Soft Sensor Based on Recursive Kernel Partial Least Squares for 4-carboxybenzaldehyde of an Industrial Terephthalic Acid Hydropurification Process

Terephthalic acid is a raw material for polyester and textile industry. However, the by-product 4- Carboxybenzaldehyde in TA is harmful to the polymer process since it can lower the polymerization rate and the average molecular weight. Thus, a hydropurification process is built to decrease 4-CBA. In this process, 4- CBA in TA is purified by hydrogen in water at 270 °C–290 °C under 7.9 MPa pressure over 0.5 wt. % carbon- coated palladium catalyst in a fixed-bed reactor. The activity of the catalyst will gradually decrease with the process running. The most important quality index for this process is the content of 4-CBA in the product. However, in real plant, the content of 4-CBA is analysed every two hours in a laboratory. It is a very large delay for control system so the operation conditions of the process could not be adjusted in time. These may results in a maximum two hours product failure. In this paper, a first principle model of the process is developed based on Aspen Plus. The accuracy of the model is verified by the model results and the actual plant results. Based on this model, a series of sensitive analysis are performed. Six variables include 4-CBA content in TA, react temperature, react pressure, hydrogen flow rate, catalyst activity and Slurry concentration are the main factors influencing the 4-CBA content in the product. Though the aspen model is accurate, these parameters in aspen model are not easy to adjust quickly. In order to predict 4-CBA content quickly, a soft sensor for 4-CBA is developed using recursive kernel partial least squares considering the nonlinearity and slow time-varying of the process. Results show that the prediction accuracy of this method is very high and it is easy for engineers to handle.