Research On Dynamic Modeling And Predictive Control Of High Temperature Steam Heat Pump

At present,in order to meet the heat demand of some industries,small coal-fired boilers of less than 10 steam tons have a larger market.The heat production efficiency of these small boilers is very low,and most of them lack effective flue gas treatment measures.To this end,many regions are promoting the replacement of small industrial coal-fired boilers with hightemperature steam heat pumps.However,there are few researches on high-temperature steam heat pumps,and how to model,analyze performance and optimize control according to the system mechanism of high-temperature steam heat pumps is the current research focus.Based on the mechanism of high-temperature steam heat pumps,this paper conducts system modeling,parameter optimization,and research on economic predictive control algorithms.The main research contents include:1.Explain the structure and model of the high-temperature steam heat pump selected in this paper,and explain the selection of various subsystems in this paper and the selection of system model parameters.Then,the system mechanism of the high-temperature steam heat pump is studied,and the static modeling process of the system is explained.Finally,the static characteristics of the built model are simulated and analyzed.The simulation results show that there may be many operating parameter solutions for the same operating point of the system,and it is very important to find a suitable and energy-saving operating point.The simulation results lay the foundation for the optimization and control research of the later system.2.Introduced the basic principles and algorithm framework of the multi-objective artificial bee colony algorithm(MOABC).The optimization parameters and optimization indexes of the optimization problem are determined,and the exergy efficiency is used as the optimized economic index.Aiming at the structural parameters of the high-temperature steam heat pump system in this paper,multi-objective optimization is carried out at the rated operating point of the system,and the optimization results are compared and analyzed.The optimization results show that compared with the first law of thermodynamics,MOABC based on the second law of thermodynamics can increase the exergy efficiency of the system by 44.7% under rated conditions,but at the same time increase the energy consumption by 23.3% and the cost by21.6%.3.Based on the conservation of energy and mass,the dynamic models of the heat exchanger,flash tank and water-jet twin-screw compressor are established.In order to better study the changes of system exergy,this paper establishes a dynamic model of system exergy loss.Finally,based on the change of the control and disturbance,the dynamic simulation and test of the system are completed,and the dynamic characteristics of the system states and performance indicators are analyzed.4.The algorithm principle of model predictive control is introduced,the augmented form of the discrete state space model with measurable disturbance is deduced,and the state estimation algorithm based on Kalman filter is deduced.The normalized EMPC algorithm based on the weighted summation method is used to convert multiple targets into single targets,and the simulation and analysis of the control algorithm are carried out.The simulation results show that EMPC based on the first and second laws of thermodynamics has good control effects.EMPC based on the second law of thermodynamics can also reduce the exergy loss in the control process and extend the service life of the equipment.Finally,the implementation of EMPC in embedded system is introduced.