| Sintering is important for the iron-making industry. As an essential pre-process for blast furnace materials, it could minimize the fluctuations of the materials, and thus guarantee the furnace's steady operation. With raising price in iron ore and public enhancing consciousness of energy saving and environmental protection, it is imperative to improve the technology in iron making plants in terms of saving energy and reducing cost. In this technical innovation, the sintering process has attracted more and more attention as it plays an important role. Therefore, it is practically significant to study the iron ore sintering process.Automatic control system for sintering contains mainly two parts:control of sinter mix proportion and control of thermal state. Both of them should be designed on the basis of the model of sintering process. However, it is difficult to establish the accurate mathematical model of sintering process. In this thesis, improved Genetic Programming algorithms are proposeed to conquer the above difficulty Then, in order to solve the multi-objective optimization problem in sintering process, Non-dominated Sorting Genetic Algorithm II (NSGAII) is applied and then enhenced.In detail, the major contributions of this thesis are summarized as followings:1. An improved Hierarchical Genetic Programming (HGP) is proposed to solve the problem of modeling the nonlinear dynamic system with unknown mechanism and massive data in industrial processes. Based on the hierarchy model and feedback, the algorithm identifies the model according to the hierarchies until the error can be acceptable. Least square method (LSM) and M-estimation is adopted to overcome the large noise and increase the robustness of the model. The experimental results demonstrated that this algorithm is effective in nonlinear dynamic model identification.2. An improved Classified Genetic Programming (CGP) is proposed. K-Means clustering or K-Medoid clustering is applied to partition conditions of the target objects. Besides, for each clustering, GP is proposed to construct the empirical model. CGP adopts least square method (LSM) and M-estimator to improve the abilities of computing and disturbance resistance. Simulation proved that CGP has satisfactory performances.3. A Preference-based Non-dominated Sorting Genetic Algorithm (PNSGA), an improved method of NSGA II (non-dominated sorting genetic algorithm II), is proposed to solve the multi-objective optimization problems. A new preferable relationship was defined based on Pareto dominance and combined with the fast non-dominated sorting. The advantage of our algorithm over NSGA II in terms of crowding mechanism was analyzed. Simulation results demonstrated the effectiveness of the algorithm on parameters identification of dynamic model, compared with conventional experienced methods.4. To avoid fluctuations of components and quality of iron ore sintering, quality prediction models are created by HGP, which contain the components and tumnler models of sinter. Partial mechanism knowledge is introduced into initial population of HGP, and the models are based on both the partial mechanism and the data. Simulation results showed the superiority of these quality predictive models. One optimization scheme of sinter mix proportions is proposed based on predictive quality of iron ore sintering. The optimization objectives are to minimize the fluctuation of TFe component, basicity and tumnler. PNSGA is utilized to solve the multi-objective optimization problems. Simulation results showed the superiority of sinter mix proportion based on quality predictive models.5. For predicting burning through point (BTP), two models for temperature prediction are established by CGP in the sintering process. The two models were the medium-term model based on the temperature inflexion and the short-term model based on the temperature neighboring to BTP. BTP was obtained by the cubic curve fitting of the predicted temperature. Simulation results proved the superiority of the two-term prediction model.
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