| One important feature of modern process industry is the advancement towards large-scale and integrated automation. Through the integration of process control, management, scheduling and marketing and other technical means, integrated Automation of Process Industry can achieve global and local optimization with the shortest cycle and minimum cost for the greatest economic benefit. Babnde A. Ogunnaikeatu discusses five problems facing modern process industry: online measurement, serious nonlinearity, modeling and recognition of the control system, modeling for the training of simulation and execution units, and process monitoring and fault diagnosis. Among them, an important issue is to clarify the operating mechanism of the system and to establish the system description model in order for strict scientific management and planning.Among the process industry, cement industry is not only one of pillar industries of national economic development, but also one of larger energy consumption industries and its technological property obviously exhibits the characteristics of process production. In the end of the 60s, with the development of computer technology, the use of computers to solve optimal scheme of engineering problems has become an increasingly important means of solving practical design problems. It not only enhances the efficiency and reliability of the problem-solving greatly, but also resolves a lot of practical problems that could not be solved in the past. Especially in recent years, process industry is growing at a rapid pace with the development of globalization of world economy. At the same time, the production in process industry is high complex, strong associated, non-linear and indeterministic. The expansion of production scale will surely complicate the parameters of the process and enhance the association among them. At present, the research on process industry concentrates mainly on the control of the process optimization and neglects the problem of parameter selection required by process optimal control; however, proper selection of parameters will influence the effect of process optimal control directly.Based on the above analysis of the process industry, this research is to treat pre-calciner, rotary kiln and grate cooler as a whole and to build its production process model. Decomposing furnace is mainly responsible for the decomposition of the raw material, and the decomposition rate could reach 90% -95%; then the decomposed material flows into rotary calciner, which takes on two tasks: one is to decompose the remaining raw material, and the other is to calcine the decomposed raw material; finally the calcined material is cooled in the grate bed. The three processes are closely linked with each other. Therefore, pre-calciner, rotary kiln and grate cooler are closely linked and mutually interacting. The quality of cement is not only dependent on one single process, but on the overall operation of the three courses. Using the sophisticated neural network technology and data mining techniques, and with the cooperation with experienced experts in cement production, we extract the parameters that influence the three production processes, and then use the flexible neural model to model the entire production process. The creative point of this work is as follows. The evolutional generation of traditional FNT model is fixed traditionally, yet the best model is not always formed in this generation, so to fix the evolutional generation is unreasonable. This improved algorithm uses mean error rate to control the number of evolutional generation instead of fixing it. This method provides theoretical basis for the production control of process industry so that the process control is more scientific and pertinent. On the other hand, it establishes a good basis for process optimal control of process industry. Using this method, we can not only obtain the best model and parameters, but enhanced the efficiency and accuracuy.In the modeling process, the most important factor that impacts of cement production process is screened out, and this modeling process is based on Probabilistic Incremental Program Evolution (PIPE) algorithm. Then the link between these main parameters is found, and finally the best combination of programs from their association is found, namely, the optimization model. This optimization process uses the Simulation Annealing (SA) algorithm. Through this modeling process, not only the parameters relationship between the same productions processes are found, but the proportional relationship between parameters among a different manufacturing process is also found. Through the entire production process modeling and optimization, the purpose of stabilizing the cement production process and improving the rate of decomposition of raw materials and reducing the amount of coal consumption are reached. Finally, the ultimate research result will be applied to the practical production control using DCS system. |
