SIA OpenIR  > 数字工厂研究室
Alternative TitleResearch on byproduct gas generation and consumption forecasting and optimization scheduling in iron and steel plant
Thesis Advisor胡静涛
Keyword副产煤气系统 预测 极限学习机 优化调度 不确定性
Degree Discipline检测技术与自动化装置
Degree Name博士
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳


Other Abstract

Iron and steel industry is a pillar industry of national economy. During its high economy-consuming, energy conservation and emission reduction of iron and steel enterprises is of great significance to the country for achieving green development. In the iron and steel production process, by-product gas is produced and consumed by many reactors and it plays an important role as a secondary energy source. The efficient use of by-product gas is crucial to the energy conservation and consumption reduction of iron and steel enterprises. Therefore, by considering the complexity and large fluctuation of by-product gas system, our research focus on by-product gas production and consumption prediction, both in point prediction and interval prediction. On the other hand, we are also engaged in scheduling optimization in view of prediction uncertainty. The specific contents of relevant theoretical and practical studies are as follows: (1) Research on byproduct gas generation and consumption forecasting method considering the change of working conditions. By-product gas production and consumption can be greatly affected by the change of working conditions. The accuracy of the prediction model can be reduced when working conditions change. To solve this problem, an online prediction method based on adaptive forgetting factor extreme learning machine is proposed. Parameters of the model are adjusted by the online feedback of the prediction error. This leads to high adaptability to the change of working conditions of the prediction model. Experiments on industrial data show that the proposed method improves the prediction accuracy of gas production and consumption. Besides, data missing is inevitable in the by-product gas flow data, which can affect prediction effect. To solve the problem, the missing values imputation method based on fuzzy C-means clustering is introduced to our work. Parameters of FCM are optimized by the particle swarm optimization method. During the optimization process, extreme learning machine is employed to compute reference values. By combining the supervised and the unsupervised learning methods, data cleaning can be performed. (2) Research on for byproduct gas generation and consumption interval prediction method considering the prediction deviation. To quantify the uncertainties of byproduct gas forecasting, an interval prediction model based on twin extreme learning machine is proposed. As a result, the upper and lower bounds of the model of the interval are directly predicted. Considering the interval deviation information, a comprehensive evaluation index of the new prediction interval is given and used as the optimization criterion of the twin extreme learning machine. In addition, in order to improve the training speed and accuracy of the model, a two-step training method based on pre-training is proposed. Compared with the traditional interval prediction method, the proposed model has advantages in simple structure, fast training speed, and high-quality prediction interval. The proposed approach is more suitable for the online prediction and scheduling of the by-product gas production and consumption. (3) Research on fuzzy optimal scheduling method for byproduct gas system considering the uncertainty. To solve the problem of uncertainty in the production and consumption forecast of by-product gas, the influence of the uncertainty of production and consumption on the optimal scheduling of the by-product gas system is analyzed, and a new by-product scheduling model is constructed based on the fuzzy chance constrained programming method. By taking the point prediction and interval prediction information into consideration, the production and consumption of by-product gas can be expressed as a fuzzy variable. Confidence is introduced to transform the fuzzy constraint to a deterministic constraint. Afterwards, mixed integer linear programming is employed to solve the problem. Furthermore, in order to evaluate the risk of the scheduling scheme obtained at different confidence levels, the risk cost measurement method of the by-product gas system is proposed for the first time. It provides an important reference for the dispatcher to select the appropriate level of confidence. The actual case of the iron and steel enterprise shows that the proposed fuzzy optimization scheduling method of the gas system can effectively deal with uncertainties of gas prediction. The proposed method provides an approach to reduce the risk of gas shortage and emission, and also provides a more comprehensive scheduling solution for dispatchers.

Contribution Rank1
Document Type学位论文
Recommended Citation
GB/T 7714
孙雪莹. 钢铁企业副产煤气的产消量预测及优化调度方法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2018.
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