Intelligent model methods and water treatment

The water treatment sector generates vast amounts of experimental and operational data, yet these datasets remain critically underutilised in advancing intelligent modelling approaches. Such data-driven methodologies are pivotal for enabling predictive analytics, process optimisation, and autonomous control systems in water/wastewater management. This Virtual Collection addresses persistent challenges in developing AI-enhanced computational models that bridge data science with environmental engineering applications. Submissions must not overlap with works that have been published or that are simultaneously being considered for publication elsewhere. Topics of interest include, but are not limited to, the following:

• Intelligent model methods for water and wastewater treatment process
• Intelligent model methods for sludge treatment process
• Intelligent model methods for environmental materials design and optimisation
• Intelligent model methods for environmental chemistry

Keywords: Machine learning; Data-driven model; Environmental engineering; Predictive process control; Smart material optimisation; Resource recovery

Instructions for Authors

Editor-in-Chief: Prof. Bing-Jie Ni

Please contact the editorial office at iwt@sciltp.com if you have any questions.