Katayoun Kargar | Urban Drainage Systems | Research Excellence Award

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Dr. Katayoun Kargar | Urban Drainage Systems | Research Excellence Award

Toronto Metropolitan University | Canada

Dr. Katayoun Kargar is a civil engineering researcher whose work advances the understanding, modelling, and management of sewer blockages within urban drainage networks, particularly those caused by non-flushable consumer products. Her research integrates hydraulic modelling, data analytics, and infrastructure asset management to address performance deterioration in sewer systems and provide actionable strategies for municipalities. She has contributed original methodologies for analyzing blockage mechanisms using open datasets, quantified the hydraulic impacts of wipe-induced obstructions, and developed simulation frameworks capable of capturing flow alterations and risk conditions in sewer networks. Her work with SWMM includes creating and validating procedures that allow utilities to incorporate blockage dynamics into routine modelling, enabling more accurate capacity evaluations, maintenance prioritization, and system reliability assessments. By translating laboratory insights into practical planning tools, she supports municipalities in enhancing inspection regimes, optimizing rehabilitation strategies, and strengthening long-term infrastructure resilience. Her scholarly contributions include publications indexed in major scientific databases and presentations at leading professional forums, where her work has been recognized through notable awards for research excellence and technical communication. Complementing her academic achievements, she has professional experience in asset management consulting, applying modelling outputs and analytical approaches to assist municipalities in developing asset management plans, capital planning frameworks, and infrastructure lifecycle strategies. Her combined research and practical expertise contribute to advancing municipal wastewater infrastructure management and promoting data-informed decision-making in the engineering of sustainable urban drainage systems.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Shamshirband, S., Hashemi, S., Salimi, H., Samadianfard, S., Asadi, E., … (2020). Predicting standardized streamflow index for hydrological drought using machine learning models. Engineering Applications of Computational Fluid Mechanics, 14(1), 339–350.

Samadianfard, S., Hashemi, S., Kargar, K., Izadyar, M., Mostafaeipour, A., … (2020). Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm. Energy Reports, 6, 1147–1159.

Kargar, K., Samadianfard, S., Parsa, J., Nabipour, N., Shamshirband, S., … (2020). Estimating longitudinal dispersion coefficient in natural streams using empirical models and machine learning algorithms. Engineering Applications of Computational Fluid Mechanics, 14(1), 311–322.

Kargar, K., Safari, M. J. S., Mohammadi, M., Samadianfard, S. (2019). Sediment transport modeling in open channels using neuro-fuzzy and gene expression programming techniques. Water Science and Technology, 79(12), 2318–2327.

Safari, M. J. S., Mohammadi, B., Kargar, K. (2020). Invasive weed optimization-based adaptive neuro-fuzzy inference system hybrid model for sediment transport with a bed deposit. Journal of Cleaner Production, 276, 124267.

 

Pankaj Indurkar | Water Treatment | Research Excellence Award

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Mr. Pankaj Indurkar | Water Treatment | Research Excellence Award

Senior Scientist | Central Salt and Marine Chemicals Research Institute | India

Mr. Pankaj Indurkar is a researcher in membrane science, separation technology, and water purification, recognized for contributions that integrate advanced materials engineering with data-driven modeling. His work spans the development of high-performance ion-exchange membranes, mixed-matrix membranes, and polymeric frameworks designed for desalination, pollutant remediation, and industrial water processing. He has played key roles in designing electrodialysis, membrane distillation, and electro-deionization systems, contributing to indigenous technology development for saline water treatment, brackish water desalination, and ultrapure water generation. His research portfolio demonstrates strong expertise in synthesizing functional materials such as MOF-incorporated membranes, porous polymeric networks, nanocomposites, and metal-doped aerogels, complemented by extensive use of statistical modeling, multivariate optimization, response surface methodology, and machine learning for predicting membrane performance and enhancing process efficiency. He has published widely in high-impact journals on topics including heavy-metal remediation, fluoride and arsenic removal, electrodialysis modeling, neural-network-guided membrane design, and nanomaterial-based water purification strategies. His work also includes book chapters on MOF-based membranes for water and wastewater remediation and contributions to the development of computational frameworks for chemicals recovery from brines, integrating optimization, techno-economic analysis, and data analytics. Through collaborative and multidisciplinary projects, he has contributed to the fabrication, testing, and field deployment of membrane-based and adsorbent-based treatment units for industry and research institutions. His expertise reflects a blend of experimental innovation and computational intelligence aimed at advancing sustainable and energy-efficient water treatment technologies.

Profiles: Scopus | Orcid

Featured Publication

Pankaj, D. I., & Kulshrestha, V. (2025). Electrochemical informed machine learning models for prediction of electrodialysis desalination performance. Separation Science and Technology.

Kumar, P., Suhag, S., Indurkar, P. D., Shahi, V. K., & Kulshrestha, V.* (2025). Synthesis of polyacrylonitrile-based terpolymer cation exchange membrane for efficient brackish water desalination via electrodialysis with neural network prediction. Desalination.

Mishra, S., Upadhyay, P., Indurkar, P. D., & Kulshrestha, V.* (2025). Designing of PVDF-based cation exchange membranes with improved electrochemical properties for energy-efficient brackish water desalination. ACS ES&T Engineering.

Shukla, S., Gupta, A. R., Ratnakar, S. B., Ganguly, B., Indurkar, P. D., & Sharma, S. (2025). A revolutionizing polymeric framework with integrated aluminium fragment for superior water decontamination empowered by a statistical modeling approach. Journal of Materials Chemistry A, 13.

Sutariya, B., Sarkar, P., Indurkar, P. D., & Karan, S.* (2024). Machine learning-assisted performance prediction from the synthesis conditions of nanofiltration membranes. Separation and Purification Technology.