Online Output Estimation for Multimode Process with Dynamic Time-delay
作者单位：Xi’an University of Technology
会议名称：《第三十九届中国控制会议》
会议日期：2020年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080202[工学-机械电子工程] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
关 键 词：Time-delay Maximum Information Coefficient Random Forest Regression Soft Sensor
摘 要：There is a time-delay problem between the correlated process variables of the industrial. It is very important to get accurate time-delay for estimating hard-to-measure variable. The existing methods for solving time-delay need to obtain both easy-to-measure variables and hard-to-measure variable. But the hard-to-measure variable cannot be directly measured due to some technical and cost reasons. To solve this problem, a random forest regression model combining sliding window and maximum information coefficient method is proposed. The on-line estimation of the time-delay between the easy-to-measure variables and the hard-to-measure variable is realized. The process variables are reconstructed by the time-delay parameters. The reconstructed process variables are used to build the soft sensor. Finally, the hard-to-measure variable can be estimated by soft sensor accurately. This method is applied to the process variable estimation of nitric acid produce process by dual-pressure method. The effectiveness of the method is verified by experiment results.