Abstract
The expenses of pig iron manufacturing stay in a relatively high level due to the large consumption of coke and ore. There are a variety of methods to optimize the ironmaking process, including multi-indexes method, production-oriented approach, and cost control strategy. In order to reduce the production cost, we optimize the raw materials supply schedule of an ironmaking plant with five sintering machines and seven blast furnaces. The ironmaking plant used to fix the feeding system, leading to an inefficient operation and mismatched connection between these two procedures. At first, the physical characteristics of each blast furnace is captured by linear regression model. Then, a self-adaptive genetic algorithm with variable population size is constructed under distribution system. Finally, the improved genetic algorithm is applied to optimize the total production cost represented by the aggregation of seven coke ratios on a cloud computing platform. With the application of this cloud collaborative optimization framework, the mean coke ratio of the ironmaking factory has decreased 13.96kg/t Fe.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.