Application of Economic Model Predictive Control on a Lab Scale Rotomolding Process

The problem of economically achieving a user specified set of product qualities in an industrial batch process is presented in the current manuscript, demonstrated using a lab-scale uni-axial rotational molding process. To achieve a product with specified qualities, a data driven Economic model predictive control (EMPC) formulation is proposed through constraints on quality variables. A state-space model of the rotational molding process is first identified from previously generated data in the lab. The evolution of the internal mold temperature for a given set of input moves (combination of two heaters and compressed air) is captured by the state space model. Further, this model is augmented with a partial-least-squares based quality model, which relates the terminal (states) prediction with key quality variables (sinkhole area and impact energy). This augmented model is then integrated within the EMPC scheme that penalizes excessive energy consumption while aiming to achieve on-spec products via constraints on the quality variables. Results obtained from experimental studies illustrates the capability of the proposed EMPC scheme in lowering the process cost (energy requirements) while achieving user specified product.