Study On Intelligent Integrated Modeling Theory And Its Applocations To Optimal Control Of Nonferrous Metallurgical Process

Nowadays, steady-state optimal control has become an important approach to tapping potential, reducing cost, increasing production, raising efficiency and improving competitive power of processing industrial enterprise. However, owing to complex process mechanism with strong non-linearity, time-variability and severe coupling, it is difficult to construct complex steady-state relationships between production objects and operational parameters, which obstructs the implement of optimal control for complex industrial process. So this dissertation emphasizes on intelligent integrated modeling methods and their industrial applications to solve the above problems.Firstly, the dissertation systematically analyzes and summarizes the status quo and existing problems of researches on process control, steady-state optimal control, industrial process modeling and non-ferrous metallurgical process control. Then aiming at the disadvantages of the present modeling methods, the characteristics of complex industrial process and the requests of optimal control for models, an idea of intelligent integrated modeling is proposed and the basic framework of the corresponding theory is constructed comprehensively and systematically. Namely, its universal definition and six integrated forms including parallel connection for compensation, parallel connection with weight, model integration in series, model embedding, neural networks implement of model structure and part substitution of modeling are described, and its formalized description and engineering implement are given. The constructed modeling theory can provide guidance for derivation of new modeling.Secondly, according to research status on NN(Neural Networks) and existing problem in modeling for industrial process, three integrated modeling based on NN are studied under the instruction of the intelligent integrated modeling theory. In Multi-NN as the first integrated modeling, an adaptive supervised distributed NN (ASDNN) is proposed. According to ASDNN, learning sample set is divided into many subsets with different eigenvalue by supervised clustering method, a series of NN models are constructed based on these subsets, then these NN models are combined by fuzzy logic technology. ASDNN has many advantages: 1) solving some problems in NN for process modeling such as large structure, slow convergence as well as overfitting and so on; 2) directly determining clustering numbers without pre-determination, guaranteeing efficient and reasonable division of sampling set; 3) strengthening its real-time predictability. The second integration modeling involves the integration of NN and the conventional modeling. At first, model embedding, parallel connection for compensation and neural networks implement of model structure the three integration forms are described in brief. Then NN integration modeling with weighted input layer by least-square Methods(LSM) isproposed, which has not only high accuracy and fast convergence but also excellent adaptability to the industrial process with strong non-linearity, especially many input variables. The third integration, integrating NN and other intelligent modeling, emphasizes on a fuzzy system integrated modeling with NN structure. The modeling has some characteristics as follow. I) describing fuzzy models of Sugeno 0 and Sugeno I as the five-layer structure of ANFIS; 2) initiating both antecedent parameters and structure based on the present expertise or competitive learning clustering algorithm to avoid local minimum; 3) proposing a hybrid algorithm combined LSM and BP algorithm with changeable learning step and momentum factor altered by fuzzy logic to speed up network learning, and optimizing network structure based on Akaike Information Criterion(AIC); 4) real time updating network parameters and structure for timevariability of industrial process. The modeling due to its simple structure and learning facility can execute fuzzy reasoning better and avoid local minimum.As far as industrial applications of the intelligent integrat...