基于人工神经网络的苜蓿固定深层太阳能干燥过程仿真
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摘要
鉴于苜蓿自然干燥营养成分损失大、产品质量差,常规能源干燥成本高且污染环境,对苜蓿进行了自然天气条件下的固定深层太阳能干燥试验。由试验结果分析可知,苜蓿固定深层太阳能干燥过程是一个高度复杂、非线性的过程,为实现干燥过程监测管理的智能化与自动化,有必要解决干燥过程中苜蓿湿含量的预测问题。人工神经网络具有良好的非线性映射能力和高度的并行信息处理能力,是解决非线性系统辨识的良好途径。为此,论文提出将神经网络应用于非线性太阳能干燥建模中。构建网络模型时,将苜蓿固定深层太阳能干燥系统划分为集热器热性能仿真系统、苜蓿表面温度仿真系统和苜蓿湿含量预测系统等3个子系统;建立了各子系统的网络模型及太阳能干燥系统的整体模型。主要结论如下:
(1)对自然天气条件下的苜蓿固定深层太阳能干燥试验研究结果表明,太阳能空气集热器出口气流温度随辐射强度的增强而升高,且集热器出口气流温度及温升的变化滞后于太阳辐射变化,即存在延时现象;集热器内气流速度对集热器温升的影响受气候条件的制约。集热器出口气流温度的回归分析结果显示,很难获得具有普遍意义的集热器出口气流温度与其影响因素间的通用回归方程。
(2)苜蓿固定深层太阳能干燥过程呈现高度非线性,草层内部不同部位苜蓿的干燥速率不同;苜蓿深层干燥过程中,沿气流方向和垂直于气流方向的截面上都存在温度梯度。温度梯度的大小、正负和变化趋势以及废气温度、废气相对湿度和介质温度、介质相对湿度间的关系均能够反映干燥进程。
(3)建立基于人工神经网络的集热器热性能仿真模型时,将对空气集热器热性能有影响的太阳高度角、入射角和时角间的关系,统一用时角余弦cosω代替,称其为时间因子;时间因子的引入简化了网络模型结构;试验验证结果表明,考虑时间因素cosω和太阳赤纬δ的太阳能空气集热器热性能神经网络模型的预测误差小、训练速度快;对测试样本的验证结果属于高度拟合、满意预测;建立了不考虑空间信息和考虑空间信息的太阳能空气集热器出口气流温度仿真模型和空气集热器效率仿真模型,验证结果表明,各网络仿真结果均为高精度拟合、满意预测或良好预测。
(4)在考虑了干燥过程中苜蓿湿含量是连续变化量、与初始湿含量和干燥时间有关等特点的基础上,将时间序列因子引入输入因子,建立基于人工神经网络的苜蓿表面温度仿真模型。验证结果表明,时间序列因子的引入丰富了网络样本的多样性、提高了苜蓿表面温度仿真模型的预测性能,降低了网络输出误差;建立了基于人工神经网络的含一维、二维和三维空间信息的单层和单日多层、多日多层苜蓿表面温度仿真模型,预测效果均属于高度拟合满意预测,均等系数EC值大于0.97,平均绝对百分误差mape值小于5.4% ;建立了苜蓿表面温度梯度的人工神经网络仿真模型,模型对测试样本的仿真结果EC值0.97以上,mape小于6%。建立沿气流方向的温度梯度仿真模型,对固定深层苜蓿太阳能干燥过程研究和管理意义深远,有助于在线预测牧草干燥过程的进程,对干燥工艺的合理匹配提供参考依据。
(5)用单层、多层和多日试验数据建立了基于神经网络的牧草湿含量仿真模型,网络性能达到较好拟合和良好预测。模型对测试样本的仿真结果EC值大于0.90,mape值在25～34%之间,属于可行预测。苜蓿湿含量多日连续模型性能良好,对测试样本的仿真结果EC值大于0.93,mape值小于25%,属于良好预测。
(6)建立了以太阳能集热器热性能仿真模型、苜蓿表面温度仿真模型及苜蓿湿含量预测模型为3个子模块的自然天气条件下的苜蓿固定深层太阳能干燥系统的仿真模型。该模型的特点为:可以对不同季节、不同时刻的太阳能苜蓿干燥系统进行性能分析和在线预测。验证结果表明,网络预测效果良好,能够较真实地反映太阳能苜蓿的动态干燥过程。
In view of the great nutrition lost and bad quality of nature drying clover products, and the high drying cost of general energy and the energy wasting and polluting environment problems, etc., the solar drying experiments of alfalfa in deep bed under real time weather condition were conducted. The traditional process modeling method is not fit to the solar drying process of alfalfa in deep bed. Since the solar drying process of alfalfa in deep bed has the characters of complicated, nonlinear and random, etc., the artificial neural networks technology is used.
