Application of Intelligent Systems in CO2 Management: State of the Art and Future Prospects
CAAI Transactions on Intelligence Technology, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Derleme
- Basım Tarihi: 2026
- Doi Numarası: 10.1049/cit2.70103
- Dergi Adı: CAAI Transactions on Intelligence Technology
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Anahtar Kelimeler: artificial intelligence, artificial neural network, carbon capturing, carbon dioxide management, emission prediction, energy consumption, fuel usage, machine learning
- Uşak Üniversitesi Adresli: Evet
Özet
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 CO2 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 CO2 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 CO2 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 CO2 transformation into products such as methanol, C2 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 CO2 management models.