Optimal Scheduling For Annealing Process And Prediction For Byproduct Gas System In Steel Industry And Their Applications

Steel industry is an important sector of national economy. The key issues to the development of iron and steel enterprises are about increasing the productivity, utilizing resources reasonably and reducing environmental pollution. It is well known that production scheduling is an effective way to make economic profits and reduce the production costs. Also, precisely forecasting energy generation and consumption can reduce energy waste and environment pollution. Based on our work concerning National High-Tech Research and Development Programs, this dissertation studies the optimal scheduling problem for bell-type batch annealing process and the prediction problem for generation and consumption of byproduct gas system. The research is mainly carried out at the Shanghai Baosteel Co. Ltd. The dissertation has mainly carried on the following research.Analyzing the scheduling complexity of bell-type batch annealing process and the feasibility to solve a discrete event system simulation by using SystemC originally treated as a simulation platform for designing very large scale integrated circuit, a discrete event simulation modeling based on SystemC for the batch annealing shop is proposed. The modeling method simulates the batch annealing process in a quick yet effective way. An optimal method of bell-type batch annealing production, combined with an adaptive genetic algorithm, is put forward in this dissertation.For the energy prediction problem, a time series forecasting method based on improved echo state network is proposed. This method can be used to establish a direct relationship between the prediction origin and prediction horizon, which can avoid the iteration error accumulation and guarantee the stability of the predictor in advance, rather than relying on the network training results. Also, instead of using linear regression, Gaussian process is adopted to obtain the relationship between the reservoir state and network output, thereby preventing the ill conditioned reservoir state matrix form occuring. This way, not only a better prediction result but also an accurate probability estimation of the result is achieved.The real-time prediction method is investigated for the generation and consumption of byproduct gas system in steel industry. Since the practical flow data in process control system typically includes a variety of noises, a noise reduction method is proposed based on the empirical mode decomposition. The main idea of this method is to decompose the time series signal into a group of independent intrinsic mode functions, and those with small-scale are de-noised by filter with adaptive threshold. Based on such method, a time series prediction procedure for gas generation and consumption of non-adjustable users is presented by exploiting an improved echo state network, where the network parameters are optimized based on the least mean square error criterion, and the output weights are obtained by singular value decomposition. In addition, an average method is used to predict consumption of adjustable users and converted users. Finally, the real production data from Baosteel are used to verify the proposed approach. The running results are shown to be satisfactory.Based on the reported above, the prediction software system for byproduct gas system is developed be resorting to the software engineering method. The application in Shanghai Baosteel Energy Center shows that the system can accurately predict the variation of gas generation and consumption, as well as the level of gasholder. The results demonstrate that the system exhibits high accuracy and provides with a rational guidance for balancing and scheduling of the byproduct gas system, which is capable of saving energy and reducing the complexity of scheduling workers.