The solar drying process of alfalfa can be divided into three processes.
After analyzing the three processes, the simulation model of every process was determined. The neural networks models were established and were validated. And six conclusions were made from the study.
(1) The temperature changing of the solar collector was influenced by the solar radicalization, surroundings temperature and humidity. From the regression analysis result of the solar collector temperature and the factors, it showed that the correlation coefficients of different experiments data were very different. The corresponding coefficients of the three regression equations had positive ones and negative ones. So the general equation was not able to find out.
(2) The solar drying process of alfalfa in deep bed has the character of great nonlinear: the drying speeds of different parts alfalfa were not the same, and the temperatures of different locations in the same section were not the same, the temperature of the middle part alfalfa was lower than any other part temperature all the time, the drying speed of deep layer alfalfa was low. To study on the magnitude, positive or negative and the changing trend of the alfalfa surface temperature grads helped to forecast the process of drying. That the temperature difference value between exhaust air and medium and the relative humidity difference value between exhaust air and medium could show the situation of mow humidity.
(3) The factors of time and the sun angle were considered in the heat characteristic simulation model of the solar air collector. The result showed that the performance of the model had improved. Temperature simulation models and the efficiency simulation models of the solar air collector were established by considering space information or not. The simulation results showed that the models could well forecast the process.
(4) The time sequence factor was considered in the alfalfa surface temperature simulation model. Not only the multiformity of the network swatches was enriched, but also the network performance was improved. The different neural network simulation models for the alfalfa surface temperature were established by considering monolayer with single dimension information, two-dimensional information and three-dimensional information, and all these information of multilayer for one day and many days. The processes were precisely forecasted: EC is above 0.97 and mape is below 5.4%. The grads neural network simulation model of the alfalfa surface temperature was established,and its result for the testing sample was also quite well: EC is above 0.97 and mape is below 6%. The temperature grads simulation model of the inside layer alfalfa along the airflow direction was established. This was important to the study and management of the deep bed alfalfa drying process, and helped to forecast the process of drying in real time. This could also be referenced as optimizing the drying process.
(5) The alfalfa moisture content simulation models for different experiments data of monolayer, multilayer and many days were established based on neural networks. The models imitated well and forecasted well.
(6) The solar drying system simulation model of the alfalfa in Deep Bed ,including the heat characteristic simulation models of the solar air collector, alfalfa surface temperature simulation models and the alfalfa moisture content simulation models ,was established. The simulation results of the models were all well. And the real time process of the alfalfa drying could be showed well.
To sum up, the simulation of solar drying model of alfalfa in Deep Bed was established based on artificial neural networks. The models could well forecast the alfalfa moisture content of different parts. The establishment of the alfalfa moisture content simulation model was very useful to the reality. This could forecast the real time alfalfa humidity and direct the real producing.
(1)对自然天气条件下的苜蓿固定深层太阳能干燥试验研究结果表明,太阳能空气集热器出口气流温度随辐射强度的增强而升高,且集热器出口气流温度及温升的变化滞后于太阳辐射变化,即存在延时现象;集热器内气流速度对集热器温升的影响受气候条件的制约。集热器出口气流温度的回归分析结果显示,很难获得具有普遍意义的集热器出口气流温度与其影响因素间的通用回归方程。
(2)苜蓿固定深层太阳能干燥过程呈现高度非线性,草层内部不同部位苜蓿的干燥速率不同;苜蓿深层干燥过程中,沿气流方向和垂直于气流方向的截面上都存在温度梯度。温度梯度的大小、正负和变化趋势以及废气温度、废气相对湿度和介质温度、介质相对湿度间的关系均能够反映干燥进程。
(3)建立基于人工神经网络的集热器热性能仿真模型时,将对空气集热器热性能有影响的太阳高度角、入射角和时角间的关系,统一用时角余弦cosω代替,称其为时间因子;时间因子的引入简化了网络模型结构;试验验证结果表明,考虑时间因素cosω和太阳赤纬δ的太阳能空气集热器热性能神经网络模型的预测误差小、训练速度快;对测试样本的验证结果属于高度拟合、满意预测;建立了不考虑空间信息和考虑空间信息的太阳能空气集热器出口气流温度仿真模型和空气集热器效率仿真模型,验证结果表明,各网络仿真结果均为高精度拟合、满意预测或良好预测。
(4)在考虑了干燥过程中苜蓿湿含量是连续变化量、与初始湿含量和干燥时间有关等特点的基础上,将时间序列因子引入输入因子,建立基于人工神经网络的苜蓿表面温度仿真模型。验证结果表明,时间序列因子的引入丰富了网络样本的多样性、提高了苜蓿表面温度仿真模型的预测性能,降低了网络输出误差;建立了基于人工神经网络的含一维、二维和三维空间信息的单层和单日多层、多日多层苜蓿表面温度仿真模型,预测效果均属于高度拟合满意预测,均等系数EC值大于0.97,平均绝对百分误差mape值小于5.4% ;建立了苜蓿表面温度梯度的人工神经网络仿真模型,模型对测试样本的仿真结果EC值0.97以上,mape小于6%。建立沿气流方向的温度梯度仿真模型,对固定深层苜蓿太阳能干燥过程研究和管理意义深远,有助于在线预测牧草干燥过程的进程,对干燥工艺的合理匹配提供参考依据。
