CO2 emission
reduction is an essential step to achieve
the climate change targets. Solvent-based post-combustion CO2 capture (PCC) processes are efficient to be retrofitted to the existing
industrial operations/installations. Solvent degradation (and/or loss)
is one of the main concerns in the PCC processes. In this study, the
thermal degradation of monoethanolamine (MEA) is investigated through
the utilization of hybrid connectionist strategies, including an artificial
neural network-particle swarm optimization (ANN-PSO), a coupled simulated
annealing-least squares support vector machine (CSA-LSSVM), and an
adaptive neuro-fuzzy inference system (ANFIS). Moreover, gene expression
programming (GEP) is employed to generate a correlation that relates
the solvent concentration to the operating variables involved in the
adverse phenomenon of solvent thermal degradation. The input variables
are the MEA initial concentration, CO2 loading, temperature,
and time, and the output variable is the remaining/final MEA concentration
after the degradation phenomenon. According to the training and testing
phases, the most accurate model is ANFIS, and the reliability/performance
of its optimal network is assessed by the coefficient of determination
(R
2), mean squared error, and average
absolute relative error percentage, which are 0.992, 0.066, and 2.745,
respectively. This study reveals that the solvent initial concentration
has the most significant impact, and temperature plays the second
most influential effect on solvent degradation. The developed models
can be used to predict the thermal degradation of any solvent in a
solvent-based PCC process regardless of the complicated reactions
involved in the degradation phenomenon. The models introduced in this
study can be employed for the development of more accurate hybrid
models to optimize the proposed systems in terms of cost, energy,
and environmental prospects.