unit increased and the total drying time increased.Orthogonal experiment was designed using unit energy consumption(energy consumption required to reduce the moisture content of 1 kg of material by 1%)and total energy consumption as the evaluation indexes.According to the results of orthogonal experiment,among the three influencing factors of weight of pea,set temperature in drying room,and set humidity in drying room,the weight of pea had the most significant influence on unit energy consumption and total energy consumption,and the set humidity in drying room had slight significant effect on the total energy consumption.Based on characterization of en

关 键 词：热泵干燥 能耗模型 回归预测 机器学习 工艺切分 

分 类 号：S226.6[农业科学—农业机械化工程]