Peer Reviewed Publications

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  1. Sharma, R., Haq, A., Bakshi, B. R., Ramteke, M., & Kodamana, H. (2023). Designing synergies between hybrid renewable energy systems and ecosystems developed by different afforestation approaches. Journal of Cleaner Production, 139804. doi: 10.1016/j.jclepro.2023.139804

  2. Gupta, M., Manchanda, S., Ranu, S., & Kodamana, H. (2023). Mirage: Model-Agnostic Graph Distillation for Graph Classification. arXiv preprint arXiv:2310.09486., doi: 10.48550/arXiv.2310.09486

  3. Haq, A., Sharma, R., Bakshi, B. R., Kodamana, H., & Ramteke, M. (2023). Forecasting Sustainable Power Generation Profiles to Achieve Net Zero Emissions Using Multi-Objective Techno-Ecological Framework: A Study in the Context of India. Computers & Chemical Engineering, 108439., doi: 10.1016/j.compchemeng.2023.108439

  4. Kumar, D., Goswami, U., Ramteke, M., Kodamana, H., & Prakash T. K., Variance-Capturing Forward-Forward Autoencoder (VFFAE): A Forward Learning Neural Network for Fault Detection and Isolation of Process Data. Process Safety and Environmental Protection, doi: 10.1016/j.psep.2023.07.083

  5. Kumar, A., Bakshi, B. R., Ramteke, M., & Kodamana, H.,. Recycle-BERT: Extracting Knowledge about Plastic Waste Recycling by Natural Language Processing. ACS Sustainable Chemistry & Engineering, doi: 10.1021/acssuschemeng.3c03162

  6. Rani, J., Tripura, T., Goswami, U., Kodamana, H., & Chakraborty, S. (2023). Fault detection using Fourier neural operator. In Computer Aided Chemical Engineering (Vol. 52, pp. 1897-1902). Elsevier. doi:10.1016/B978-0-443-15274-0.50301-2

  7. Gupta, N., Anand, S., Kumar, D., Ramteke, M., & Kodamana, H. (2023). Proximal policy optimization for the control of mAB production. In Computer Aided Chemical Engineering (Vol. 52, pp. 1903-1908). Elsevier. doi: 10.1016/B978-0-443-15274-0.50302-4

  8. Goswami, U., Rani, J., Kumar, D., Kodamana, H., & Ramteke, M. (2023). Energy Out-of-distribution Based Fault Detection of Multivariate Time-series Data. In Computer Aided Chemical Engineering (Vol. 52, pp. 1885-1890). Elsevier. doi: 10.1016/B978-0-443-15274-0.50299-7

  9. Kumar, A., & Kodamana, H. (2023). An NLP-based framework for extracting the catalysts involved in Hydrogen production from scientific literature. In Computer Aided Chemical Engineering (Vol. 52, pp. 1457-1462). Elsevier. doi: 10.1016/B978-0-443-15274-0.50232-8

  10. Kumar, A., Upadhyayula, S., & Kodamana, H. (2023). A Convolutional Neural Network-based gradient boosting framework for prediction of the band gap of photo-active catalysts. Digital Chemical Engineering, 100-109. doi:10.1016/j.dche.2023.100109

  11. Gupta, N., Anand, S., Joshi, T., Kumar, D., Ramteke, M., & Kodamana, H. (2023). Process control of mAb production using multi-actor proximal policy optimization. Digital Chemical Engineering, 100108. doi:10.1016/j.dche.2023.100108

  12. Gupta, M., Kodamana, H. and Ranu.,S. 2023. Frigate: Frugal Spatio-temporal Forecasting on Road Networks, Proceedings of the 29th ACM SIGKDD international conference on knowledge discovery & data mining, KDD (2023)

  13. Goswami, U., Rani, J., Kodamana, H., Kumar, S., & Tamboli, P. K. (2023). Fault detection and isolation of multi-variate time series data using spectral weighted graph auto-encoders. Journal of the Franklin Institute., doi: 10.1016/j.jfranklin.2023.04.030

