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Decreasing the acetic acid consumption in purified terephthalic acid (PTA) solvent system has become a hot issue with common concern. In accordance with the technical features, the electrical conductivity is in direct proportion to the acetic acid content. General regression neural network (GRNN) is used to establish the model of electrical conductivity on the basis of mechanism analysis, and then particle swarm optimization (PSO) algorithm with the improvement of inertia weight and population diversity is proposed to regulate the operating conditions. Thus, the method of decreasing the acid loss is derived and applied to PTA solvent system in a chemical plant. Cases studies show that the precision of modeling and optimization are higher. The results also provide the optimal operating conditions, which decrease the cost and improve the profit.
In accordance with the technical features, the electrical conductivity is in direct proportion to the acetic acid content. General regression neural network (GRNN) is used to establish the model of electrical conductivity on the basis of mechanism analysis, and then particle swarm optimization (PSO) algorithm with the improvement of inertia weight and population diversity is proposed to regulate the operating conditions. Thus, the method of decreasing the acid loss is derived and applied to PTA solvent system in a chemical plant. Cases studies show that the precision of modeling and optimization are higher.