Modelling of a Post-combustion CO2 Capture Process Using Bootstrap Aggregated Extreme Learning Machines

Abstract

Abstract This paper presents a study of modelling post-combustion CO2 capture process using bootstrap aggregated ELMs. The dynamic ELM models predict CO2 capture rate and CO2 capture level using the following variables as model inputs: inlet flue gas flow rate, CO2 concentration in inlet flue gas, pressure of flue gas, temperature of flue gas, lean solvent flow rate, MEA concentration and temperature of lean solvent. In order to enhance model accuracy and reliability, multiple ELM models are developed from bootstrap re-sampling replications of the original training data and combined. Bootstrap aggregated ELM model can offer more accurate and reliable predictions than a single ELM model, as well as provide model prediction confidence bounds. The developed models can be used in the optimisation of CO2 capture processes.