(716d) Smart Manufacturing for Monitoring and Control of an Industrial Air Separation Unit | AIChE

(716d) Smart Manufacturing for Monitoring and Control of an Industrial Air Separation Unit


Ganesh, H. S., McKetta Department of Chemical Engineering, The University of Texas at Austin
Avraamidou, S., Texas A&M University
Cao, Y., McMaster University
Leyland, S., Process Systems Enterprise
Pistikopoulos, E. N., Texas A&M Energy Institute, Texas A&M University
Wang, Y., Linde PLC
Air separation via cryogenic distillation is the basis for most industrially produced air products such as pure oxygen, nitrogen, and argon. These products are heavily used in the chemical industries for inert blanket gases in process vessels and as feedstocks. Air Separation Units (ASU) typically involves cryogenic distillation processes and highly interconnected recycle loops to recover cooling capacity. This process is highly complex and energy intensive, with ASUs in the US accounting for approximately 2% of all electrical energy consumption of the US manufacturing sector [1]. A 1% improvement in the energy efficiency of ASU plants leads to multimillion dollars per year savings industry-wide.

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 [2] is followed for the development of multiparametric Model Predictive Controllers (mpMPC)[3]. Firstly, a high-fidelity model is developed, based on [4], from thermophysical relations to recreate the real-life ASU’s 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 [5]. 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 [5]. 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.


[1] United States Energy Information Administration, 2014, “Consumption of Energy for All purposes”, EIA, Washington, DC, accessed April 28, 2020,https://www.eia.gov/consumption/manufacturing/data/2014/#r2

[2] Pistikopoulos, E. N.; Diangelakis, N. A.; Oberdieck, R.; Papathanasiou, M.; Nascu I.; Muxin S.; PARAC – An integrated framework and software platform for the optimization and advanced model-based control of process systems Chemical Engineering Science 2015, 136, 115-138.

[3] Pistikopoulos E. N. Perspectives in multiparametric programming and explicit model predictive control. AIChE Journal 2009, 55 (8), 1918-1925.

[4] Yanan C.; Swartz C.L.E.; Flores-Cerillo J.; Jingran M.; Dynamic modeling and collocation-based model reduction of cryogenic air separation units. AIChE Process Systems Engineering 2016, 62, 1602-1615.

[5] Oberdieck, R.; Diangelakis, N. A.; Pistikopoulos, E. N. Explicit Model Predictive Control: A connected-graph approach. Automatica 2017, 76, 103-112.