Application of Intelligent Systems in CO ₂ Management: State of the Art and Future Prospects

ABSTRACT Machine learning (ML) integration is becoming increasingly popular in advancing the 4th industrial revolution, known as Industry 4.0. This review paper examines ML applications in CO 2 management stages: emission, capture, and conversion. ML models, including multiple linear regression (MLR), multiple nonlinear regression (MNLR), and artificial neural networks (ANN), are used in the emission phase to predict the CO 2 emissions for the countries and sectors, variables such as energy consumption, fuel usage, gross domestic product (GDP), population, and other miscellaneous socio‐economic variables. In the capture stage, ML forecasts post‐combustion and direct air CO 2 capture efficiencies using models such as ANN, generalized regression neural networks (GRNN), backpropagation neural networks (BPNN), and group method of data handling (GMDH), considering input variables such as catalysts, process parameters, and rheological properties. In the conversion stage, ML models, primarily ANN, multi‐layer perceptron (MLP), particle swarm optimization (PSO), and genetic algorithm (GA), forecast and optimize CO 2 transformation into products such as methanol, C 2 hydrocarbons, syngas, methane, lower olefins, and biofuels, considering factors such as catalyst compositions and process conditions. This study identifies several trends, with ANN emerging as the predominant ML technique. The discoveries will benefit academia and industry in the development of CO 2 management models.