Cement Clinker Production Process Control And Optimization Considering Quality And Energy Consumption

Cement production is an important industry for the economy development of a country.Clinker production is an important stage of the cement production process.It has the properties of large inertia,nonlinearity,large time-delay,and strong coupling,and is the key point of cement production process control.At present,the overall level of automation of many cement manufacturers is still not very high,and the control of quality and energy consumption still relies on the experience of production managers based on the application of distributed control systems.In this thesis,the optimization model of cement process considering clinker quality and energy consumption is studied,and the model predictive controllers of the decomposition furnace and the rotary kiln are designed to realize the automatic control of temperature in the furnace and the kiln,and the optimization of the cement production process.First,the new dry cement production process are reviewed.The background and significance of this research are explained,followed by a survey on existing research in this area.In Chapter 2,the operating variables and controlled variables of the cement clinker production process and their correlation are analyzed.Then the control and optimization of clinker quality and energy consumption are studied in depth.The input variables for the soft-measurement model for the quantity of f-CaO are determined.Then an optimization model with the objective of minimizing energy consumption and the constraint of quality requirement is proposed.Finally,the overall design scheme of the optimal control system considering quality and energy consumption is introduced.The cement production control and optimization model considering quality and energy consumption is developed in Chapter 3.Firstly,the data acquisition method is introduced,and the collected data is preprocessed.The best time matching of the input variables and the output variable of the soft measurement model is obtained based on the optimization for minimizing the fitting error.Then,the soft measurement model for f-CaO quantity based on the least squares support vector machine(LS-SVM)and the back propagation neural network(BPNN)is employed respectively.The LS-SVM model is finally chosen by comparing their performances.Taking energy consumption minimization as the objective function and the quality requirement as the constraint,the cement production process control and optimization model is finally developed.In Chapter 4,the model predictive control algorithm is applied to control and optimize the production process of the decomposition furnace based on the production optimization model considering quality and energy consumption.Firstly,the model of the decomposition furnace temperature is identified by the least squares method,then the model prediction controller of the furnace temperature is designed to realize the rolling optimization and closed-loop control.Numerical simulation is carried out to examining the control and optimization model with considering the disturbance on the furnace temperature and the quantity of coal feeding.Finally,the model predictive control algorithm is modified by combining with the optimization model developed in Chapter 3,and the weight of energy consumption in the objective function is adjusted in the simulation of model predictive control.Thus energy consumption is optimized through this method.A model predictive controller that considers the clinker quality requirement and energy consumption of the rotary kiln temperature is designed in Chapter 5 to realize the automatic control of the rotary kiln temperature and the optimization of quantity of coal feeding.The predictive model is developed by the least squares method,and then simulation of the model prediction control of the rotary kiln temperature is carried out.The result of simulation shows satisfactory dynamic performance and steady state performance.The test of introducing disturbance on rotary kiln temperature and the quantity of coal feeding also shows that the control system has good disturbance rejection ability.The research in this chapter shows that the control and optimization considering the quality and energy consumption goal leads to reduction of coal feeding.In this thesis,the control and optimization algorithm of the cement production process considering quality and energy consumption are studied.The performance of applying this method to the control of furnace temperature and rotary kiln temperature is good and the energy consumption is reduced.The method developed in this research has an important significance for the cement enterprise to improve the technology of control and optimization of cement production process.