Moving average convergence and divergence indexes based online intelligent expert diagnosis system for anaerobic wastewater treatment process

Wu, Xu; Wang, Yulan; Wang, Cheng; Wang, Wei; Dong, Fang

doi:10.1016/j.biortech.2020.124662

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…In addition, other artificial intelligence application tools such as expert systems (Wu et al 2021), fuzzy logic (Mazhar et al 2019), artificial neuro-fuzzy inference systems (Nam et al 2023), support vector machine (Zhang et al 2023b), knowledge-based systems (Liu et al 2023), ruled-based systems (Victor et al 2005), fuzzy logic control (Santín et al 2018), pattern recognition (Gao et al 2023), swarm intelligence (Negi et al 2023), genetic algorithm (Aparna and Swarnalatha 2023), reinforcement learning (Wang et al 2023a), hybrid systems (Tariq et al 2021), and so on have gained its purposes in process control systems and prediction of water quality characteristics.…”

Section: Assessment Of Water Qualitymentioning

confidence: 99%

Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: a review

Ganthavee,

Trzcinski

2024

Environ Chem Lett

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The access to clean and drinkable water is becoming one of the major health issues because most natural waters are now polluted in the context of rapid industrialization and urbanization. Moreover, most pollutants such as antibiotics escape conventional wastewater treatments and are thus discharged in ecosystems, requiring advanced techniques for wastewater treatment. Here we review the use of artificial intelligence and machine learning to optimize pharmaceutical wastewater treatment systems, with focus on water quality, disinfection, renewable energy, biological treatment, blockchain technology, machine learning algorithms, big data, cyber-physical systems, and automated smart grid power distribution networks. Artificial intelligence allows for monitoring contaminants, facilitating data analysis, diagnosing water quality, easing autonomous decision-making, and predicting process parameters. We discuss advances in technical reliability, energy resources and wastewater management, cyber-resilience, security functionalities, and robust multidimensional performance of automated platform and distributed consortium, and stabilization of abnormal fluctuations in water quality parameters.

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