Transforming Industrial Wastewater Treatment With CO₂ Gas Hydrates: The Impact of Machine Learning on Desalination - A Review

Industrial wastewater treatment is a critical challenge due to increasing water scarcity and stringent environmental regulations. Conventional treatment methods, including membrane-based, thermal, biological, and advanced oxidation processes, have limitations such as high energy requirements, scalability issues, and inefficiency in handling non-biodegradable contaminants. To overcome these challenges, CO₂ gas hydrate-based treatment is a promising technique for desalination and pollutant removal. Gas hydrates, formed under specific thermodynamic conditions, enable the separation of pure water from saline and contaminated sources. This process offers energy-efficient and environmentally sustainable wastewater treatment. The integration of machine learning (ML) enhances the efficiency of CO₂ hydrate-based desalination by optimizing process parameters such as pressure, temperature, and hydrate formation kinetics. ML models, including Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Support Vector Machines (SVM), improve prediction accuracy and real-time monitoring, leading to cost reduction and operational efficiency. Despite its potential, challenges remain, including limited understanding of hydrate formation mechanisms, the need for suitable hydrate promoters, and the development of scalable reactor designs. This study explores the feasibility of CO₂ gas hydrate-based wastewater treatment, emphasizing its advantages over conventional techniques.