(149o) Data Driven Economic Model Predictive Control of a Rotational Molding Process

Batch processes are commonly found in many domains including chemical, mechanical, biochemical, agriculture and pharmaceutical industries due to the requirement of producing high-value products. Since the productions amount is small in batch process, it is important to maintain consistency in obtaining products with excellent quality and this can be only achieved by deploying a suitable control strategy. Model predictive controller (MPC) is a standard control technology preferred in the industries due to its ability to predict the future of the process and come up with an optimal input sequence subject to any sort of constraints [1,2,3]. Rotational molding, or simply rotomolding, is one such batch process, used in industries for fabricating hollow plastics. Production of these items can be made profitable by using optimal control to significantly reduce the waste costs and in order to develop such a strategy one must focus on the modelling aspect associated with the same, especially for this process the quality variables which have to be tracked are not measurable and thus not available during the 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.

References:

1. Bonvin, D. Optimal operation of batch reactors—a personal view. Journal of Process Control 1998, 8, 355–368.

2. Flores-Cerrillo, J.; MacGregor, J. F. Control of particle size distributions in emulsion semibatch polymerization using mid-course correction policies. Industrial and Engineering Chemistry Research 2002, 41, 1805–1814

3. Mhaskar, P.; Garg, A.; Corbett, B. Modeling and Control of Batch Processes; Advances in Industrial Control; Springer International Publishing: Cham, 2019.