Abstract
In this paper the optimization of Purified Terephthalic Acid (PTA) crystal crystallizer based on FGMDH networks and Adaptive Differential Evolutionary (ADE) algorithm is discussed in detail. Due to the existence of many by-products and impurity in PTA continuous industry production process, it is very difficult to build mechanism models for this process. Since Artificial Neural networks have been proved to be able to approximate a wide class of functional relationships very well in modeling chemical process, we apply a kind of FGMDH networks to build PTA granularity model, which is incorporated with human experiences. To implement the control of PTA granularity, which is one of the key product quality indexes, a kind of global real-value optimization algorithm -— ADE algorithm is proposed for optimizing of PTA crystallization process. The proposed ADE is capable of find the optimal operation conditions effectively and efficiently and suitable for industrial application.
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