Energy and Carbon Footprint Reduction for Hybrid Heat-Integrated Distillation Systems: Robust Model Predictive Control Approach

Industrial distillation processes consume a significant amount of energy, making control improvements essential to reduce operating costs and minimise environmental impact. This paper presents a robust model predictive control strategy for hybrid heat-integrated distillation systems, focusing on the effects of progressive fouling in key process components. The control approach is based on an offset-free implicit tube formulation that accounts for uncertainties arising from fouling on heat-transfer surfaces. A dynamic simulation model is developed to evaluate the proposed control strategy in comparison with conventional proportional-integral-derivative controllers. Two widely used control tasks are considered: setpoint tracking and disturbance rejection. Each task is analysed under four fouling levels corresponding to realistic industrial operating conditions. Results demonstrate that the robust predictive controller improves closed-loop performance, reducing the control error by over 35% and eliminating overshoot under conservative tuning. More aggressive tuning results in faster responses with minimal oscillations. The proposed control method also reduces energy demands, fuel oil consumption, and associated carbon emissions compared to conventional control. This study demonstrates that the proposed robust model predictive control strategy offers an effective solution for maintaining stable and efficient operation in distillation systems, particularly in the presence of fouling.