  14. Rani, J., Tripura, T., Kodamana, H., Chakraborty, S., & Tamboli, P. K. (2023). Fault detection and isolation using probabilistic wavelet neural operator auto-encoder with application to dynamic processes. Process Safety and Environmental Protection, 173, 215-228., doi: 10.1016/j.psep.2023.02.078

  15. Joshi, T., Kodamana, H., Kandath, H., & Kaisare, N. (2023). TASAC: A twin-actor reinforcement learning framework with a stochastic policy with an application to batch process control. Control Engineering Practice, 134, 105462. doi: 10.1016/j.conengprac.2023.105462

  16. Rani, J., Roy, A. A., Kodamana, H., & Tamboli, P. K. (2023). Fault detection of pressurized heavy water nuclear reactors with steady state and dynamic characteristics using data-driven techniques. Progress in Nuclear Energy, 156, 104516, doi: 10.1016/j.pnucene.2022.104516

  17. Kumar, A., Pant, K. K., Upadhyayula, S., & Kodamana, H. (2022). Multiobjective Bayesian Optimization Framework for the Synthesis of Methanol from Syngas Using Interpretable Gaussian Process Models. ACS Omega, doi: 10.1021/acsomega.2c04919

  18. Ghosh, D., Chakraborty, S., Kodamana, H., & Chakraborty, S. (2022). Application of machine learning in understanding plant virus pathogenesis: trends and perspectives on emergence, diagnosis, host-virus interplay and management. Virology Journal, 19(1), 1-11, doi: 10.1186/s12985-022-01767-5

  19. Gupta, N., De, R., Kodamana, H., & Bhartiya, S. (2022). Batch-to-Batch Adaptive Iterative Learning Control─ Explicit Model Predictive Control Two-Tier Framework for the Control of Batch Transesterification Process. ACS omega, 7(45), 41001-41012, doi: 10.1021/acsomega.2c04255

  20. Agrawal, A., Bakshi, B. R., Kodamana, H., & Ramteke, M. (2022). Renewables-Integrated Energy Systems Can Provide Electricity at Lower Cost with Less Environmental and Social Damage. ACS Sustainable Chemistry & Engineering, 10(40), 13390-13401, doi: 10.1021/acssuschemeng.2c03629

  21. Srujan, K. S. S. S., Sandeep, S., Suhas, E., & Kodamana, H. (2022). A dynamical linkage between Western North Pacific tropical cyclones and Indian monsoon low-pressure systems. Geophysical Research Letters, 49, e2022GL098597, doi: 10.1029/2022GL098597

  22. Kumar, A., Ganesh, S., Gupta, D. and Kodamana, H., 2022. A text mining framework for screening catalysts and critical process parameters from scientific literature-a study on Hydrogen production from alcohol. Chemical Engineering Research and Design,184, pp.92-102, doi: 10.1016/j.cherd.2022.05.018

  23. Sharma, R., Agrawal, D., and Kodamana, H., 2021. Data reconciliation frameworks for dynamic operation of hybrid renewable energy systems. ISA transactions, doi: 10.1016/j.isatra.2021.12.006

  24. Nair, A., Srujan, K S S., Kulkarni, S., Alwadhi, K.,Jain, N., Kodamana, H., and Sandeep, S., and John, V. O., 2021. A Deep Learning Framework for the Detection of Tropical Cyclones from Satellite Images. IEEE Geoscience & Remote Sensing Letters,19, doi: 10.1109/LGRS.2021.3131638

  25. Chakraborty, S., Kodamana, H., and Chakraborty, S. 2021. Deep Learning aided automatic and reliable detection of tomato begomovirus infections in plants.Journal of Plant Biochemistry and Biotechnology. (Accepted)

  26. Sharma, R., Kodamana, H., and Ramteke, M. 2021. Multi-objective dynamic optimization of hybrid renewable energy systems .Chemical Engineering and Processing: Process Intensification, 170, doi: 10.1016/j.cep.2021.108663.

  27. Joshi, T., Makker, S., Kodamana, H and Kandath, H., 2021. Twin Actor Twin Delayed Deep Deterministic Policy Gradient Learning for batch process control. Computers & Chemical Engineering, 155, doi: 10.1016/j.compchemeng.2021.107527.

