A Hybrid Process Monitoring Strategy for Steel Making Shop

Abstract

The article deals with the development of a process monitoring strategy for a Steel Making Shop (SMS) involving an ensemble of statistical and AI techniques. The monitoring strategy being devised was employed primarily to demonstrate the monitoring of nonlinear processes. The monitoring strategy was based on neural network fitting model and Hotelling T2 control chart. Data pertaining to process and feedstock characteristics of Steel Making Shop (SMS) was considered for checking the efficacy of the monitoring strategy being devised. The neural network fitting model is used for partial or full transformation of the nonlinear data into linear data. Thereafter Hotelling T2 chart was employed on the transformed data for monitoring of the process. The test of nonlinearity of the data involved pairwise comparison of any two characteristics by plotting them in a fitted line plot and computing the model fit value which is indicative of the level of linearity. The hybrid strategy involving the neural network model and Hotelling T2 square chart thus devised was able to monitor the process and detect out of control observation correctly.