Rajib Mukherjee

Rajib Mukherjee

Adjunct Assistant Professor
College of Engineering
Department of Chemical Engineering
Office
EB Room 3.100E

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Education

Louisiana State University, Baton Rouge, LA (2004-2010)

PhD in Chemical Engineering

MS in Chemical Engineering

Indian Institute of Technology (IIT Kanpur), India (1997-1999)

Masters in Chemical Engineering

Andhra University College of Engineering, Visakhapatnam, India (1993-1997)

Bachelors in Chemical Engineering

Bio

Dr. Mukherjee graduated from the Process Systems Engineering Laboratory at Department of Chemical Engineering, Louisiana State University (2010). Before joining UTPB, he has conducted postdoctoral research at Tulane University, Center for Computational Science, United States Environmental Protection Agency (US EPA) as an ORISE postdoctoral fellow, University of Illinois Chicago (UIC)/Vishwamitra Research Institute and worked at the Department of Mechanical Engineering, Texas A&M University (TAMU) as visiting assistant professor.

Dr. Mukherjee’s research interests include Sustainability, Water-Energy Nexus, Data analytics using machine learning (ML), multi-resolution and multivariate statistics, algorithm development for sustainable chemical process design, process integration and optimization under uncertainty. Dr. Mukherjee has published 31 research articles in peer-reviewed journals, has several conference proceedings and a state-of-the-art book on engineering sustainability, “Measuring Progress Towards Sustainability: A Treatise for Engineers”, Springer, November, 2016, ISBN-13: 978-3319427171. He received a $300,000 grant from the UT System STARs Program for developing water-energy nexus laboratory and high-performance computing laboratory for process systems engineering. He is CoPI in the proposal “Development of Solar Assisted Hybrid Oilfield Water Treatment Technology with Techno-Economic Assessment and Sustainability Analysis” supported by The University Lands.

Selected Publications

  1. Mukherjee, & U. M. Diwekar, (2021). Multi-objective optimization of the TEG dehydration process for BTEX emission mitigation using machine-learning and metaheuristic algorithms. ACS Sustainable Chemistry & Engineering, 9(3), 1213-1228., https://doi.org/10.1021/acssuschemeng.0c06951
  2. Mukherjee*, B. Beykal*, A.T. Szafranc, M. Onel, F. Stossi, M.G. Mancini, D. Lloyd, F.A. Wright, L. Zhou, M.A. Mancini, E.N. Pistikopoulos, 2020, “Classification of estrogenic compounds by coupling high content analysis and machine learning algorithms”, PLOS Computational Biol. 16(9), e1008191 https://doi.org/10.1371/journal.pcbi.1008191
  3. Mukherjee**, R. Reddy Asani, N. Bopanna, M.M. El-Halwagi, 2020, “Performance evaluation of shale gas processing and NGL recovery plant under uncertainty of the feed composition”, J. Natural Gas Sc. & Eng., Vol 83, 103517 https://doi.org/10.1016/j.jngse.2020.103517
  4. Oke, R. Mukherjee, D. Sengupta, T. Majozi, M.M. El-Halwagi, 2020, “On the Optimization of Water-Energy Nexus in Shale Gas Network under Price Uncertainties”, Energy, 203, https://doi.org/10.1016/j.energy.2020.117770
  5. Diwekar et al., 2021, “A perspective on the role of uncertainty in sustainability science and engineering”, Resources, Conservation & Recycling, Vol 164, pp105140 https://doi.org/10.1016/j.resconrec.2020.105140
  6. Mukherjee, U. M. Diwekar, & N. Kumar, (2020). Real-time optimal spatiotemporal sensor placement for monitoring air pollutants. Clean Technologies and Environmental Policy, 22(10), 2091-2105. https://doi.org/10.1007/s10098-020-01959-z
  7. Shahmohammadi, R. Mukherjee, C. G. Takoudis, & U. M. Diwekar, (2021). Optimal design of novel precursor materials for the atomic layer deposition using computer-aided molecular design. Chemical Engineering Science, 234, 116416. https://doi.org/10.1016/j.ces.2020.116416
Last Updated: 04/19/2022