Research Projects

Considering the Government of India’s relentless efforts to provide safe and adequate drinking water to our citizens and our significant progress in providing individual household tap connections, the need to monitor microbiological water quality in water supply systems (WSS) has become more critical than ever. Applying computer-based tools built on models mathematically defining the microbial growth and biofilm formation dynamics of water distribution pipes can be a realistic approach to safeguarding biological stability during WSS operation. However, a significant research gap exists in developing tools for forecasting pathogens or non-pathogenic microbial matter formation and transmission over large-scale pipe networks. Towards this direction, the proposed SuViJal project aims to develop engineering tools for analyzing the chemical and microbiological activity in full-scale WSS. The project is dissected into four work programs (WPs) representing the testing of the hypotheses below. H1-Microbial regrowth and biofilm formation in water distribution pipes are significantly influenced by source water quality, environmental conditions, pipe material, and flow dynamics. H1 can be tested by systematically controlling and measuring the effects of substrate and chlorine levels, water temperature, flow regimes, and pipe material on microbial dynamics in an experimental test rig. H2-Machine learning (ML) methods can be used to develop simplified surrogate kinetic models with fewer variables and parameters than traditional process-based models. H2 can be tested by training ML-based models using experimental and simulation data from WP-1 and evaluating their ability to predict microbiological quality trends compared to traditional process-based models. H3-Physics-Informed Neural Networks (PINNs) can serve as accurate and efficient surrogate models for approximating the solutions of advective-dispersive-reactive equations governing contaminant fate and transport in WSS. H3 will be tested by generating datasets through systematic experimental design and simulations (from WP-1 and WP-2 outcomes), followed by training and validating PINNs on these datasets. H4-Integrating PINNs-based surrogate models into a process-based modeling framework to create a hybrid model will result in a water quality modeling tool that is both generic and computationally efficient. H4 will be tested by developing the hybrid model using outputs from WP-3, calibrating and validating it against simulation data, and applying it to virtual and pilot-scale real-world WSS. The outcome of the SuViJal project will be an open-source engineering toolbox that can forecast the chemical and microbiological quality dynamics of drinking water from the source to the consumer taps. This toolbox will shift focus from the traditional hydraulics-based design and operation of WSS towards a “people-first” approach, where safe and reliable tap water quality is the primary objective.