Outlet Liquid Material Concentration Prediction of an Evaporation Process Based on Knowledge and Data Information

Abstract

The outlet liquid material concentration is a key production indicator to evaluate the evaporation quality and an important basis to adjust the evaporation operation parameters. However, the online concentration analyzer has strict installation conditions and high prices, and it is difficult to obtain the liquid material concentration in time. Usually, the field works perform imprecise operations according to the time delay information. In addition, the process data contain errors, which affects the accuracy and timeliness of process optimization and control. Therefore, a hybrid prediction model of concentration based on data reconciliation is presented in this paper. First, to obtain the high-quality process data, the data reconciliation method is applied for preprocessing. Moreover, the process mechanistic model is constructed by utilizing the process knowledge and the balance principle. Taking into account the volatility and nonlinearity characteristics, a data-driven model based on autoregressive integrated moving average integrated generalized autoregressive conditional heteroscedasticity is established, and then the support vector regression model is built for prediction residual optimization. Furthermore, the prediction results of the mechanistic model and the data-driven model are balanced comprehensively. Finally, an evaporation process is selected for simulation verification. The results demonstrate that the proposed hybrid prediction model has improved the prediction condition and performance.