From Waste to Resource: Circular Economy Approaches for Sustainable Water Treatment

Obiora Jeremiah Obiafudo¹, Charles Chikwendu Okpala², Oladapo Babafemi Fakiyesi³

^1,3 Lecturer, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria.

² Professor, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria. 

Abstract

Water treatment systems are critical to public health and environmental protection, yet they remain energy-intensive and largely linear in design, and thus generate significant residual waste while also underutilize embedded resources. This study presents a data-driven circular economy framework that repositions wastewater treatment plants as resource recovery hubs through the integration of machine learning-based process optimization, life cycle assessment, and techno-economic analysis. Using multi-source operational datasets from municipal and industrial wastewater treatment systems, the framework links real-time process control with quantifiable sustainability indicators. The obtained results demonstrated that optimized circular configurations achieve 28–38% reductions in net energy consumption, 35–42% decreases in greenhouse gas emissions, and up to 60% improvement in nutrient recovery efficiency relative to baseline operations. Enhanced biogas production and water reuse further contribute to both environmental and economic gains, thereby yielding positive net present values with payback periods of 6–8 years despite moderate increases in capital investment. Through the integration of circular economy objectives directly into data-driven optimization workflows, the proposed approach moves beyond static or technology-specific solutions towards scalable, system-wide sustainability improvements. The findings provide empirical evidence that coupling digital analytics with circular economy strategies enables measurable, concurrent environmental and economic benefits in water treatment systems. The proposed framework is technology-agnostic, reproducible, and adaptable across scales and contexts, as it offers a practical pathway for the acceleration of sustainable and resilient water infrastructure transitions. By delivering quantifiable reductions in energy use, GHG emissions, and resource depletion alongside enhanced nutrient and water recovery, this framework directly supports the attainment of key United Nations Sustainable Development Goals (SDGs), particularly SDG 6 (Clean Water and Sanitation), SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action).     

Keywords: Circular economy, Water treatment, Data-driven optimization, Resource recovery, Sustainability assessment, Machine learning, Life cycle analysis, SDGs

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