Crystallization process particle size distribution modeling and control method

Abstract

The invention discloses a crystallization process particle size distribution modeling and control method. A crystallization process granularity distribution model is explained by constructing a neuralnetwork model in the field of artificial intelligence; the constructed neural network model is trained through historical experimental data to construct a model capable of effectively explaining crystallization process particle size distribution, influence variables of crystallization process particle size distribution are simulated through a hidden layer of the neural network model, and variousinfluence variables and influence weights thereof are determined; based on a constructed crystallization process particle size distribution model, output data is detected by changing variables which are represented by each hidden layer and influence particle size distributio; the average particle size of the crystal at the crystallization endpoint moment in the output data is determined, the optimal control strategy is obtained for the optimal control model of the crystallization process particle size distribution model when the average particle size is maximum, the obtaining mode of the control curve of the crystallization process is improved, and the crystallization efficiency and the average particle size of the crystal are improved.

Description

technical field [0001] The invention relates to the technical field of crystallization process modeling and control, in particular to a crystallization process particle size distribution modeling and control method. Background technique [0002] The construction of the particle size distribution model and the research on the control of the crystallization process can be widely used in the fields of food processing, pharmaceutical production, metallurgy and chemical industry, and is an indispensable link in industrial production. At the same time, the crystal particle size distribution is an important index to measure the quality of crystals. Modeling and controlling the particle size distribution in the crystallization process is of great significance for improving the quality of crystal products and crystallization efficiency. The crystallization process is a complex process of heat and mass transfer, and the particle size distribution of crystals is not only affected by a ...

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Problems solved by technology

Moreover, due to the impact of the affected variables on the crystal particle size distribution, it has been difficult to obtain effective progress in improving the crystal yield and improving the particle size distribution, which in turn has led to limitations in the use of crystallization in various technical fields.

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