Design and application of data driven economic model predictive control for a rotational molding process

The present manuscript addresses the problem of economically achieving a user specified set of product qualities in an industrial complex batch process, illustrated through a lab-scale uni-axial rotational molding (also known as Rotomolding) setup. To this end, a data driven Economic MPC (EMPC) formulation is developed and implemented to achieve product specification via constraints on the predicted quality variables. First, a state-space dynamic model of the rotomolding process is built using previous batch data generated in the lab using uni-axial rotomolding setup. The dynamic model captures the internal mold temperature trajectory for an input sequence (combination of two heaters and compressed air). This model is then supplemented by a partial-least-squares quality model, which relates with key quality variables (sinkhole area and impact energy) with the terminal (states) prediction. The complete model is then placed within the EMPC scheme which minimizes the cost associated with inputs and allows the user to specify required product quality via constraints on the quality variables. Results achieved from experimental studies illustrate the capability of the proposed EMPC scheme in lowering the process cost (energy requirements) for two manifestations of the economic objective.