A reactive-iterative optimization algorithm for scheduling of air separation units under uncertainty in electricity prices

Basán, Natalia Paola; Cóccola, Mariana Evangelina; Dondo, Rodolfo Gabriel; Guarnaschelli, Armando; Schweickardt, Gustavo Alejandro; Méndez, Carlos A.

doi:10.1016/j.compchemeng.2020.107050

Cited by 15 publications

(9 citation statements)

References 40 publications

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“…However, only N 2 and O 2 are considered as products. Current surrogate models are more complex as their data basis is either derived from historical plant data 10,12–15 or from steady‐state simulation of various operating points 13–23 . Here, dynamic plant behavior is represented by optimization constraints, for example, the maximum load change velocity 10,13,14,16–19 .…”

Section: Introductionmentioning

confidence: 99%

“…more complex as their data basis is either derived from historical plant data 10,[12][13][14][15] or from steady-state simulation of various operating points. [13][14][15][16][17][18][19][20][21][22][23] Here, dynamic plant behavior is represented by optimization constraints, for example, the maximum load change velocity. 10,13,14,[16][17][18][19] The main focus of literature is on less complex plant topologies such as O 2 11,16-18,23 or N 2 plants.…”

mentioning

confidence: 99%

“…10,13,14,[16][17][18][19] The main focus of literature is on less complex plant topologies such as O 2 11,16-18,23 or N 2 plants. 10,15,19,22 Only a few of these studies consider the additional separation of Ar. [12][13][14]20,21 Due to the increasing share of renewable energies in the energy supply, short-term fluctuations in the energy market can be observed.…”

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confidence: 99%

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

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…Production of purified nitrogen, oxygen, and argon in large commercial quantities is realized by separation of air through distillation in an air separation unit (ASU). This unit is the fundamental utility provider for integrated gasification combined cycle plants, metallurgy, and chemical industries 1–4. However, the distillation unit is characterized by relatively low energy efficiency and high energy consumption 3–7.…”

Section: Introductionmentioning

confidence: 99%

“…This unit is the fundamental utility provider for integrated gasification combined cycle plants, metallurgy, and chemical industries 1–4. However, the distillation unit is characterized by relatively low energy efficiency and high energy consumption 3–7. Hence, tremendous research progress has been made to ensure considerable energy is saved while improving the energy efficiency of the ASU.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control of a High‐Purity Internal Thermally Coupled Distillation Column

Zangina

Wang

Qin³

et al. 2021

Chem Eng & Technol

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The energy‐saving potential of the internal thermally coupled air separation column (ITCASC) is well‐established, but distinct dynamic characteristics and control loop interactions make it inflexible to control. To take care of high‐purity ITCASC control complications, a state‐space model predictive control (MPC) was formulated. A direct finite‐horizon control approach was exploited to align the dynamic states with the model predictions. MPC‐I and MPC‐II were developed, and further compared to a previous adaptive multivariable generalized prediction control (AM‐GPC). The results obtained show that the control performance of the proposed MPC‐II is superior to that of MPC‐I and AM‐GPC.

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

Tsay,

Qvist

2023

AIChE Journal

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Electrolysis‐based hydrogen production can play a significant role in industrial decarbonization, and its economic competitiveness can be promoted by designing demand response operating schemes. Nevertheless, the scale of industrial supply plants may be significantly large (on the order of gigawatts), meaning that electricity prices cannot be treated as an input for scheduling problems, that is, the “price taker” approach. This article presents a framework for the optimization of a large‐scale, electricity‐powered hydrogen production facility considering its integration with the power grid. Using a computational case study, we present an iterative scheme for integrating the process model with a model for power grid optimization and capacity expansion, taking the popular GenX model as an example.

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A reactive-iterative optimization algorithm for scheduling of air separation units under uncertainty in electricity prices

Cited by 15 publications

References 40 publications

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

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

Model Predictive Control of a High‐Purity Internal Thermally Coupled Distillation Column

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

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