International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 4 (July-August 2025) Submit your research before last 3 days of August to publish your research paper in the issue of July-August.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 4
July-August 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

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

Author(s) Mr. Akshat Vinay Sabnis, Mr. Vishal Suryakant Wakarekar, Ms. Tanvi Parshuram Patil
Country India
Abstract 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.
Keywords Industrial Wastewater Treatment, CO₂ Gas Hydrates, Machine Learning, Desalination, Water Purification, Environmental Sustainability, Smart Water Technologies, Data-Driven Treatment Optimization, Gas Hydrate-Based Separation, AI in Environmental Engineering
Field Engineering
Published In Volume 7, Issue 4, July-August 2025
Published On 2025-08-09
DOI https://doi.org/10.36948/ijfmr.2025.v07i04.53224
Short DOI https://doi.org/g9w5jd
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 4Cover Page Vol 7 Isu 3Cover Page Vol 7 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com