Conventional treatment models may depend on extensive data or inadequately represent the coupling between biochemical reactions and hydraulic dynamics. The proposed approach instead links a mechanistic representation of treatment processes to a neural network that learns system behavior from sparse observations, allowing physical process information to constrain the machine-learning component. At the center of the framework is an inverse learning strategy.

Rather than requiring a large collection of measurements, it estimates kinetic and hydraulic parameters directly from limited BOD and COD data. These inferred parameters are incorporated into the combined model for monitoring, parameter estimation and virtual tests of how the represented system may respond to changes. The research presents this as a unified modeling method, rather than as a reported full-scale plant intervention or a quantified change in pollutant discharge.

Sensitivity analysis identified aeration intensity, biological reaction kinetics, influent flow rate and biomass yield as the dominant drivers of pollutant-removal efficiency within the model. These results indicate which variables are especially influential in the simulated treatment process and provide a basis for targeted monitoring and operational analysis. The authors position the framework as a tool for circular-economy-oriented industrial water management.

By improving the use of limited data, the model can support adaptive decisions, more efficient treatment and virtual experimentation before operational changes are made. The proposed digital twin is also described as transferable to industrial wastewater systems operating under similar data constraints, although the supplied evidence does not establish performance across different plants or demonstrate field-scale energy savings, pollutant-load reductions or implementation costs. Its main reported advance is therefore a stable, data-efficient modeling structure that combines process physics with neural learning, identifies influential treatment variables and allows monitoring, inference and scenario analysis within one framework.

Subject of Research: Physics-informed digital twins for data-efficient modeling of textile wastewater treatment

Article Title: Circular economy transition through physics informed digital twins for sustainable wastewater management in textile industry

Article References: Rafiq, M., Haider, G., Arshad, M., & Maqbool, S. (2026). Circular economy transition through physics informed digital twins for sustainable wastewater management in textile industry. Discover Sustainability. https://doi.org/10.1007/s43621-026-04232-3

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04232-3

Keywords: textile wastewater, digital twins, Physics-Informed Neural Networks, mechanistic modeling, inverse learning, BOD, COD, aeration, pollutant removal, circular economy, Circular, economy