Predictive Control Research On Temperature Of Cement Calciner Based On Three-way Decision Theory Optimization

In cement production,the temperature of the cement decomposition furnace will have a great impact on the quality of cement.Controlling the temperature of the cement decomposition furnace within a reasonable and stable range is the key to ensuring the safe operation of the system and improving the quality of cement.However,in the cement manufacturing process,each process restricts each other and the working conditions change dynamically,which often results in manual inability to adjust the temperature and operation indicators of the calciner in time,and the fluctuation of materials and the consideration of energy saving and consumption reduction also bring certain difficulties to the control of the calciner.Therefore,this paper proposes a predictive control algorithm for cement decomposition furnace outlet temperature based on three-way decision-making optimizations.The three-way decision theory is used to dynamically adjust the decomposition temperature operating indicators according to changes in working conditions to achieve real-time dynamic optimization of process indicators.At the same time,the model predictive control method is used to build a calciner temperature control model,to build an objective function combining set value control and interval control,and the optimal solution is obtained by optimizing the objective function to achieve precise control of the calciner temperature.The specific research content is as follows:First,study the production process of the new dry cement technology,analyze the reaction mechanism of the pre-decomposition system,and discuss the factors that affect the outlet temperature of the calciner.An identification algorithm for outlet temperature of the calciner predictive model based on genetic algorithm and improved particle swarm optimization least square support vector machine(GA-IPSO-LSSVM)is proposed.By improving the parameters of the particle swarm algorithm,and then introducing genetic algorithm into the improved particle swarm algorithm to improve the search performance of the particle swarm algorithm and increase the diversity of the population particles.The problem that the particle swarm algorithm is easy to converge prematurely and fall into the local optimal solution is improved.Then use the GA-IPSO algorithm to optimize the regularization coefficient and kernel width in the classic least squares support vector machine algorithm to realize automatic optimization of parameters.And through experimental comparison,it is proved whether it is effective in enhancing the identification accuracy and generalization ability of the model,which lay a model foundation for the establishment of a predictive control system for the outlet temperature of the calciner.Secondly,in view of the problem of real-time optimization of the operation index of the cement decomposing furnace outlet temperature,the three-way decision theory is introduced,and a dynamic optimization rule for the setting value of the cement decomposing furnace outlet temperature is designed based on the three-way decisionmaking.The temperature control system of the decomposition furnace can automatically optimize the set value of the outlet temperature of the decomposition furnace according to different working conditions.The mayfly algorithm is used to optimize the three-way decision loss function to obtain the optimal decision threshold,which reduces the dependence of the establishment of the three-way decision rough set model on prior knowledge and achieves the goal of minimizing decision risk.Finally,in view of the control of the outlet temperature of the cement decomposer,this paper combines the dynamic optimization of the set value of the cement decomposer outlet temperature based on three-way decision theory with the decomposer temperature model prediction controller.The real-time optimization of the set value of the outlet temperature of the calciner avoids the problem of artificial setting of high or low set values due to different levels of operators.The model predictive controller adopts the GA-IPSO-LSSVM model identification algorithm proposed in this paper,and uses the combination of set value control and interval control to control the outlet temperature in the normal interval and make it closer to the set value,thereby realizing Control of the outlet temperature of the decomposition furnace.Experiments show that the outlet temperature of the calciner can accurately follow the change of the set value,and the validity and correctness of the algorithm are verified.