Intelligent Modeling Research On Calcination Process Of Lithopone Rotary Kiln

In this paper, the complexity of the calcination process of rotary Lithopone kiln is analyzed. On the basis of process data acquisition and analysis, several intelligent modeling methods for process control have been presented and discussed in detail. Firstly, after studying the present state of the modeling and control of calcination process of rotary kiln in domestic and foreign countries and analyzing the difficulties and key problems to be resolved urgently, segmentation modeling strategy is proposed, which establishes the foundation for subsequent modelings. Secondly, a two-stage identification method is proposed for the identification of the temperature control system of kiln head, which can overcome the shortcomings resulted from the correlation between the feedback and input. Moreover, the linear model of the temperature of kiln head as a function of the jaw opening of oil return valve is established through simulation For the modeling of the quality control system of the calcination process of rotary kiln, based on the idea of energy balance, under the condition of stabilizing the temperature of kiln head and flow rate and dry result, a new method is proposed to adjust calcination speed so that changing calcination time and adjusting the energy value of the calcination process and changing the ACC index. In addition, energy balance control of calcination process is deduced using Arrhenius empirical equation, on this basis, the linear regression prediction model concerning the logarithm of calcination angular speed versus the calcination temperature is established. Furthermore, the characteristics and approximation accuracy of the model are also discussed Thirdly, in order to improve the accuracy of the model of the calcination temperature of rotary kiln, a new modeling method—adaptive neuro-fuzzy inference modeling system (ANFIS) based on T-S model is proposed combining fuzzy logic with neural networks. By employing T-S identification model and using the learning ability of the neural networks, it can greatly improve the identification accuracy compared to the traditional linear regression modeling method. For the study of data clustering method, a novel clustering method based on artificial immune system (AIS) is developed to solve the problem of fuzzy structure identification, which makes the adjustment of fuzzy rules fast and flexible. This appears very useful in the process control with huge data and complex environment. In this paper, the influence on the system identification result by the suppress threshold and clustering range ratio in AIS network is also discussed in detail. Considering the randomness of AIS, the algorithm is modified to prevent the rule number of clustering from fluctuation In order to enhance the identification speed of the control model of the rotary calcination kiln, a novel least square support vector machines (LS-SVM) is proposed, which is another new try for solving the problem of complex system by employing statistical learning theory and establishing process model based on the principle of structure risk minimization. LS-SVM applys least squares linear system to replace the quadratic programming algorithm to realize its learning function, which has a simple structure, is easy of use and has an excellent learning speed within required accuracy. Through simulations it demonstrates more better identification accuracy and faster speed compared to ANFIS. In enhancing the identification capability of the proposed algorithm, a new modeling algorithm based on mixed kernel function is developed after analyzing the mapping of two typical kernal functions, which synthesizes the merits of suppressing prediction output fluctuation of global kernal function and the higher fitting accuracy of local kernal function and thus has excellent performance of synthesized identification compared to the SVM with single kernal function. Finally, the modeling strategy of LS-SVM is applied to design a multiple inputs and single output system, in which a model with multiple variables is establis...