A self-learning dynamic optimization method for batch processes driven by data differences

Abstract

The invention discloses a dynamic optimization method for intermittent process self-learning driven by data difference, including off-line collection of production process data, PCA operation to eliminate singular batches, constructing period and index variance matrix to perform PLS operation to generate initial optimization strategy, and collecting new batches Secondary data, running recursive algorithm, updating optimization strategy and other steps. The present invention utilizes perturbation method to establish an initial optimization strategy for optimizing variable setting curves. On this basis, the self-learning iterative update of the mean and standard deviation is carried out based on the statistical difference of the data, and the continuous improvement of the optimization index is realized, which provides a new method for the batch process optimization strategy to solve practical industrial problems. The present invention is entirely based on the operating data of the production process, and does not require prior knowledge and mechanism models of the process mechanism. It is suitable for the dynamic optimization of the operating trajectory of batch reactors, batch rectification towers, batch drying, batch fermentation, batch crystallization and other processes and systems that operate in batch mode.

Description

technical field [0001] The invention belongs to the field of chemical process manufacturing industry, and relates to a model-free intermittent process self-learning dynamic optimization method driven by data differences. The method is suitable for the dynamic optimization of the operating trajectory of batch reactors, batch rectification towers, batch drying, batch fermentation, batch crystallization and other processes and systems operated in batch mode. Background technique [0002] Batch processes are widely used in the production and preparation of various products such as food, polymers, and pharmaceuticals, and play an important role in chemical production and process industries. With the continuous development of computer technology, process control and optimization technology, the quality control and optimization of batch process has become one of the current research hotspots in industry and academia, which is of great significance to the development of batch proces...

AI Technical Summary

Problems solved by technology

However, when optimizing the operation of a batch production process, if only one optimization is performed, the optimization performance is limited

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