Smart Online Fuel Sulfur Prediction in Diesel Hydrodesulfurization Process

Abstract

In the process industry, online operators usually adjust the entire production process in time according to changes in certain key indicators. However, it is difficult to directly obtain the value of key indicators due to the complex structure and the numerous processes of the process industry. This paper proposes a method to achieve high precision on-line prediction of key indicators in the process industry. The method first performs data preprocessing of multi-source heterogeneous time series data involved in industrial processes based on the professional knowledge, which not only keeps the prediction error within 10%, but also reduces the prediction time. Then a model framework is constructed based on LSTM neural network and the error correction algorithm is proposed to improve prediction accuracy based on real-time error, which directly causes the error to drop by 3%-5%. At the end, a set of multi-mode online training strategies and related trigger conditions are designed to perform predicting online. Diesel hydrodesulfurization is a typical case in the process industry. The effectiveness of the proposed method is empirically studied by applying its actual data sets. Through the comparison with other traditional well-known forecasting models, and models optimized by adjusting parameters, the experimental results demonstrate that the method can achieve the great prediction performance in terms of both accuracy and stability.

Keywords

• Fuel sulfur
• LSTM
• online predicting
• sensor