A novel multi-step prediction model for process monitoring

Abstract

In the competitive market, process monitoring can ensure the quality of products, but strong nonlinearities, slow dynamics, and uncertainties characterize the complexities of the large-scale chemical plant. When the fault occurs, it will not influence the process instantaneously but will react after a few time points. After all the products affected by the faults are inspected, it is too late to fix the process. Conventional approaches neither do nor care about early detection before any disturbance significantly affects the process. To estimate disturbances propagated through the process, a multi-step prediction model is essential. The purpose of early process monitoring is to detect any problem with the currently running process as early as possible. In this paper, a multi-step prediction system is proposed. The system is a dynamic model that can capture the dynamic relationship of past process input variables and future process output variables. It provides a lower dimension and a lower noise-contaminated space for data analysis. Particularly, the past input and output process data can be mapped from the observation space into the latent space to acquire their intrinsic properties. The latent variables preserve the dynamic information for future multi-step prediction so that early warning can be achieved. An industrial example of the PVC dying process is presented to show the multistep predictive ability of the proposed method.