Enhanced just-in-time soft sensor calibration method using data density estimation

Abstract

Soft sensor is an efficacious solution to predict the hard-to-measure target variable by using the process variables. In practical application scenarios, however, the target feedback cycle is usually larger than that of process variables which causes a lack of sufficient prediction errors during the period of a target feedback cycle. Consequently soft sensor cannot make calibration timely and performance deteriorates. We proposed an enhanced just-in-time (JIT) soft sensor calibration method using data density estimation. The enhanced JIT method as the core is basically implemented by the estimate of data density of the history database. First the database is divided into a plenty of data blocks. The center of each block is calculated in pair of the process and target variables respectively. For each center we designed a criterion to preliminarily work out the corresponding optimized sampling number to indirectly represent the data density of each block and further use pooling strategy to partition the database into some differently dense zones. Ultimately we obtain the data density of the database making precise sampling feasible to improve the performance of the JIT-based method. The proposed calibration method is tested through comparative experiments on a pH neutralization facility in our laboratory and is verified feasible and effective.