Supervisor: Dr. Hare Krishna Mohanta
Topic: Development of Machine Learning and Molecular Simulations Driven Smart Gas Sensors for Toxic Gases
Brief Description of the topic:
There are several pollutant gases that are harmful to human beings and need to be detected. The high-performance sensing materials to be used in gas detection will be identified using molecular simulations, including molecular dynamics simulations and grand canonical Monte Carlo simulations. Machine learning techniques will be used to develop smart gas sensors. Molecular simulations and machine learning techniques together will help to design highly selective and sensitive smart gas sensors to be operated at room temperature.
Essential Minimum Qualification:
The candidate must be a graduate (B.E./BTech.)/post graduate (M.E./MTech.) in Chemical Engineering or allied disciplines or MSc in Physics/Chemistry/Material Sciences/Nanotechnology with at least 60% marks.
Desired Qualification:
Knowledge of programming languages like MATLAB, python, C/C++ will be helpful but not essential. Candidates should be willing to learn new modeling and simulation methods or computational skills.
Selected Publications:
- S Venkata Vijayan, Hare K Mohanta, Bijay K Rout and Ajaya Kumar Pani (2023), Adaptive soft sensor design using a regression neural network and bias update strategy for non-linear industrial processes, Measurement Science and Technology, 34, 085012. IF:2.398
- Venkata Vijayan S., Hare K. Mohanta, Ajaya Kumar Pani (2022). Adaptive non-linear soft sensor for quality monitoring in refineries using Just-in-Time Learning—Generalized regression neural network approach, Applied Soft Computing, 119, 108546, ISSN 1568-4946. IF:8.26. Link
- Venkata Vijayan S, Hare Krishna Mohanta, Ajaya Kumar Pani, (2021), Support vector regression modeling in recursive just-in-time learning framework for adaptive soft sensing of naphtha boiling point in crude distillation unit, Petroleum Science, 18 (4), 1230-1239, ISSN 1995-8226. IF: 4.76. Link
- Singh, H., Pani, A. K., Mohanta, H. K. (2019). Quality monitoring in petroleum refinery with regression neural network: Improving prediction accuracy with appropriate design of training set. Measurement, 134, 698-709.
- Pani, A. K., Amin, K. G., & Mohanta, H. K (2016), “Soft sensing of product quality in the debutanizer column with principal component analysis and feed-forward artificial neural network”, Alexandria Engineering Journal, 55, 1667-1674.Pani, A. K., Mohanta, H. K (2016),” Online monitoring of cement clinker quality using multivariate statistics and Takagi-Sugeno fuzzy-inference technique”, Control Engineering Practice, 57, 1-17.
Financial Assistance
Full-time Ph.D. students admitted into the Ph.D. program are eligible to be considered for fellowship of ₹34,000 or ₹37,000 per month as per intake qualifications. Consideration for fellowship will be as per Institute norms, details of which are available in the PhD brochure on the admission website. It will be obligatory on the part of every admitted full time student to undertake 8 hours (per week) of work as assigned to her/him by the Institute.
For details on the admissions process, refer (https://www.bitsadmission.com/ph/). Deadline to receive filled applications with the prescribed application fee is 17:00 hrs. on 25 November, 2023.
For more details, contact:
Dr. Hare Krishna Mohanta, Associate Professor, Department of Chemical Engineering, BITS Pilani – Pilani Campus, Pilani – 333031 (Rajasthan).
Email: harekrishna@pilani.bits-pilani.ac.in. Ph.+91-1596-255754 (O)
Website:https://www.bits-pilani.ac.in/pilani/harekrishna/profile
Google Scholar Link: https://scholar.google.co.in/citations?user=cxaKC3MAAAAJ&hl=en
ResearchGate Link: https://www.researchgate.net/profile/Hare-Krishna-Mohanta
ORCID Link: https://orcid.org/0000-0001-9251-2836
For details please see:https://www.bits-pilani.ac.in/pilani/chemicalengineering/PhDadmissions
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