By Dragoljub II Gajic
Stainless steel plays a very important role in the economic development and its consumption per capita has been considered as an index of relative prosperity of a community. Stainless steel is also recognized as a reference for sustainable development because it is 100% recyclable at the end of its life. On the other hand the stainless steel industry is among the most energy-intensive industries where energy has a major share of operating costs and energy savings initiatives play very important role from both economic and environmental point of view.
In the last few years the stainless steel market has become highly competitive mainly due to a sudden growth in the Chinese economy. As a result, the highest priority of the latest research work on stainless steel production has been to identify the most efficient optimization techniques to produce a given quantity of stainless steel at the lowest possible cost in an integrated stainless steel plant. Stainless steel coil products emerge from the melt shop, the hot rolling, the cold rolling and the finishing processes, in that order. Optimized scheduling is required for all these steps to operate efficiently, but it is too complicated to create schedules for all of these processes at once.
We develop and apply in real case studies an LP- and MILP-based methodology to handle each process independently [1, 2] and later on an appropriate coordination strategy because the operational efficiency of downstream processes needs to be considered while scheduling upstream processes, and the scheduling results of the upstream processes are required as input to the downstream processes. We call such an approach end-to-end production scheduling optimization since at first we optimally schedule each process starting from downstream moving upstream and then readjust the schedule of the downstream processes again using both the upstream schedules and the energy prices as inputs [3, 4]. Apart from reduced energy costs and thus lower primary energy consumption and greenhouse gases emissions we also achieve energy savings directly within the processes by minimization of the production make span that is an objective function during both melt shop and hot rolling mill scheduling optimization.
1. I. Harjunkoski, I. E. Grossmann, A Decomposition Approach for the Scheduling of a Steel Plant Production, Computers and Chemical Engineering, 2001.
2. M. Biondi, S. Saliba, I. Harjunkoski, Production optimization and scheduling in a steel plant: hot rolling mill, 18th IFAC World Congress, 2011.
3. H. Hadera, I. Harjunkoski, Continuous-time batch scheduling approach for optimizing electricity consumption cost, ESCAPE 23, 2013.
4. S. Mitra, D. Gajic, Case study on generic discrete-time modelling approach for stainless steel operations under time-sensitive electricity prices, CAPD & TK-AST, 2013.