Artificial intelligence-incorporated membrane fouling prediction for membrane-based processes in the past 20 years: A critical review.

The state of art on the prediction of efficiency and modeling of the processes of pollutants removal based on machine learning

The state of art on the prediction of efficiency and modeling of the processes of pollutants removal based on machine learning.

Artificial neural network and statistical modelling of biosorptive removal of hexavalent chromium using macroalgal spent biomass.

Artificial-intelligence methods and machine-learning models have demonstrated their ability to optimize, model, and automate critical water- and wastewater-treatment applications, natural-systems monitoring and management, and water-based agriculture such as hydroponics and aquaponics. In addition to providing computer-assisted aid to complex issues surrounding water chemistry and physical/biological processes, artificial intelligence and machine-learning (AI/ML) applications are anticipated to further optimize water-based applications and decrease capital expenses. This review offers a cross-section of peer reviewed, critical water-based applications that have been coupled with AI or ML, including chlorination, adsorption, membrane filtration, water-quality-index monitoring, water-quality-parameter modeling, river-level monitoring, and aquaponics/hydroponics automation/monitoring. Although success in control, optimization, and modeling has been achieved with the AI methods, ML models, and smart technologies (including the Internet of Things (IoT), sensors, and systems based on these technologies) that are reviewed herein, key challenges and limitations were common and pervasive throughout. Poor data management, low explainability, poor model reproducibility and standardization, as well as a lack of academic transparency are all important hurdles to overcome in order to successfully implement these intelligent applications. Recommendations to aid explainability, data management, reproducibility, and model causality are offered in order to overcome these hurdles and continue the successful implementation of these powerful tools.

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Gulzar Alam

Papers

Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects