(100a) Integration of Smart Manufacturing in the Advancement of Polymer Processing | AIChE

(100a) Integration of Smart Manufacturing in the Advancement of Polymer Processing


Pistikopoulos, E. N. - Presenter, Texas A&M Energy Institute, Texas A&M University
Ogumerem, G. S., Texas A&M University
Burnak, B., Texas A&M University
In lieu of improving efficiency and embracing smart manufacturing technologies, the U.S. Department of Energy (DOE) has instituted the Clean Energy Smart Manufacturing Innovation Institute (CESMII), with Texas A&M Energy Institute leading the Gulf Coast Regional Manufacturing Center. The goals of CESMII are to (1) demonstrate at least 15% improvement in energy efficiency in real plant demonstration projects within five years, supporting a later goal of 50% improvement in energy productivity in 10 years; and (2) reduce the cost of deploying Smart Manufacturing initiatives to processing plant by 50% through the development tools and technologies. These includes developing platforms that enables machine-to-plant-to-enterprise real time sensing, instrumentation, monitoring, optimization, and control of energy. The idea of Smart Manufacturing integrates our expertise in high fidelity or advanced modeling of industrial operation, big data analytics, real time optimization and leverages high performance computing to achieve a seamless development of optimal processes and energy reduction.

The most recent advances in sampling technology and sensor development has enabled continuous online monitoring of polymerization reactions, leading to an opportunity for the application of advanced controllers. In this work, we develop model based advanced control strategies for batch polyolefin reactions via the PARametric Optimization and Control (PAROC) framework. Based on accurate linear representations of an initial high fidelity model, we formulate a Model Predictive Control (MPC) scheme that delivers a trajectory tracking problem. We solve the multi-parametric counterpart of the MPC problem (mpMPC) to determine the explicit expressions of the optimal control actions. Hence, the online computational burden of the optimization problem is reduced to a simple lookup table and affine function evaluation problem. Such an alleviation in the computational cost and ease of application allows for an opportunity for the integration of the control strategies in the Smart Manufacturing cloud platform to be used by industrial partners.