Cement production is characterized by its great capacity, long-time delay, multi variables, difficult measurement and multi disturbances. According to the distributed intelligent control strategy based on the multi (a...Cement production is characterized by its great capacity, long-time delay, multi variables, difficult measurement and multi disturbances. According to the distributed intelligent control strategy based on the multi (agent,) the multi agent control system of cement production is built, which includes integrated optimal control and diagnosis control. The distributed and multiple level structure of multi agent system for the cement control is studied. The optimal agent is in the distributed state, which aims at the partial process of the cement production, and forms the optimal layer. The diagnosis agent located on the diagnosis layer is the diagnosis unit which aims at the whole process of the cement production, and the central management unit of the system. The system cooperation is realized by the communication among optimal agents and diagnosis agent. The architecture of the optimal agent and the diagnosis agent are designed. The detailed functions of the optimal agent and the diagnosis agent are analyzed. At last the realization methods of the agents are given, and the application of the multi agent control system is presented. The multi agent system has been successfully applied to the off-line control of one cement plant with capacity of 5 000 t/d. The results show that the average yield of the clinker increases 9.3% and the coal consumption decreases 7.5 kg/t.展开更多
Recognizing the drawbacks of stand-alone computer-aided tools in engineering, several hybrid systems are suggested with varying degree of success. In transforming the design concept to a finished product, in particula...Recognizing the drawbacks of stand-alone computer-aided tools in engineering, several hybrid systems are suggested with varying degree of success. In transforming the design concept to a finished product, in particular, smooth interfacing of the design data is crucial to reduce product cost and time to market. Having a product model that contains the complete product description and computer-aided tools that can understand each other are the primary requirements to achieve the interfacing goal. This article discusses the development methodology of hybrid engineering software systems with particular focus on application of soft computing tools such as genetic algorithms and neural networks. Forms of hybridization options are discussed and the applications are elaborated using two case studies. The forefront aims to develop hybrid systems that combine the strong side of each tool, such as, the learning, pattern recognition and classification power of neural networks with the powerful capacity of genetic algorithms in global search and optimization. While most optimization tasks need a certain form of model, there are many processes in the mechanical engineering field that are difficult to model using conventional modeling techniques. The proposed hybrid system solves such difficult-to-model processes and contributes to the effort of smooth interfacing design data to other downstream processes.展开更多
文摘Cement production is characterized by its great capacity, long-time delay, multi variables, difficult measurement and multi disturbances. According to the distributed intelligent control strategy based on the multi (agent,) the multi agent control system of cement production is built, which includes integrated optimal control and diagnosis control. The distributed and multiple level structure of multi agent system for the cement control is studied. The optimal agent is in the distributed state, which aims at the partial process of the cement production, and forms the optimal layer. The diagnosis agent located on the diagnosis layer is the diagnosis unit which aims at the whole process of the cement production, and the central management unit of the system. The system cooperation is realized by the communication among optimal agents and diagnosis agent. The architecture of the optimal agent and the diagnosis agent are designed. The detailed functions of the optimal agent and the diagnosis agent are analyzed. At last the realization methods of the agents are given, and the application of the multi agent control system is presented. The multi agent system has been successfully applied to the off-line control of one cement plant with capacity of 5 000 t/d. The results show that the average yield of the clinker increases 9.3% and the coal consumption decreases 7.5 kg/t.
文摘Recognizing the drawbacks of stand-alone computer-aided tools in engineering, several hybrid systems are suggested with varying degree of success. In transforming the design concept to a finished product, in particular, smooth interfacing of the design data is crucial to reduce product cost and time to market. Having a product model that contains the complete product description and computer-aided tools that can understand each other are the primary requirements to achieve the interfacing goal. This article discusses the development methodology of hybrid engineering software systems with particular focus on application of soft computing tools such as genetic algorithms and neural networks. Forms of hybridization options are discussed and the applications are elaborated using two case studies. The forefront aims to develop hybrid systems that combine the strong side of each tool, such as, the learning, pattern recognition and classification power of neural networks with the powerful capacity of genetic algorithms in global search and optimization. While most optimization tasks need a certain form of model, there are many processes in the mechanical engineering field that are difficult to model using conventional modeling techniques. The proposed hybrid system solves such difficult-to-model processes and contributes to the effort of smooth interfacing design data to other downstream processes.
