article · Discover Water
Digital technologies such as the Internet of Things, artificial intelligence, and machine learning offer significant opportunities to enhance wastewater treatment operations. These tools support real-time monitoring, predictive maintenance, and intelligent decision-making, which can optimise resource use, lower energy consumption, and boost water quality. Evaluating digital solutions using standard performance indicators reveals their analytical effectiveness in treatment plant settings. Integrating these systems also contributes towards carbon neutrality targets through more efficient, data-driven resource allocation. However, deploying these technologies introduces practical challenges for treatment facilities, including financial costs, operational complexity, and data security risks that must be addressed to support clean water and sanitation goals.
Wastewater treatment plants are essential for public health and environmental protection, but they can be energy-intensive. Applying digital tools allows plant managers to run facilities more efficiently and catch equipment failures early. This lowers operational emissions, conserves energy, and improves effluent quality, directly advancing global sustainable development targets for clean water and climate action.
The work highlights digital solutions suited for wastewater treatment plant operators and utility managers seeking to optimise facilities and cut energy costs. Because the study focuses on an analytical evaluation using standard error metrics rather than piloting a specific commercial product, the technologies reflect an early-stage review of digital tools. Practical commercial adoption will require overcoming reported hurdles related to implementation costs, operational complexity, and data security.
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This paper investigates the role of digitalization in enhancing wastewater treatment processes, emphasizing its potential to optimize resource utilization, reduce energy consumption, and improve water quality. By examining the implementation of digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML), the study demonstrates how these tools enable real-time monitoring, predictive maintenance, and intelligent decision-making in wastewater treatment operations. The paper provides a comparative analysis based on key performance indicators (MAPE, RMSE, R2) to evaluate the effectiveness of these digital solutions. Additionally, it discusses the benefits and challenges associated with integrating digital tools in wastewater treatment plants (WWTPs), including cost, complexity, and data security concerns. The study also addresses the impact of digitalization on carbon neutrality goals, highlighting how data-driven approaches can enhance resource allocation and management. By offering insights into current practices and future directions, this paper aims to contribute to the advancement of sustainable wastewater treatment and support the achievement of UN SDG#6, ensuring clean water and sanitation for all.
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DOI: 10.1007/s43832-024-00134-5
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