Dynamic analysis and linear model predictive control for operational flexibility of post-combustion CO2 capture processes

Jung, Howoun; Im, Dasom; Heo, Seongmin; Kim, Boeun; Lee, Jay H.

doi:10.1016/j.compchemeng.2020.106968

Cited by 20 publications

(5 citation statements)

References 24 publications

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“…The energy requirement for the regeneration step may fluctuate significantly, especially in the post-combustion CO 2 capture plant, as the power demands may vary during peak hours. As a result, the reboiler heat duty is also changed with high amplitudes …”

Section: Resultsmentioning

confidence: 99%

“…The control strategy should be flexible enough to deal with such abrupt changes to maintain output variables at their setpoints . Therefore, the Fast MPC control strategy performance is evaluated under a large step change of ±15% at 60 s in Y 2 as the stripper temperature fluctuates with high amplitude, a possible scenario during operation due to changing power plant energy requirements Figure and show the responses of Y 2 , U 2 , Y 1 , and U 1 for Fast MPC 1 and 2 when there is a step change of +15% in Y 2 .…”

Section: Resultsmentioning

confidence: 99%

“…As a result, the reboiler heat duty is also changed with high amplitudes. 54 As the CO 2 absorption/stripping system is highly integrated, a change in one part of the plant affects the rest of the system as well due to the process interaction. Hence, when a step change is introduced in the output variable Y 1 , the output variable Y 2 is also disturbed from its nominal value.…”

Section: Fast Mpc Resultsmentioning

confidence: 99%

“…58 Therefore, the Fast MPC control strategy performance is evaluated under a large step change of ±15% at 60 s in Y 2 as the stripper temperature fluctuates with high amplitude, a possible scenario during operation due to changing power plant energy requirements. 54 The Fast MPC is required to achieve the desired new setpoint value of 0.3 for Y 2 . It can be observed that Fast MPC 1 controller is able to achieve the new setpoint successfully with negligible offsets.…”

Section: Controller Performance Parameters For ±5%mentioning

confidence: 99%

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Model Analysis for the Implementation of a Fast Model Predictive Control Scheme on the Absorption/Stripping CO₂ Capture Plants

et al. 2022

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The purpose of this paper is to investigate the possible implementation of the Fast model predictive control (MPC) scheme for chemical systems. Due to the difficulties associated with complicated dynamic behavior and model sensitivity, which results in considerable offsets, the Fast MPC controller has not been implemented on the CO 2 capture plant based on the absorption/stripping system. The main objective of this work is to evaluate the most appropriate model for implementing the Fast MPC control strategy, which results in fast output responses, negligible offsets, and minimum errors. The steady-state and dynamic simulation models of the CO 2 capture plant are designed in Aspen PLUS. In the System Identification Toolbox, multiple state-space models are identified to achieve a highly accurate model for the Fast MPC controller. The Fast MPC controller is then implemented to evaluate the performance under a setpoint tracking mode with ±5 and ±15% step changes. The results showed that the Fast MPC based on the statespace prediction focus model has on average 7.9 times lower offset than the simulation focus model and 10.4 times lower integral absolute error values. The comparison study concluded that the Fast MPC control strategy performs efficiently using prediction-based focus state-space models for CO 2 capture plants using the absorption/stripping system with minimum offsets and errors.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Fast Mpc Resultsmentioning

confidence: 99%

Section: Controller Performance Parameters For ±5%mentioning

confidence: 99%

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Model Analysis for the Implementation of a Fast Model Predictive Control Scheme on the Absorption/Stripping CO₂ Capture Plants

et al. 2022

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“…Possible technologies for CO 2 separation would differ in the ramping speed. For instance, membrane gas separation and PSA allow fast ramping [11,23] while chemical absorption [15] takes longer time to change the operation level. In order to investigate how much the maximal ramping speed affects the optimal operation as well as the cost savings, we assume different values of T Ramp (one to three hours).…”

Section: Case Studymentioning

confidence: 99%

Flexible operation of modular electrochemical CO2 reduction processes

Roh

Brée

Schäfer

et al. 2021

Preprint

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Electrochemical CO2 reduction (eCO2R) is an emerging technology that is capable of producing various organic chemicals from CO2, but its high electricity cost is a big economic obstacle. One solution to reduce the cumulative electricity cost is demand side management, i.e., to adjust the power load based on time-variant electricity prices. However, varying the power load of CO2-electrolyzers often leads to changes in Faraday efficiency towards target components and thereby influences the product composition. Such deviations from the target product composition may be undesired for downstream processes. We tackle this challenge by proposing a flexible operating scheme for a modular eCO2R process. We formulate the economically optimal operation of an eCO2R process with multiple electrolyzer stacks as a parallel-machine scheduling problem. Adjusting the power load of each sub-process properly, we can save electricity costs while the desired product composition is met at any time. We apply an algorithm based on wavelet transform to solve the resulting large-scale nonlinear scheduling problem in tractable time. We solve each optimization problem with a deterministic global optimization software MAiNGO. We examine flexible operation of a modular eCO2R process for syngas production. The case studies show that the modular structure enables savings in the cumulative electricity cost of the eCO2R process via flexible operation while deviations in the syngas composition could be reduced. Also, the maximum ramping speed of the entire process is found to be a key parameter that strongly influences the cost saving.

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Design of CO2 Capture and Mineralization Systems: Integrated Process Optimization and Controllability Assessment in Parallel Infrastructures

Prousalis¹,

Gkizas

Παπαδόπουλος

et al. 2023

Computer Aided Chemical Engineering

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Dynamic analysis and linear model predictive control for operational flexibility of post-combustion CO2 capture processes

Cited by 20 publications

References 24 publications

Model Analysis for the Implementation of a Fast Model Predictive Control Scheme on the Absorption/Stripping CO₂ Capture Plants

Model Analysis for the Implementation of a Fast Model Predictive Control Scheme on the Absorption/Stripping CO₂ Capture Plants

Flexible operation of modular electrochemical CO2 reduction processes

Design of CO2 Capture and Mineralization Systems: Integrated Process Optimization and Controllability Assessment in Parallel Infrastructures

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Dynamic analysis and linear model predictive control for operational flexibility of post-combustion CO2 capture processes

Cited by 20 publications

References 24 publications

Model Analysis for the Implementation of a Fast Model Predictive Control Scheme on the Absorption/Stripping CO2 Capture Plants

Model Analysis for the Implementation of a Fast Model Predictive Control Scheme on the Absorption/Stripping CO2 Capture Plants

Flexible operation of modular electrochemical CO2 reduction processes

Design of CO2 Capture and Mineralization Systems: Integrated Process Optimization and Controllability Assessment in Parallel Infrastructures

Contact Info

Product

Resources

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Model Analysis for the Implementation of a Fast Model Predictive Control Scheme on the Absorption/Stripping CO₂ Capture Plants

Model Analysis for the Implementation of a Fast Model Predictive Control Scheme on the Absorption/Stripping CO₂ Capture Plants