A Short Review of Sustainability of Wastewater Treatmetn Plants in Malaysia

Dec 3, 2025 Updated on Dec 5, 2025

The current body of literature on the wastewater treatment plants (WWTP) in Malaysia mainly focuses on 2 aspects:

  1. The assessment of the baseline efficeincy and pollution control of exisitng Malaysia's infrastructure.

  2. Exploration of advanced technological interventions to meet future sustainbility goals.

Cuurent Trends of WWTPs Studies in Malaysia

Several recent studies focuses on evaluating the efficacy of WWTPs against specific pollutants, such as nutrients and emerging contaminants. Research indicates that while centralized plants in Kuching are effective against conventional pollutants, they face challenges with emerging contaminats like pharmaceuticals and microplastics Kuok2022 . This is supported by a comprehensive screenings in Malaysian wastewater, which highlight the environmental hazards posed by pharmaceutical residues and active chemicals Sapkota2025 .

Epidemiological approach based on wastewater have been applied in both Penang and Sungai Petaini to assess the amount of heavy metals concentrations, serving as a proxy for public health monitoring Ruzi2023a Ruzi2024 . Furthermore, studies in specific industrail sectors, such as batik industry require integrated physical-biological treatment systems to manage complex wastewaters effluents had also been examined Daud2022 .

In terms of operational efficiency, studies involving Data Envelopment Analysis (DEA) have been used to benchmark the eco-environmental efficeincy of sewage treatment plants across Malaysia. Findings suggest that there are gaps in resoruce utilization and energy consumption for different sizes of WWTPs, suggesting the potential for cost-savings and environmental beneftis through optimization Fallahiarezoudar2022 . Additionally, similar studies of reliability assessments in suspended growth systems in Melaka also points out the need for consistent process monitoring to improve the overall operational efficiency Ishak2022 .

Technological Interventions

There is a clear methodological trend in the literature from physical modelling to the adoption of dynamic, data-driven approaches for WWTPs in Malaysia. Several studies have explored the use of machine learning techniques, such as Reccurent Neural Network (RNN) and Long Short Term Memory (LSTM) models to predict key performance indicators, such as effluent quality and detection of time-delay water quality indicators of WWTPs Wongburi2023 Zhang2023a . In Malaysia, a growing amount of machine learning studies have been conducted in the context of WWTPs in local settings Ansari2018 Rahmat2023 Rahmat2022 .

On global scale, machine learning is also increasingly used ot model nitrous oxide ($N_2 O$) emissions and several other greenhouse gases from WWTPs Khalil2023 . Intelligent prediction modls are now seen as essential for developing precise mitigation strategies to reduce the emissions of greenhouse gases from WWTPs, as well as optimization on energy utilization Zhang2025a Peng2025 . In WWTPs, a major research motivation is the due to the time lag between operational changes and their impact on emissions, making traditional modelling approaches less effective, for example measuring Biochemical Oxygen Demand takes up to 5 days Rahmat2023 . Recently, research used AI as soft sensors to predict these values in real-time using easily measurable parameters like pH, temperature, and turbidity, which allow for immediate operational adjustments Rahmat2023 Zhang2023a . To the best of my knowledge, there is no such study in greenhouse gases prediction in Malaysia yet. Given the increasing focus on sustainability and climate change mitigation, this represents a significant research gap that needs to be addressed.

Study Convergences on WWTPs

Across the reviewed literature, there is a clear trends of move away from reactive management towards proactive and predictive strategies. Whether through Deep Learning for influent quality Zhang2023a or data assimilation techniques for ammonium nitrogen prediction Bertret2025 , researchers agree that the historical data must be effectively incorporated to anticipate the operational performance of WWTPs.

In terms of renewable energy integration, research consistently identifies the aeration process in biological treatment, specifically activated sludge systems, as the largest energy consumer in WWTPs Shao2024 Gandiglio2017 Tsalas2024 Sembuil2022 . Consequently, optimization efforts are primarily target this stage.

There is also a strong research convergence on retrofitting WWTPs with solar photovoltaic (PV) systems to offset grid dependency. Studies emphasize that WWTPs cover large surface areas, making them an ideal candidate for PV installation Shao2024 Colacicco2020 . An interesting finding also suggested cable-supported floating PV systems on WWTPs' aeration ponds can further enhance energy generation while minimizing land use Milani2024 . Recent research indicates that combining optimized operational parameters with PV systems can reduce carbonn emissions by 30-50% compared to conventional WWTPs Shao2024 , noted that the photovoltaic panels in the study occupied an area of 22,000 m², with daily treatment capacity of 100,000 m³/day, reducing the electricity cost by 40-60%. Additionally, some studies even suggest that small scale WWTPs with flow rates less than $1.89 \times 10^4 m^3/d$, can achieve energy self-sufficiency through solar PV integration alone, with supplying required energy varying from 30% to 100% Milani2024 .

In machine learning applications, multiple sources agree that LSTM networks as superior for handling time-series data and capturing long-term dependencies in water quality parameters Zhang2025a Wongburi2023 . Other than sequential models, ensemble learning methods, such as Random Forests and Gradient Boosting Machines, are consistently highlighted for their high accuracy and interpretability in predicting energy consumption and effluent quality, often outperforming their traditional counterparts like Support Vector Machines (SVM) and simple Artificial Neural Networks (ANN) Alali2023 Zamfir2025 .

Summary

In summary, current research converges on transforming WWTPs into smart, energy-neutral facilities. This is achieved by integrating renewable energy sources, particularly solar PV, and utilizing AI/ML for real-time predictive management. The focus on optimizing aeration processes and utilizing data-driven models reflects a broader trend towards sustainability and operational efficiency in wastewater treatment.

Future research should address existing gaps, such as greenhouse gas emissions modeling using AI techniques, to further advance the sustainability of wastewater treatment in Malaysia and surrounding regions.


*This article do not represent the views of any organization I am affiliated with. It is solely my personal thoughts based on the review of existing literature.

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