An Implicit and Explicit Dual Model Predictive Control Formulation for a Steel Recycling Process

TL;DR

This paper introduces implicit and explicit dual model predictive control methods for optimizing steel recycling, balancing cost and pollutant uncertainty reduction through novel control formulations.

Contribution

The paper develops and compares implicit and explicit dual MPC formulations that explicitly incorporate uncertainty management in steel recycling process optimization.

Findings

Dual MPC formulations outperform non-dual approaches.

Explicit dual MPC encourages active exploration of uncertainties.

Implicit dual MPC reduces uncertainty indirectly through constraints.

Abstract

We present a formulation for both implicit and explicit dual model predictive control for a steel recycling process. The process consists in the production of new steel by choosing a combination of several different steel scraps with unknown pollutant content. The pollutant content can only be measured after a scrap combination is molten, allowing for inference on the pollutants in the different scrap heaps. The production cost should be minimized while ensuring high quality of the product through constraining the maximum amount of pollutant. The dual control formulation allows to achieve the optimal explore-exploit trade-off between uncertainty reduction and cost minimization for the examined problem. Specifically, the dual effect is obtained by considering the dependence of the future pollutant uncertainties on the scrap selection in the predictions. The implicit formulation promotes uncertainty reduction indirectly via the impact of active constraints on the objective, while the explicit formulation adds a heuristic cost on uncertainty to encourage active exploration. We compare the formulations by numerical simulations of a simplified but representative industrial steel recycling process. The results demonstrate the superiority of the two dual formulations with respect to a robustified but non-dual formulation.