Soft-Sensor Modeling for Semi-Batch Chemical Process Using Limited Number of Sampling

Abstract

Batch or semi-batch processes have been of great use in various industrial chemical plants. For efficiently monitoring such processes, soft-sensor models can be employed. Many of previously proposed soft-sensor models assumed that objective variable values for model construction can be available at any time during process operation. However, in many chemical plants, it is difficult to sample product from the ongoing process due to such extreme reaction conditions as high pressure and temperature. Therefore, understanding the relationship between time-series soft-sensor model’s predictability and the number of sampling points is important. In the present work, we clarified this relationship using simulation datasets, which can be easily reproduced. When sampling points were scarce, data augmentation strategy was also found to be effective. Soft-sensor models can be effectively built using sampling points in the early phase of the process. These findings were applied to build a soft-sensor model of an industrial semi-batch process.