Dr. Rahul Shrivastava

Educational Qualifications:

• Ph.D. in Chemical Engineering — Jaypee University of Engineering & Technology (JUET), Guna
• M.E. in Chemical Engineering — Birla Institute of Technology and Science (BITS), Pilani
• B.E. in Chemical Engineering — Madhav Institute of Technology and Science (MITS), Gwalior

Academic and Professional Experience:

• Assistant Professor (Grade-SG)
  Department of Chemical Engineering, Jaypee University of Engineering & Technology, Guna
  2008 – Present
• Lecturer
  Department of Chemical Engineering, Hindustan College of Science & Technology, Farah, Mathura
  2007 – 2008
• Lecturer
  Department of Chemical Engineering, Institute of Engineering, Jiwaji University, Gwalior
  2004 – 2007

Areas of Research Interest:

Dr. Shrivastava’s research interests focus on process monitoring, fault detection and diagnosis, modeling and simulation, and the application of machine learning techniques in chemical engineering processes. His work aims at improving industrial process reliability and efficiency by leveraging advanced data-driven and computational methods.

PUBLICATIONS 

·         K N Gupta, R Kumar, R Shrivastava, and A Thakur, "Analysis of breakthrough curve and application of response surface methodology for the optimization of VOC adsorption" International Journal of Chemical Reactor Engineering, 2025.

·         S K Srivastava, R Shrivastava, “Modeling for Copper Recovery from E-Waste by Using Machine Learning Technique: An Approach for the Circular Economy”, Current Analytical Chemistry, 2025. (At present available online)

·         Devyani Thapliyal, R Shrivastava, G D Verros, S Verma, R K Arya,  P Sen, S C Prajapati, Chahat, and A Gupta et al., “Modeling of Triphenyl Phosphate Surfactant Enhanced Drying of Polystyrene/p-Xylene Coatings Using Artificial Neural Network,” Processes, vol. 12, no. 2, pp. 260–260, Jan. 2024.

·         R K Arya, J Sharma, R Shrivastava, D Thapliyal, GD Verros, "Modeling of Surfactant- Enhanced Drying of Poly(styrene)-p-xylene Polymeric Coatings Using Machine Learning Technique”, Coatings, pp. 1529, 2021. (Publisher: MDPI)

·         R Shrivastava, "Comparative Study of Boosting and Bagging based methods for fault detection in a chemical process," IEEE International Conference on Artificial Intelligence and Smart Systems (ICAIS), pp. 674-679, 2021. (Publisher: IEEE)

·         R Shrivastava, K N Gupta, N N Dutta, " Performance Assessment of Ensemble Decision Tree based Fault Detection System in a Chemical Process," International Journal of Applied Engineering Research, 13(9), pp. 7190-7196, 2018.

·         R Shrivastava, H Mahalingam, N N Dutta, "Application and Evaluation of Random Forest Classifier Technique for Fault Detection in Bioreactor Operation," Chemical Engineering Communications, 204, pp. 591-598, 2017. (Publisher: Taylor & Francis)

·         R Shrivastava, H Mahalingam, N N Dutta, " Optimum Parameters for Fault Detection in Bioreactor using Support Vector Machine and Neural Networks," International Journal for Research for Applied Science and Engineering Technology, 5, pp. 354-364, 2017.

·         K Rajurkar, N Kulkarni, V Rane, R Shrivastava, " Selective Oxidation of Toluene to Benzaldehyde using Cu/Sn/Br Catalyst System," International Journal of Chemical Sciences, 9, pp. 545-552, 2011.