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

Aug 2, 2026

Uploaded by Theodore

Clean, drinkable water is under pressure as industrialization and urbanization increase pollution, while many emerging contaminants—especially antibiotics and pharmaceutical residues—escape conventional wastewater treatments and harm ecosystems. This review examines how artificial intelligence and machine learning optimize pharmaceutical wastewater treatment systems, covering water quality monitoring, disinfection, renewable energy integration, biological treatment, blockchain-enabled workflows, and data-driven decision support. It highlights advances in cyber-physical automation, big data analytics, cyber-resilience and security, reliable distributed platforms, and stabilization of abnormal parameter fluctuations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages26

Reading Time1 h 6 min

AccessFree to download

Abstract

Introduction

Emerging contaminants and circular economy goals

Key challenges of conventional treatment methods

Use of AI/ML for intelligent operation and parameter optimization

Integration in wastewater plant management systems

Conclusion

Environmental Chemistry Letters (2024) 22:2293–2318 [https://doi.org/10.1007/s10311-024-01748-w](https://doi.org/10.1007/s10311-024-01748-w) Artificial intelligence and machine learning for the optimization of pharmaceutical wastewater treatment systems: a review Voravich Ganthavee1 · Antoine PrandotaTrzcinski1 Received: 14 November 2023 / Accepted: 2 May 2024 / Published online: 21 May 2024 © The Author(s) 2024 Abstract 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. Keywords Algorithm · Cyber-security · Big data · Automation · Internet of things · Blockchain technology Introduction Rapid urbanization and population growth across the world have led to the widespread production of emerging contaminants, which puts significant pressure on wastewater treatment systems. Water scarcity drives our focus towards achieving maximum resource recovery. Zero waste generation is one of the ideal pathways towards achieving a circular economy, which brings remarkable transformation of wastewater treatment systems through commercialization by adding value management processes (Matheri et al. 2022) . If left untreated and discharged from conventional wastewater systems, emerging pharmaceutical contaminants in aquatic or marine ecosystems can adversely impact human health and the environment (Osman et al. 2023 ; Priya et al. 2022) . When pharmaceutically active compounds are released into the environment through human metabolites, it can cause a wide range of side effects on non-target aquatic * Voravich Ganthavee [Voravich.Ganthavee@unisq.edu.au](Voravich.Ganthavee@unisq.edu.au) 1 School of Agriculture and Environmental Science, University of Southern Queensland, Toowoomba, QLD 4350, Australia organisms even at minute concentrations (ng/L or μg/L) . This leads to the development of multi-resistant strainsand the formation of endocrine-disrupting chemicals from breakdown of intermediate by-products from parental compounds, causing significant carcinogenicity, mutagenicity, and teratogenicity in humans and aquatic organisms (Zhanet al. 2019). Among these pollutants, the types of contaminants that have become increasingly challenging to treat are pharmaceuticals and personal care products, disinfection by-products, and per-and poly-fluoroalkyl substances. In addition, wastewater treatment systems are highly complex and dependent on different environmental factors. Process parameters are optimized to tailor the control systems to improve the efficiency of wastewater treatment processes. Although industrial and anthropogenic activities have introduced significant amounts of impurities and hazardous pollutants into our environment, several methods have been developed to minimize the effects of water pollution. These methods have its own merits in terms of the levels of water treatment qu

FAQs

Why are emerging pharmaceutical contaminants difficult for conventional wastewater treatment to handle?

Many pharmaceuticals and antibiotics can escape standard wastewater treatments and are discharged into aquatic ecosystems, where they pose risks to human health and the environment even at very low concentrations.

What main functions does AI and machine learning provide for optimizing pharmaceutical wastewater treatment systems?

AI/ML supports monitoring contaminants, analyzing data, diagnosing water quality, enabling autonomous decision-making, and predicting process parameters to improve operational performance.

Which application areas does the review cover regarding treatment and system design?

The review focuses on water quality and disinfection, renewable energy, biological treatment, and also discusses blockchain technology, automated smart grid power distribution, and cyber-physical system control.

Aug 2, 2026

Uploaded by Theodore