Date of Award
Master of Engineering (ME)
Tom E. Marlin
Christopher L.E. Swartz
A centralized optimization strategy is proposed to determine optimal raw material purchasing and plant operation practices as applied to the steel processing industry. Primary steelmaking can be separated into three sub-areas: cokemaking, ironmaking, and steelmaking. Raw materials are purchased on the open market for each area and include coal, iron ore pellets, and scrap steel. Many raw material vendors exist, providing products varying in quality and price. Additionally, the processing of raw materials within each area has an impact on its neighbours and, therefore, it is desired to determine the least costly method of both purchasing and processing the raw materials to make steel of acceptable quality.
This work studies the modeling of primary steelmaking using a combination of mass balances and empirical relationships. The model, in addition to process constraints, is combined with a cost objective function and solved using a mixed-integer nonlinear programming (MINLP) solver. Various case studies are shown that illustrate the strong connection between the cokemaking and ironmaking as the carbon, volatile matter, and phosphorous contents of the coals and pellets have a large impact raw material selection. The centralized optimization results are then compared to the classic decentralized approach showing a clear reduction in cost.
Raw material uncertainty is incorporated using two-stage stochastic programming. The formulation considers numerous raw material quality scenarios and the optimizer is required to make purchasing decisions based on the probability of each scenario occurring. The results indicate that by making a slightly more expensive raw material purchase, the frequency of constraint violation during processing can be significantly reduced.
Multi-period optimization is also studied to determine how multi-tiered raw material pricing affects purchasing decisions. Steel demand forecasting is combined with the Multi-period formulation to make planning decisions over an entire year. Case studies are provided that illustrate how multi-tiered pricing can significantly change the slate of raw materials purchased. A rolling horizon optimization approach is then incorporated to determine how decisions change throughout the year in the face of errors in demand forecasting.
Gerardi, David, "Integrated Plant Modeling and Optimization Under Uncertainty" (2010). Open Access Dissertations and Theses. Paper 4996.
McMaster University Library