(5)用单层、多层和多日试验数据建立了基于神经网络的牧草湿含量仿真模型,网络性能达到较好拟合和良好预测。模型对测试样本的仿真结果EC值大于0.90,mape值在25～34%之间,属于可行预测。苜蓿湿含量多日连续模型性能良好,对测试样本的仿真结果EC值大于0.93,mape值小于25%,属于良好预测。
(6)建立了以太阳能集热器热性能仿真模型、苜蓿表面温度仿真模型及苜蓿湿含量预测模型为3个子模块的自然天气条件下的苜蓿固定深层太阳能干燥系统的仿真模型。该模型的特点为:可以对不同季节、不同时刻的太阳能苜蓿干燥系统进行性能分析和在线预测。验证结果表明,网络预测效果良好,能够较真实地反映太阳能苜蓿的动态干燥过程。
In view of the great nutrition lost and bad quality of nature drying clover products, and the high drying cost of general energy and the energy wasting and polluting environment problems, etc., the solar drying experiments of alfalfa in deep bed under real time weather condition were conducted. The traditional process modeling method is not fit to the solar drying process of alfalfa in deep bed. Since the solar drying process of alfalfa in deep bed has the characters of complicated, nonlinear and random, etc., the artificial neural networks technology is used.
The solar drying process of alfalfa can be divided into three processes.
After analyzing the three processes, the simulation model of every process was determined. The neural networks models were established and were validated. And six conclusions were made from the study.
(1) The temperature changing of the solar collector was influenced by the solar radicalization, surroundings temperature and humidity. From the regression analysis result of the solar collector temperature and the factors, it showed that the correlation coefficients of different experiments data were very different. The corresponding coefficients of the three regression equations had positive ones and negative ones. So the general equation was not able to find out.
(2) The solar drying process of alfalfa in deep bed has the character of great nonlinear: the drying speeds of different parts alfalfa were not the same, and the temperatures of different locations in the same section were not the same, the temperature of the middle part alfalfa was lower than any other part temperature all the time, the drying speed of deep layer alfalfa was low. To study on the magnitude, positive or negative and the changing trend of the alfalfa surface temperature grads helped to forecast the process of drying. That the temperature difference value between exhaust air and medium and the relative humidity difference value between exhaust air and medium could show the situation of mow humidity.
(3) The factors of time and the sun angle were considered in the heat characteristic simulation model of the solar air collector. The result showed that the performance of the model had improved. Temperature simulation models and the efficiency simulation models of the solar air collector were established by considering space information or not. The simulation results showed that the models could well forecast the process.
(4) The time sequence factor was considered in the alfalfa surface temperature simulation model. Not only the multiformity of the network swatches was enriched, but also the network performance was improved. The different neural network simulation models for the alfalfa surface temperature were established by considering monolayer with single dimension information, two-dimensional information and three-dimensional information, and all these information of multilayer for one day and many days. The processes were precisely forecasted: EC is above 0.97 and mape is below 5.4%. The grads neural network simulation model of the alfalfa surface temperature was established,and its result for the testing sample was also quite well: EC is above 0.97 and mape is below 6%. The temperature grads simulation model of the inside layer alfalfa along the airflow direction was established. This was important to the study and management of the deep bed alfalfa drying process, and helped to forecast the process of drying in real time. This could also be referenced as optimizing the drying process.
(5) The alfalfa moisture content simulation models for different experiments data of monolayer, multilayer and many days were established based on neural networks. The models imitated well and forecasted well.
(6) The solar drying system simulation model of the alfalfa in Deep Bed ,including the heat characteristic simulation models of the solar air collector, alfalfa surface temperature simulation models and the alfalfa moisture content simulation models ,was established. The simulation results of the models were all well. And the real time process of the alfalfa drying could be showed well.
To sum up, the simulation of solar drying model of alfalfa in Deep Bed was established based on artificial neural networks. The models could well forecast the alfalfa moisture content of different parts. The establishment of the alfalfa moisture content simulation model was very useful to the reality. This could forecast the real time alfalfa humidity and direct the real producing.
引文
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