(716d) Smart Manufacturing for Monitoring and Control of an Industrial Air Separation Unit
AIChE Annual Meeting
Thursday, November 19, 2020 - 8:45am to 9:00am
In this work, we apply smart manufacturing techniques for the online monitoring and optimal operation of industrial scale ASUs. Specifically, this work focuses on the development of model-based methodologies, tools, and platforms for Real Time Optimization. The approach aims (i) to create a high-fidelity model of a real-life ASU and (ii) provide implementable policies and controls on the real-life unit. PAROC framework  is followed for the development of multiparametric Model Predictive Controllers (mpMPC). Firstly, a high-fidelity model is developed, based on , from thermophysical relations to recreate the real-life ASUâs behaviour. Since the high-fidelity model is not suitable for fast simulation, optimization, or model-based advanced control purposes, we create reduced order control-oriented models. The derived reduced order models are then embedded into an MPC formulation to develop mpMPCs for the whole process through multiparametric programming. The developed controllers reduce the online burden of solving the optimal control problem by solving the entire map of solutions offline . This leads to a significant reduction of online computational time and expands the applications of MPC approaches to more complex systems that would otherwise be constrained computationally, such as RTO of ASUs . We then close the loop by deploying the developed controllers on the detailed high-fidelity model for tuning with prospects of employing them on the real industrial plant. The process operation under RTO and mpMPC showed improvement in energy efficiency without violating constraints on inputs, outputs, process, or quality constraints.
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