基于改进堆叠自动编码器的循环冷却水系统工艺介质温度预测控制方法

Predictive control method of process medium temperature in circulating cooling water system based on improved stacked auto encoders

循环冷却水系统中冷却供给量与工艺介质冷却需求量之间往往存在"大马拉小车"的现象,造成大量的冷却资源浪费.为了匹配冷却需求量与供给量,提高循环冷却水系统能源利用率,给出一种基于多工艺介质温度目标循环冷却水最小压差控制系统,并将深度学习引入工艺介质温度预测研究中,提出一种基于改进堆叠自动编码器(improved stacked auto encoders,ISAE)的工艺介质温度预测方法.首先,对工业现场数据进行清洗;然后,将多个自动编码器堆叠,构建深度学习网络结构,采用"逐层贪婪无监督预训练-参数微调"方法训练网络参数,并基于均方根反向传播(root mean square back propagation,RMSProp)优化方法对网络参数进行微调,减小陷入局部最优的概率;最后,利用某化工厂历史运行数据进行测试,与浅层神经网络、未改进的SAE方法进行比较,所得结果表明,所提出的ISAE方法的预测准确性高,预测的工艺介质温度平均百分比误差仅为0.85%,且泛化能力优于未改进的SAE算法.

The designed capacity of cooling supply is much higher than demand of medium in a circulating cooling water system,which leads to the great waste of electricity energy.In order to keep balance between cooling demand and supply,and improve the energy efficiency of the circulating cooling water system,a minimum differential pressure control scheme for the multi-process medium temperature target of the circulating cooling water system is presented,and deep learning is applied to the study of the medium temperature prediction.And a temperature prediction method for media based on improved stacked auto encoders(ISAE)is proposed.Firstly,the industrial data is cleaned.Then,multiple autoencoders are stacked,and the"greedy unsupervised pre-training layer by layer"method is used to train network parameters.The root mean square back propagation(RMSProp)optimization is deployed to fine-tune network parameters to reduce the possibility of falling into local optimums.Finally,the process medium temperature prediction model is obtained using off-line training of the historical operation data of a chemical plant.Compared with the results of shallow neural networks and unmodified SAE methods,the prediction accuracy of the proposed ISAE method is high,the mean absolute percentage error(MAPE)is only 0.85%,and the generalization ability is better than the unmodified SAE algorithm.