Publications

Showing 7 results for Author: Subrata Mukherjee

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Sep, 2026

Conference Paper

High-frequency parameter updates in digital twins: Report on a 2-week challenge test for fully autonomous adaptive operation of an effluent water recovery system.

Digital twins (DTs) that can predict the transient behaviour of a water treatment plant combined with buffer capacity, enable reductions in specific energy consumption (SEC) in response to time-of-use (TOU) electricity costs. The accuracy of a DT depends on the richness of its training data (Conejos Fuertes et al. 2020). However, process changes cannot easily be tested in…
Jul, 2026

Conference Paper

Rolling Root Mean Square Based Multimodal Anomaly Detection for Real Time Monitoring of Smart Grid

Reliable real-time monitoring is valuable for maintaining the operational integrity of modern electrical smart grids. Deployment of heterogeneous sensing technologies in substations has enabled high-resolution, multichannel waveform monitoring, but also introduces challenges for anomaly detection due to noise, baseline drift, and modality-dependent signal characteristics.…
Mar, 2026

Journal

Predicting initial trans-membrane pressure across cycles in the ultrafiltration process using random forest

With growing freshwater scarcity, direct potable reuse (DPR) systems that reclaim wastewater for drinking are becoming increasingly important for sustainable water supply. Reliable operation requires minimizing downtime in ultrafiltration (UF) units, where membrane fouling leads to elevated trans-membrane pressure (TMP). This study develops data-driven regression models ba…
Mar, 2026

Conference Paper

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Ultra-filtration(UF) units are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in UF systems. Recent advancements in machine learning (ML) have ena…
Mar, 2026

Conference Paper

A DATA EFFICIENT SPARSE MODELING FRAMEWORK FOR POWER ESTIMATION IN WATER TREATMENT SENSING OPERATIONS

With increasing freshwater scarcity, advanced process design mechanisms such as Closed-Circuit Reverse Osmosis (CCRO) and Digital/Physical Twin systems are gaining traction in water treatment and reuse operations. While digital and physical twin models enable improved system insight and control, their development is often expensive and computationally intensive, requiring…
Mar, 2026

Conference Paper

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Reverse Osmosis (RO) are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in (ultra-filtration) UF systems. Recent advancements in machine learning…
Mar, 2026

Conference Paper

Unsupervised anomaly clustering via offset alignment in multivariate grid sensing data

Modern industries increasingly rely on multi-sensor technologies to acquire complex, high-dimensional data streams, enabling advanced monitoring and control systems. One critical application is online anomaly detection in electrical smart grids, where multivariate and multimodal sensing technologies play a vital role. However, detecting anomalies in such time-series data i…

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Publications

High-frequency parameter updates in digital twins: Report on a 2-week challenge test for fully autonomous adaptive operation of an effluent water recovery system.

Rolling Root Mean Square Based Multimodal Anomaly Detection for Real Time Monitoring of Smart Grid

Predicting initial trans-membrane pressure across cycles in the ultrafiltration process using random forest

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

A DATA EFFICIENT SPARSE MODELING FRAMEWORK FOR POWER ESTIMATION IN WATER TREATMENT SENSING OPERATIONS

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

Unsupervised anomaly clustering via offset alignment in multivariate grid sensing data