Energy In Steel Industry Research Articles

An Optimization Model Based on Electric Power Generation in Steel Industry

Electric power is an important energy in steel industry. Electricity accounts for roughly 20% to 30% of the gross energy consumption and costs about 10% of the gross cost of energy. In this paper, under the premise of ensuring the stability of energy supply and the normal production safety, the mathematical programming method and the dynamic mathematical optimization model were used to set up the surplus gas in the optimal allocation among the buffer users and steam production dispatching for the production equipment. The application of this optimization model can effectively improve the energy efficiency and the accuracy of power generation, making full use of secondary energy and residual heat. It also can realize the rationalization of the electricity production structure optimization which can effectively reduce the flare of the gas and steam on one hand, and save energy and decrease production cost on the other.

Online Parameter Optimization-Based Prediction for Converter Gas System by Parallel Strategies

Linz Donawitz converter gas (LDG) is one of the most important sources of fuel energy in steel industry, whose reasonable use plays a crucial role in energy saving and environment protection. In practice, online prediction of variation of gas holder level and gas demand by users is fundamental to gas utilization and scheduling activities. In this study, a least square support vector machine-based prediction model combined with the parallel strategies is proposed, in which parameter optimization is realized online by a parallel particle swarm optimization and a parallelized validation method, both being implemented with the use of a graphic processing unit. The experiments demonstrate that the online parameter optimization based model greatly improves the prediction quality compared to the version with the fixed modeling parameters. Furthermore, the parallelized strategies largely reduce the computational cost thus guaranteeing the real-time effectiveness of the practical application.

An optimal method for prediction and adjustment on byproduct gas holder in steel industry

To maintain the balance of byproduct gas holder is an important task in optimal scheduling of byproduct energy in steel industry. However, this is often influenced by many factors and is difficult to obtain a precise mechanism model for analysis. In this paper, an optimal method for prediction and adjustment on byproduct gas holder is proposed. Considering the different operation styles of gasholders, both single and multiple gasholders level prediction models are established by machine learning methodology. And, a hybrid parameter optimization algorithm is developed to optimize the model for high prediction accuracy. Then, based on the predicted gasholder level, the optimal adjustment amount is calculated by a novel reasoning method to sustain the gasholder within safety zone. This method has been verified in the Energy Center of Baosteel, China. The results demonstrate that the proposed approach can precisely predict and adjust gasholders and provide a remarkable guidance for reasonable scheduling of byproduct gases.

Calculating Method for Influence of Material Flow on Energy Consumption in Steel Manufacturing Process

From the viewpoint of systems energy conservation, the influences of material flow on its energy consumption in a steel manufacturing process is an important subject. The quantitative analysis of the relationship between material flow and the energy intensity is useful to save energy in steel industry. Based on the concept of standard material flow diagram, all possible situations of ferric material flow in steel manufacturing process are analyzed. The expressions of the influence of material flow deviated from standard material flow diagram on energy consumption are put forward.