Data Mining For Thermodynamic System Process Control And Optimization

Aiming at ameliorate the inefficient energy consumption method, improvement of the operation level is one of the most effective way in which energy can be saved to the maximum extent and additional investment for the substitution of equipment is unnecessary. This is the main focus of the research work of this dissertation. . The first part of this dissertation is about the automation control of the thermodynamic systems and the data mining technology applied in this field. The research objective is to improve the control system performance through the combination of the advanced control strategy with the meaningful information mined from the history operation database. Firstly, a new solution for the control performance enhancement is presented by the reference of the KDD technology. Aiming at practical application, a novel boiler load control method, which combines the multiple model adaptive control strategy with the data mining technology, is put forward in this dissertation. Through utilizing the association rules to identify the fuel heating characteristics, the auto-tuning of the air/fuel ratio in the boiler combustion control can be realized.Secondly, a thermodynamic system predictive control strategy based on the working regime identification online and multiple local linear models is presented, including a new working regime decomposition and identification method and the novel online multi-model combination and controller reconfiguration method. In order to upgrade the controller performance further, we extend our research work to the multiple nonlinear models predictive control and its application in the control of thermodynamic systems. Some amelioration strategies are presented to facilitate the practical application of the ordinary nonlinear predictive controller. Through the simulation test, the feasibility and superiority of these methods are proved.The second part of this dissertation mainly focuses on the thermal engineering process operation optimization and some applications of the data mining technology in this field. Firstly, aiming at solving the difficult problem in scheduling control of the heating load in a practical district heating system, a strategy is proposed which can realize the real balance between the quantity of heat needed for the resident and the quantity of heat provided by heating plant. This method is based on the heating loadpredictive model which is extracted from the practical operation data recorded in production field by the data mining tools, and is realized through the online model-based dynamic optimization.Finally, in order to improve the combustion efficiency of the boiler which is operated in a wide working condition range, a novel optimum oxygen content setting module is proposed by the statistic analysis of the operation data record, which is based on working condition classification, identification and a RBF static mapping function model. This setting module has been implemented in a practical combustion control system and is proved to be effective in fuel-saving.