Comparative analysis of machine learning models for Ammonia Capture of Ionic Liquids

TL;DR

This paper compares machine learning models for predicting ammonia solubility in ionic liquids, showing AI methods outperform traditional equations of state in accuracy, with implications for eco-friendly industrial solvent selection.

Contribution

It introduces the use of advanced AI models like MLP and PSO-ANFIS for ammonia solubility prediction in ionic liquids, providing a comparative analysis with traditional equations of state.

Findings

AI models outperform equations of state in accuracy

MLP and PSO-ANFIS effectively predict ammonia solubility

Traditional equations of state are less reliable for this purpose

Abstract

Industry uses various solvents in the processes of refrigeration and ventilation. Among them, the Ionic liquids (ILs) as the relatively new solvents, are known for their proven eco-friendly characteristics. In this research, a comprehensive literature review was carried out to deliver an insight into the ILs and the prediction models used for estimating the ammonia solubility in ILs. Furthermore, a number of advanced machine learning methods, i.e. multilayer perceptron (MLP) and a combination of particle swarm optimization (PSO) and adaptive neuro-fuzzy inference system (ANFIS) models are used to estimate the solubility of ammonia in various ionic liquids. Affecting parameters were molecular weight, critical temperature and pressure of ILs. Furthermore, the salability is also predicted using the two-equation of states. Down the line, some comparisons were drawn between experimental and modeling results which is rarely done. The study shows that the equations of states are not able estimate the solubility of ammonia accurately, by contrast, artificial intelligence methods have produced promising results.