Medium-term prediction of key chemical process parameter trend with small data

Accidents might happen in chemical factories when less experienced operators unreasonably react to emergencies. It is therefore desirable to predict key process parameters early enough to help plant management easily and readily assess the coming risks and take proper actions to avoid them. Some of the predictions are in the time scale of days, if not months. However, such tools are rarely available in the chemical process industry, and often require a large amount of data for modeling. With inspiration from ecological researches, we propose a novel integrated causal analysis multiview embedding(ICAME) algorithm to predict medium-term trends of key chemical process parameters with small datasets. The proposed ICAME is mainly based on empirical dynamic modeling(EDM), a group of time series analysis approaches. Its application in a simulated dataset and a real industrial process demonstrated its advantages on small data over other methods for medium-term trend predictions of chemical process parameters.(c) 2021 Elsevier Ltd. All rights reserved.