Improving Flexibility and Energy Efficiency of Post-Combustion CO2 Capture Plants Using Economic Model Predictive Control
Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
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Processes 2018, 6(9), 135; https://doi.org/10.3390/pr6090135
Received: 30 July 2018 / Revised: 15 August 2018 / Accepted: 17 August 2018 / Published: 21 August 2018
(This article belongs to the Special Issue Modeling and Simulation of Energy Systems
)
To reduce CO emissions from power plants, electricity companies have diversified their generation sources. Fossil fuels, however, still remain an integral energy generation source as they are more reliable compared to the renewable energy sources. This diversification as well as changing electricity demand could hinder effective economical operation of an amine-based post-combustion CO capture (PCC) plant attached to the power plant to reduce CO emissions. This is as a result of large fluctuations in the flue gas flow rate and unavailability of steam from the power plant. To tackle this problem, efficient control algorithms are necessary. In this work, tracking and economic model predictive controllers are applied to a PCC plant and their economic performance is compared under different scenarios. The results show that economic model predictive control has a potential to improve the economic performance and energy efficiency of the amine-based PCC process up to 6% and 7%, respectively, over conventional model predictive control.
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MDPI and ACS Style
Decardi-Nelson, B.; Liu, S.; Liu, J. Improving Flexibility and Energy Efficiency of Post-Combustion CO2 Capture Plants Using Economic Model Predictive Control. Processes 2018, 6, 135.
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