• Project Title: Process Topology based Computational Methods for Sensor and Actuator Placement in Large Scale Systems. Funding Agency: Start-up Research Grant (SRG) by DST-SERB. Grant Value (in INR): 24 lakhs.
• Project Title: Computer Vision based Characterization of Dense Dispersed Systems in Food Applications. [Co-PI: Dr. Trivikram Reddy Nallamilli]. Funding Agency: New Faculty Start-up Grant (NFSG) by Indian Institute of Technology Tirupati. Grant Value (in INR): 40 lakhs.

Major Areas of Interest:

• Process Systems Engineering
• Machine Learning

Other Areas of Interest:

• Data-driven Modeling
• Sensor Placement
• Process Optimization
• State Estimation
• Model Predictive Control
• Water distribution network

Refereed Journal Publications:

• M. Nabil and S. Narasimhan, “Sensor network design for optimal process operation based on data reconciliation”, Ind. Eng. Chem. Res., 51 (19), pages 6789-6797, 2012. (IF: 2.843).
• M. Nabil, S. Skogestad and S. Narasimhan, “Profitable and dynamically feasible operating point selection for constrained processes”, Journal of Process Control, 24 (5), pages 531-541, 2014. (IF: 2.700).
• H. Alighardashi, M. Nabil and B. Huang, “Expectation maximization approach for simultaneous gross error detection and data reconciliation using Gaussian mixture distribution”, Ind. Eng. Chem. Res., 56 (49), pages 14530-14544, 2017. (IF: 3.141).
• A. Kumar, M. Nabil and S. Narasimhan, “Economically optimal input design approach for identification of constrained processes”, Ind. Eng. Chem. Res., 57 (20), pages 6956-6967, 2018. (IF: 3.141).
• W. Shen, Z. Li, B. Huang and M. Nabil, “Chance constrained model predictive control for SAGD process using robust optimization approximation”, Ind. Eng. Chem. Res., 58 (26), pages 11407-11418, 2019. (IF: 3.141).
• H. Alighardashi, M. Nabil, and B. Huang, “Data Rectification for Multiple Operating Modes: A MAP Framework”, Computers & Chemical Engineering, 123, pages 272-285, 2019. (IF: 3.113).
• R. Li, M. Nabil, V. Prasad and B. Huang, “Constrained Mutimodal Ensemble Kalman Filter based on Kullback- Leibler Divergence", Journal of Process Control, 79, pages 16-28, 2019. (IF: 3.316).
• R. Li, M. Nabil, V. Prasad and B. Huang, “Constrained Ensemble Kalman Filter based on Kullback- Leibler Divergence", Journal of Process Control, 81, pages 150-161, 2019. (IF: 3.316).
• R. Xie, M. Nabil, K. Hao, L. Chen, and B. Huang, “Supervised Variational Autoencoders for Soft Sensor Modeling with Missing Data", IEEE Transactions on Industrial Informatics, 16 (4), pages 2820 - 2828, 2020. (IF: 7.377).
• F. Shen, M. Nabil, B. Huang, and H. Yang, “Probabilistic Just-in-Time Approach for Nonlinear Modeling with Bayesian Nonlinear Feature Extraction", Chemometrics and Intelligent Laboratory Systems, 196, 103895, 2020. (IF: 3.491).
• M. Nabil, B. Huang, A. Espejo, L. Zelmer, L. Gulbransen, and F. Xu, “Just-In-Time Learning for Predicting Oilsands Ore Characteristics using GPS Data in Mining Applications", The Canadian Journal of Chemical Engineering, pages 1 - 12, 2020.
• Y. Cao, M. Nabil, B. Huang, and Y. Wang, “Multimodal Process Monitoring Based on Variational Bayesian PCA and Kullback-Leibler Divergence between Mixture Models", Chemometrics and Intelligent Laboratory Systems, Accepted, 2021. (IF: 3.491).
• A. Sadeghian, M. Nabil, O. Wu, and B. Huang, “Robust Probabilistic Principal Component Regression with Switching Mixture Gaussian Noise for Soft Sensing", Chemometrics and Intelligent Laboratory Systems, Accepted, 2022. (IF: 3.491).
• Y. Cao, M. Nabil, B. Huang, Y. Wang, Z. Pan, and W. Gui, “No-Delay Multimodal Process Monitoring Using Kullback-Leibler Divergence Based Statistics in Probabilistic Mixture Models", IEEE Transactions on Automation Science and Engineering, Accepted, 2022. (IF: 5.083).

Refereed Conference Publications:

• M. Nabil, S. Narasimhan and S. Skogestad, “Economic back-off selection based on optimal multivariable controller”, in proceedings of 8th IFAC International Symposium on Advanced Control of Chemical Processes, Singapore, pages 792 - 797, 2012.
• M. Nabil and S. Narasimhan, “Integrated sensor network design”, Computer Aided Chemical Engineering, 31(1), pages 1522-1526, 2012.
• M. Nabil, S. Narasimhan and S. Skogestad, “Optimal selection of sensor network and backed-off operating point based on economics", in proceedings of European Control Conference, Zurich, Switzerland, pages 4472 - 4477, 2013.
• G. Menon, M. Nabil, and S. Narasimhan, “Branch and bound algorithm for optimal sensor network design", in IFAC Proceedings Volumes, pages 690 - 695, 2013.
• A. Kumar, M. Nabil and S. Narasimhan, “Economical and plant friendly input design for system identification", in proceedings of European Control Conference, Strasbourg, France, pages 732 - 737, 2014.
• A. Kumar, M. Nabil and S. Narasimhan, “Economical input design for identification of Multivariate Systems", in IFAC PapersOnLine, 48(28), pages 1313 - 1318, 2015.
• O. Wu, H. Kodamana, M. Nabil, R. Tan, B. Huang, “Robust soft sensor development using multi-rate measurements”, in IFAC PapersOnLine, 50(1), pages 10190-10195, 2017.
• R. Li, M. Nabil, V. Prasad and B. Huang, “Constrained Extended Kalman Filter based on Kullback- Leibler Divergence", in proceedings of European Control Conference, Limassol, Cyprus, pages 831 - 836, 2018.
• C. Li, M. Nabil, and B. Huang, “Data analytics for oil sands subcool prediction – a comparative study of machine learning algorithms", in proceedings of 10th IFAC International Symposium on Advanced Control of Chemical Processes, Shenyang, Liaoning, China, pages 886 - 891, 2018.
• C. Li, M. Nabil, and B. Huang, “Stacking Approach for Soft Sensor Design in Steam Assisted Gravity Drainage Applications", in IFAC PapersOnLine, 51(21), pages 30-35, 2018.

Ph.D. Scholars:

• Manjay Kumar: Process Monitoring
• Ajaz Ahmed Bhat: Gas Separations (Guide: Prof. Sasidhar Gumma)

M.S. (Research):

• Arjun M: Sensor Placement
• Rahul Yadav: Reinforcement Learning in Process Control (Co-Guide: Dr. Ajin Joseph George)

B.Tech Students:

• Ayush Singh: Computer Vision for Dense Dispersed Systems
• Noble Saji Mathews: Reinforcement Learning for Batch Process Scheduling
• M U Abuthagir: Reinforcement Learning for Batch Process Control