Improving the load flexibility of industrial air separation units using a pressure‐driven digital twin

Abstract

Air separation units are one of the prime examples for studies on demand side management and (non-)linear model predictive control due to their high power consumption and energy storage potential. These plants separate ambient air into its main components, nitrogen, oxygen, and argon, by means of cryogenic distillation at different pressure levels. Approximately two thirds of the industrially operated air separation units consider the separation of argon either as a value product or for reasons of energy efficiency. However, most of the studies in literature neglect the separation of argon since this requires additional equipment, increases the heat and process integration and, thus, the complexity of process control. In this work, a digital twin of an air separation unit with argon system is used to analyze and to improve load change procedures. Moreover, the potential of applying the digital twin as a soft sensor is demonstrated.

Reduced-Order Modeling and Control of Heat-Integrated Air Separation Column Based on Nonlinear Wave Theory

“…The cryogenic air separation is a primary approach for obtaining high-purity gases like nitrogen, oxygen, and argon. The raw material for cryogenic air separation units is atmospheric air, and their primary energy consumption comes from electricity, accounting for over 70% of the production cost [4][5][6][7]. Particularly, as environmental concerns intensify, the demand for energy efficiency and emission reduction is increasing [8,9].…”

Integrating process and power grid models for optimal design and demand response operation of giga‐scale green hydrogen

“…13,14 This represents a potential win-win for the power grid and manufacturer, serving as a form of economic incentive for helping to balance grid demand. Numerous recent works study demand-side management of other electrified industrial processes (see recent perspectives by Billings and Powell, 15 Cegla et al 16 ), including air separation, [17][18][19] chlor-akali electrolysis, 20,21 steel manufacturing, 22 zeolite crystallization, 23 and copper production. 24 Demand response schedules are typically computed using dynamic 25,26 or static 27 surrogate models, given an electricity price forecast, by solving a dynamic or (mixed-integer) linear optimization problem.…”

Hazard Analysis of Fixed‐Tube‐Sheet Shell‐and‐Tube Heat Exchangers

“…Kender et al [13] focus on the smart operation of complete process plants by developing a digital twin for air separation units. These digital twin concepts are used in several case studies by Klein et al [14,15] and Kender et al [47].…”