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A machine learning approach for modeling and optimization of a CO2 post-combustion capture unit by Shalaby, Abdelhamid; Elkamel, Ali; Douglas, Peter L.; Zhu, Qinqin; Zheng, Qipeng P. is a Engineering article available to read on EtoBox.

What is A machine learning approach for modeling and optimization of a CO2 post-combustion capture unit about?

Reducing CO2 emissions from fossil fuel fired power plants has been a major environmental concern over the last decade. Amongst various carbon capture and storage (CCS) technologies, the utilization of solvent-based post-combustion capture (PCC), played a major role in the reduction of CO2 emissions. This paper illustrates the development of machine learning models to predict the outputs of the PCC unit. A fine tree, Matérn Gaussian process regression (GPR), rational quadratic GPR, and squared exponential GPR models were developed and compared with a feed-forward artificial neural network (ANN) model. An accuracy of up to 98% in predicting the process outputs was achieved. Furthermore, the models were utilized to determine the optimum operating conditions for the process using a sequential quadratic programming algorithm (SQP) and genetic algorithm (GA). The use of the machine learning models has proven to be very useful since the complete mechanistic model is too large, and its runtime is too long to allow for rigorous optimal solutions. The machine learning models and optimization problems were developed and solved using MATLAB. The data used in this work was obtained from simula

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Author

Shalaby, Abdelhamid; Elkamel, Ali; Douglas, Peter L.; Zhu, Qinqin; Zheng, Qipeng P.

Publisher

Elsevier Science; Thomas Telford; Elsevier ; Thomas Telford Ltd.; Elsevier Ltd.; ICE Publishing Ltd.; Elsevier BV (ISSN 1751-4223)

Published

2021

Language

EN

Field

Engineering (Physical Sciences)