Renewable Energy, Journal Year: 2024, Volume and Issue: 233, P. 121162 - 121162
Published: Aug. 10, 2024
Language: Английский
Renewable Energy, Journal Year: 2024, Volume and Issue: 233, P. 121162 - 121162
Published: Aug. 10, 2024
Language: Английский
The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 948, P. 174873 - 174873
Published: July 20, 2024
Language: Английский
Citations
5Separation and Purification Technology, Journal Year: 2024, Volume and Issue: 359, P. 130577 - 130577
Published: Nov. 16, 2024
Language: Английский
Citations
5Environmental Science & Technology, Journal Year: 2024, Volume and Issue: unknown
Published: Dec. 16, 2024
Polymeric membranes have been widely used for liquid and gas separation in various industrial applications over the past few decades because of their exceptional versatility high tunability. Traditional trial-and-error methods material synthesis are inadequate to meet growing demands high-performance membranes. Machine learning (ML) has demonstrated huge potential accelerate design discovery membrane materials. In this review, we cover strengths weaknesses traditional methods, followed by a discussion on emergence ML developing advanced polymeric We describe methodologies data collection, preparation, commonly models, explainable artificial intelligence (XAI) tools implemented research. Furthermore, explain experimental computational validation steps verify results provided these models. Subsequently, showcase successful case studies emphasize inverse methodology within ML-driven structured framework. Finally, conclude highlighting recent progress, challenges, future research directions advance next generation With aim provide comprehensive guideline researchers, scientists, engineers assisting implementation process.
Language: Английский
Citations
5Advances in Environmental and Engineering Research, Journal Year: 2025, Volume and Issue: 06(01), P. 1 - 18
Published: Jan. 15, 2025
The World climate is changing, with a great impact on global food production systems. Extreme weather events, floods, wildfires and draughts are phenomena of disrupted previously stable natural patterns, which vital for crop animal husbandry alike. Most the World’s produced in temperate climatic zones rich arable land those affected by increasing unpredictability naturally occurring seasons conditions. This work aims to provide possible sustainable solution challenges under pressures change. Changing methods moving indoor agriculture poses immense opportunities at same time. Technical solutions currently researched explored innovators, governments industry leaders developed Bio Steel Cycle can be seen as nucleus other industries, including production, could starting point new standard all systems: SusCip principle.
Language: Английский
Citations
0Sustainable Energy Technologies and Assessments, Journal Year: 2025, Volume and Issue: 75, P. 104226 - 104226
Published: Feb. 7, 2025
Language: Английский
Citations
0Building and Environment, Journal Year: 2025, Volume and Issue: 275, P. 112817 - 112817
Published: March 9, 2025
Language: Английский
Citations
0ACS Sustainable Chemistry & Engineering, Journal Year: 2025, Volume and Issue: unknown
Published: April 2, 2025
Language: Английский
Citations
0Renewable and Sustainable Energy Reviews, Journal Year: 2025, Volume and Issue: 216, P. 115688 - 115688
Published: April 9, 2025
Language: Английский
Citations
0Catalysis Letters, Journal Year: 2025, Volume and Issue: 155(5)
Published: April 9, 2025
Language: Английский
Citations
0Processes, Journal Year: 2025, Volume and Issue: 13(4), P. 1160 - 1160
Published: April 11, 2025
Carbon Capture, Utilization, and Storage (CCUS) technologies have emerged as indispensable tools in reducing greenhouse gas (GHG) emissions combating climate change. However, the optimization scalability of CCUS processes face significant technical economic challenges that hinder their widespread implementation. Machine Learning (ML) offers innovative solutions by providing faster, more accurate alternatives to traditional methods across value chain. Despite growing body research this field, applications ML remain fragmented, lacking a cohesive synthesis bridges these advancements practical This review addresses gap systematically evaluating all major components—CO2 capture, transport, storage, utilization. We provide structured representative examples for each category critically examine various techniques, objectives, methodological frameworks employed recent studies. Additionally, we identify key parameters, limitations, future opportunities applying enhance systems. Our thus comprehensive insights guidance stakeholders, supporting informed decision-making accelerating ML-driven commercialization.
Language: Английский
Citations
0