Steel Production Process Energy Forecasting And Scheduling Optimization

Steel production process is a typical high temperature, discrete and continuous mixing process of physical and chemical changes, with multi-factor, multi-process, multi-position, strong coupling and nonlinear characteristics. High energy consumption is the main problem in the process of steel smelting production. Energy conservation and consumption is conducive not only to reduce production costs, but also to achieve a low-carbon processing purpose. Prediction of energy consumption in iron and steel production logistics can provide support for the iron and steel enterprises to formulate an energy overall plan; In the process of steel production, rational and efficient production scheduling has an extremely important significance for reducing energy and material consumption, improving product quality, accelerating the production cycle and reducing production costs; In addition, it plays an important role to online testing, failures detecting, avoid wasting and save energy and material consumption cost in the process of steel production. This dissertation covers research on prediction of logistics energy consumption in the process of steel production, scheduling optimization in production process, failures prediction in production process and so on. Mainly including the following three aspects:(1) For the issues, such as it is difficult to establish energy forecasting model and prediction accuracy is low, this dissertation proposes a model of steel production logistics and energy prediction based on ant colony optimization wavelet neural network. At first, we analyze steel production process and the factors that impact production energy, determine the input parameters constitute feature space. Then reconstruct feature space using wavelet transform, model energy consumption prediction with neural network model. Finally optimize the solution process using ant colony algorithm. The energy consumption prediction experiments in iron-making, steel-making and rolling process show that the proposed method has better universality, at the same time it improves the prediction accuracy and provide guidance for the steel enterprises understanding energy needs in advance.(2) For the problems that in the process of steel production, there exists multi-objective, multi-constrained and uncertainty in key process of steelmaking continuous casting, we propose a scheduling optimization algorithm based on genetic particle swarm optimization in steel-making and continuous casting process. Firstly, under the constraint condition of steel-making and continuous casting production process, establish scheduling optimization model to minimize the processing time of the optimization target with this understanding of energy saving, reliable quality and production ordered. Then, utilize the characteristics of fast convergence of particle swarm and global search ability of genetic algorithm to optimize the design and solving parameters. And finally, establish optimal scheduling model. Experimental results indicate that the proposed algorithm is an efficient scheduling optimization method in steel-making and continuous casting production, which is capable of compiling the executable steel-making operation plan to achieve continuous casting. And it can also reduce the loss which was made by the oven waiting time between the various processes; reduce the processing flow time, to reduce energy costs.In order to solve the difficult problems of failures detection or diagnosis in rolling process, we propose a online failure detection model in rolling steel production process based on multi-core learning theory. First, for learning samples, build a nuclear principal component analysis and support vector data description model. And then, preliminary identify the rolling process based on T2, Q statistic and data domain description envelope. Finally, make fine classification for the first results based on multi-core minimum secondary support vector prediction model, and identify the failure categories. We utilize the above model to do experiments for rolling mill furnace failures and testing unit failures, and the results show that the method can effectively detect failures in the process of rolling steel production.