Deep Learning Based Soft Sensor to Predict Total Suspended Solids of Refinery Water Treatment Plant Using Real Process Data

Abstract

Soft sensors have proven themselves as a valuable tool in terms of quality prediction, process monitoring, process control and minimizing operating cost. Compared to traditional hardware sensors, a soft sensor is a technology that consists of all combinations of mathematical model, data processing and software techniques. Deep learning method has the great potential to be data-driven soft sensors. In this paper, Gated Recurrent Unit (GRU) which is one of the deep learning model was developed as soft sensors for prediction of Total Suspended Solids (TSS) concentration having a role of water quality of New Water Treatment Plant (NWTP) in oil refinery. A real dataset consisting of 39 parameters (41,123 observations) over 6 months from different sensor measurements including flow, temperature, pressure and turbidity etc. was used for GRU model. Also, Principal Component Analysis (PCA) was used for reducing the dimensionality of datasets, providing data interpretation and examining data that correlates with each other. In this case, three different scenarios were created using 5, 10 and 39 process variables as input based on the PCA results. The results show that acceptable estimation has been achieved through the developed algorithm. The minimum mean absolute errors of 0.0108 % for testing dataset and R2 of 0.899 for using 10 input variables that affect TSS concentration were achieved. The motivation in this study is that the data-driven predictive model is realized from historical data in large scale system, so it is valuable for the theory to take shape in an industrial application.