Scheduling and Feed Quality Optimization of Concentrate Raw Materials in the Copper Refining Industry

Scheduling and feed quality optimization for processing solid concentrates in the copper refining industry may be formulated as a large-scale, discrete-time, nonconvex mixed-integer nonlinear program (MINLP) by including logistics operations and ad-hoc blending constraints. However, to solve this complex problem, the full space MINLP for the blending of solid concentrates of copper and the scheduling of their logistics is partitioned into a mixed-integer linear program (MILP) and a nonlinear program (NLP). The solution strategy considers the relax-and-fix rolling horizon with nearby time window overlaps and the use of multiple MILP solutions applied in a two-step MILP-NLP procedure. Two models are proposed for the flowsheet balances: a split fraction model and a process network model. The results indicate that the split fraction model yields near optimal solutions with a large computational effort, whereas the process network can generate several feasible solutions faster. We present a motivating example and an industrial problem with MILP to NLP gaps close to 0%.