Improving profitability of continuous processes facing raw material variability through data-driven SMB-PLS model-based adaptive control

Reducing the impact of lot-to-lot raw material variability through optimization of operating conditions is key when the lots are already purchased, and available in inventory. The objective of this paper is to provide a framework to optimize operating conditions to maximize profitability while aiming at achieving product quality targets each time a new lot of raw material is fed to a continuous process. The proposed approach consists of solving an optimization problem in the latent space of a sequential multi-block partial least square model (SMBPLS). Model updating and closed-loop operation are considered to overcome parametric disturbances. The approach is illustrated using a simulated grinding-flotation plant for a sequence of ore lots with variable properties. The case study shows that optimizing operating conditions with the proposed approach allows increasing biannual gain by 1.5 to 2 % compared to nominal operation. This represents between 59 and 75 % of the true achievable gain.