AI-based soft sensors transform wastewater monitoring in Indian cities

A groundbreaking study has introduced a practical, AI-driven solution to a long-standing challenge in India’s urban wastewater management. Titled "Intelligent Effluent Management: AI-Based Soft Sensors for Organic and Nutrient Quality Monitoring," the peer-reviewed research published in Processes (2025) unveils two artificial neural network (ANN)-based models that accurately predict critical effluent quality parameters using a single, easily measurable input: turbidity.

At a time when modular wastewater treatment plants (WWTPs) in India’s densely populated residential complexes are struggling with inadequate monitoring due to limited budgets and technical expertise, this innovation offers a transformative, low-cost alternative. The study, led by researchers from Pondicherry University and the University of Petroleum and Energy Studies, tested its approach across five modular WWTPs in Bangalore and confirmed the feasibility of real-time, AI-assisted monitoring to ensure regulatory compliance and environmental safety.

Why are conventional wastewater monitoring methods failing urban India?

India’s urban WWTPs face significant challenges in complying with effluent discharge standards due to the high cost and delay associated with traditional laboratory testing. Typically, effluent samples are sent to external labs, where testing for parameters such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP) can be time-consuming and resource-intensive.

The result is sporadic monitoring, which risks untreated or partially treated wastewater entering natural water bodies. The research underscores that the frequency and quality of plant monitoring remain inadequate, primarily due to prohibitive operational costs and lack of skilled technical staff. This is especially problematic in developing nations where treatment must be as cost-effective as possible.

By leveraging artificial intelligence, specifically soft sensors that simulate the function of real instruments, the researchers offer a viable solution. The models use turbidity, an inexpensive and rapidly measurable parameter, as a proxy to predict harder-to-measure water quality indicators.

How do AI-based soft sensors provide a technological breakthrough?

The study developed two ANN-based soft sensor models named NN1 and NN2. NN1 predicts TSS, BOD, and COD, while NN2 estimates TN and TP. Both models require turbidity readings as the primary input. NN1 additionally factors in the plant’s capacity, while NN2 includes a plant identifier to account for variations across the five treatment units. These plants, although different in capacity, follow the same extended aeration process and serve 23 residential towers collectively.

The training data spanned three years (2019–2023), with 156 complete datasets used for NN1 and 185 for NN2. Advanced feature selection and hyperparameter tuning were conducted, including the evaluation of 17 training algorithms. Bayesian regularization emerged as the most effective training algorithm for both models.

Hyperparameter optimization through K-fold cross-validation (K=5) was used to select the best network architecture. The NN1 model performed best with two hidden layers and eight neurons, while NN2 performed best with two hidden layers and seven neurons. Both used “tansig” activation functions for the hidden layers and “purelin” for the output layer.

According to validation metrics on the training datasets, the models achieved strong predictive capabilities. For example, NN2 showed an R² of 0.736 and R of 0.859 for total nitrogen, and NN1 achieved an R² of 0.699 and R of 0.837 for TSS. These results demonstrate the ANN’s effectiveness in capturing complex, nonlinear relationships in treated wastewater quality data, something traditional linear models fail to accomplish.

Can these AI models generalize well across plants and real-world data?

To ensure broad applicability, the researchers used Kohonen Self-Organizing Maps (KSOM) to assess the similarity of effluent quality across the five treatment plants. The analysis revealed no significant clustering, suggesting uniformity in influent quality and plant performance, which justified a single model serving all five units.

Further, the models were validated against independent datasets not used in the training phase - 47 sets for NN1 and 25 for NN2. Performance on these independent datasets closely matched the training results. Notably, the models maintained or even improved their correlation coefficients and mean squared errors (MSEs) during validation. For example, the TSS prediction model improved from an R of 0.837 in training to 0.878 in validation, while maintaining a lower MSE of 1.34 compared to 2.04 in training.

These consistent results demonstrate that the models are well-generalized, neither overfitting nor underfitting, and capable of reliably predicting effluent parameters in operational settings. Confidence intervals for MSE and MAE (mean absolute error) across parameters further confirmed statistical stability.

Overall, the research suggests that these AI-based soft sensors can be deployed at scale in India’s modular WWTPs without the need for expensive instruments or continuous lab support. Operators with access to a simple turbidity meter can instantly assess compliance with effluent discharge standards, enabling real-time responses to potential issues.

As cities across India and other developing nations grapple with rapid urbanization and environmental degradation, the need for scalable, cost-effective monitoring solutions becomes critical.