(537h) Green Operation of an Air Separation Unit Using an Efficient MILP Optimal Scheduling Framework

Kelley, M., The University of Texas at Austin
Baldick, R., The University of Texas at Austin
Baldea, M., The University of Texas at Austin
Greenhouse gas emissions in the United States increased by 7% from 1990 to 2014, and carbon dioxide accounted for 82% of greenhouse gas (GHG) emissions in 2015 [1,2]. Electricity transmission and production accounts for 29% of the greenhouse gas emissions, and industry accounts for 21% [1]. In this context, our work analyzes the possibility of simultaneously lowering the industrial and electricity sector greenhouse gas (particularly, carbon dioxide) emissions by changing the electricity demand profile of electricity-intensive industrial manufacturing plants.

In this context, we define green operation as a strategy for modulating plant production rates in a manner that favors electricity consumption at times when the contribution of renewable sources to the energy generation mix is maximized. Specifically, we postulate a general production scheduling framework that utilizes information from the power grid (and the instantaneous contribution of e.g. wind and solar generation) and demand data to, i) raise production levels when renewable generation is high, generating product in excess of demand, ii) store the excess product, and iii) lower production rates when non-renewable (i.e., fossil fuel-based) power generation increases. In the latter case, stored products are used to meet demand. In order to account for significant daily fluctuations in the rate of power generation by renewables, the scheduling framework relies on dynamic models of the manufacturing process.

We illustrate this framework by performing optimal production scheduling calculations for greener operation of an air separation unit (ASU) as a prototypical electricity-intensive chemical process. ASUs are typically large plants, with dominant time constants in the order of hours. As a consequence, production rate changes are scheduled over hourly intervals, under a set of previously derived [3,4] dynamic constraints. The overall scheduling problem is posed as a Mixed Integer Linear Program (MILP).

An extensive computational case study reveals that green operation can significantly reduce CO2 emissions without jeopardizing the production process. The cost of green operation is compared with conventional demand-response scheduling (which is typical in the interaction of ASUs with the power grid). Our study reveals new potential strategies and electricity pricing schemes that can be used to incentivize greener operation by the chemical industry.

[1] EPA: Overview of Greenhouse Gases. (2018). Retrieved October 4, 2018, from https://www.epa.gov/ghgemissions/overview-greenhouse-gases

[2] IPCC (Intergovernmental Panel on Climate Change). 2013. Climate change 2013: The physical science basis. Working Group I contribution to the IPCC Fifth Assessment Report. Cambridge, United Kingdom: Cambridge University Press.www.ipcc.ch/report/ar5/wg1.

[3] Kelley, M. T., Pattison, R. C., Baldick, R., & Baldea, M. An Effcient MILP Modeling Framework for Demand Response Operation of Air Separation Units. Applied Energy, Accepted.

[4] Pattison, R. C., Touretzky, C. R., Johansson, T., Harjunkoski, I., & Baldea, M. (2016). Optimal Process Operations in Fast-Changing Electricity Markets: Framework for Scheduling with Low-Order Dynamic Models and an Air Separation Application. Industrial & Engineering Chemistry Research, 55(16), 4562–4584. https://doi.org/10.1021/acs.iecr.5b03499