Research On Energy Efficiency Optimization Method Of Building Ceramic Polishing Workshop For Personalized Customization

Polishing process is one of the key techniques of the architectural ceramics,the energy consumption and costs of which are rather high.Thus,it is of great significance for reducing the costs of ceramic production to improve the energy efficiency of polishing process.With the individualization requirements of customers increasing gradually,the ceramic industry has been transferred from the traditional mass standardized production to the production mode of multiple species with small quantity and personalized customization,which has caused the frequent switching of orders,thus making it rather difficult to make the ceramic production scheduling with a serious waste of energy.Through making the deep analysis of the production processes and energy consumption influence factors of the polishing engineering of architectural ceramics,the thesis has clarified the problems and characteristics of the energy efficiency optimization scheduling in the production mode of individual customization.Firstly,with the various kinds of the polishing tiles,there is a great difference in the equipment of each polishing production line and the parameters of the different machines in the production line are different when each kind of polishing tiles are processed,which makes it difficult to accurately predict the energy consumption of the machines in processing each kind of polishing tiles.Secondly,in the situation of various orders of the polishing tiles,the adjustment time is very small or even could be negligible when the orders with large similarity are processed on the same machine,However,the adjustment time of the orders with small similarity is rather large,in which the orders with large similarity should be grouped together to make the mass production in order to reduce adjustment time of the equipment.At last,because each order has a corresponding delivery time limit and the orders arranged into the groups will extend the delivery time of some orders,the factor of delivery time must be considered in arranging the operating order of the orders.And the thesis has made the following work in order to solve the above problems.(1)According to the problem of predicting the energy consumption of polishing production line’s processing each kind of polishing tiles,the energy consumption of polishing production line has been be defined as the function of processing parameters of feed speed and motor speed of the ceramic tiles and so on.What’s more,a data model of energy consumption prediction based on the RBF neural network has been proposed to support the energy consumption prediction in the polishing scheduling process of the architectural ceramics.(2)In order to solve the clustering group problem of orders,the fuzzy C-means clustering algorithm has been applied to make the clustering analysis of the orders of polished tiles according to the characteristics of the material and specification of them and so on.Through a specific example of the order similarity clustering analysis,which will be more varieties of small batch production mode,the value of the attribute to achieve the value of the processing,and then set the similarity degree of the order under each group,which lays the foundation for setting the adjustment time in the simulation experiment of energy efficiency optimization dispatching.(3)According to the scheduling problem of the orders after grouping allocated to each polishing machine to make the lowest cost of production,the scheduling problem of the energy efficiency optimization after grouping will be described firstly in this thesis to establish energy efficiency optimization model taking the tardiness penalty cost and energy consumption cost as the goals.Then the model of energy efficiency optimization is solved by integratedly using the ordinary genetic algorithm and the heuristic rule algorithm of HEDDLAT and HEDDLPE.And the ordering of the orders in the same group and the operating strategy of the different order groups on the machines will be determined.At last,the feasibility and reliability of the models will be verified by the simulation experiments.In addition,the Java language has been applied to develop the scheduling module of energy efficiency optimization based on the above researches,which will be then integrated into the energy management system.