A dynamic correction method for the optimal value settings of the solution purification process at multiple time scales

The solution purification process includes multiple continuous reactors. Setting the key technical indicators of each reactor through global optimization is the prerequisite for realizing the optimal operation of the entire process. Affected by fluctuations in inlet conditions, adjustments of operating parameters, and random disturbances, the operating status of the solution purification process will change accordingly, causing the optimal value settings based on global optimization to become no longer applicable. To ensure the applicability of the optimal value settings as the process changes and considering that the production data collected at different time scales contain different process information, this study proposes a dynamic correction method for the optimal value settings of the solution purification process at multiple time scales. First, considering the low-frequency testing data that can reflect the operation effect, the low-frequency correction is realized by combining mechanism knowledge and expert experience. Second, based on the characteristic that the high-frequency detection data can reflect the changing operating status in time, a supervised self-organizing map method is proposed to classify the changing trends in the operating status. Finally, an integrated, spatiotemporal, just-in-time learning method (with multiple changing trends in the operating status) is proposed to realize high-frequency correction. The experimental results show that the proposed method can dynamically correct the optimal value settings and reduce resource consumption while ensuring product quality.