  28. Agrawal, D., Sharma, R., Ramteke, M. C., and Kodamana, H., 2021. A Hierarchical two-tier optimization framework for the optimal operation of a network of Hybrid Renewable Energy Systems. Chemical Engineering Research & Design, 175, pp.37-50.

  29. Sinha, A, Gupta, M., Kodamana, H., and Sandeep, S., 2021. Prediction of synoptic-scale sea level pressure over the Indian summer monsoon using deep learning. IEEE Geoscience & Remote Sensing Letters, doi: 10.1109/LGRS.2021.3100899.

  30. Roy, A. A., Dhawan, H., Kodamana, H. and Upadhyayula, S., 2021. Insights from Principal Component Analysis applied to the Py-GCMS study of Indian coals and their solvent extracted clean coal products. Journal of Coal Science and Technology, pp.1-11

  31. Singh, S., Agrawal, A., Kodamana, H. and Ramteke, M. C., 2021. Multi-objective optimization based recursive feature elimination for process monitoring. Neural Processing Letters, 53(2), pp.1081-1099

  32. R. Ravinder, Singh, S., Bishnoi,S., Jan,A., Sharma,A., Kodamana, H., Krishnan, N.M., 2020. An Adaptive, Interacting, Cluster-Based Model For Predicting the Transmission Dynamics of COVID-19. Heliyon, 6(12), p.e05722

  33. Bishnoi, S., Ravinder, R., Singh, H., Kodamana, H. Krishnan, N.M., 2021. Scalable Gaussian processes for predicting the optical, physical, thermal, and mechanical properties of inorganic glasses with large datasets. Material Advances, 2(1), pp.447-487

  34. Gupta, M., Kodamana, H. , and Sandeep, S., 2020. Prediction of ENSO Beyond Spring Predictability Barrier Using Deep Convolutional LSTM Networks. IEEE Geoscience & Remote Sensing Letters, doi: 10.1109/LGRS.2020.3032353, pp.1-5

  35. Joshi, T., Goyal, V., and Kodamana, H.., 2020. A novel dynamic Just-in-Time learning framework for Modeling of Batch Processes. Industrial & Engineering Chemistry Research, 59(43), pp.11352-11363

  36. Saxena, N., Tiwari, A., Sonawat, D., Kodamana, H. and Rathore., A. S., 2020. Reinforcement Learning based Optimization of Process Chromatography for Continuous Processing of Biopharmaceuticals. Chemical Engineering Science, 230, p.116171

  37. Atmaram, L.L. and Kodamana, H. , 2020. Successive Linearization based Stochastic Model Predictive Control for batch processes described by DAEs. IFAC-PapersOnLine, 53(1), pp.380-385

  38. Singh, V. and Kodamana, H. , 2020. Reinforcement learning based control of batch polymerisation processes. IFAC-PapersOnLine, 53(1), pp.667-672

  39. Ravinder, R., Sridhara, K.H., Bishnoi, S., Grover, H.S., Bauchy, M., Jayadeva, J., Kodamana,H. and Krishnan, N.A., 2020. Deep Learning Aided Rational Design of Oxide Glasses. Materials Horizons, 7(7), pp.1819-1827

  40. Z.Liu, Kodamana, H. , and Huang, Artin, A., 2019. A GMM-MRF Based Image Segmentation approach for Interface Level Estimation. IFAC-PapersOnLine, 52(1), pp.28-33

  41. Rivera, J., Berjikian, J., Ravinder, R., Kodamana, H., Das, S., Bhatnagar, N., Bauchy, M. and Krishnan, N.M., 2019. Glass Fracture upon Ballistic Impact: New Insights from Peridynamics Simulations. Frontiers in Materials, 6, p.239

  42. Bishnoi, S., Singh, S., Ravinder, R., Bauchy, M., Gosvami, N.N., Kodamana, H. and Krishnan, N.A., 2019. Predicting Young's modulus of oxide glasses with sparse datasets using machine learning. Journal of Non-Crystalline Solids, 524, p.119643

  43. Mate, S., Kodamana, H., Bhartiya, S. and Nataraj, P.S.V., 2019. A Stabilizing Sub-Optimal Model Predictive Control for Quasi-Linear Parameter Varying Systems. IEEE Control Systems Letters, 4(2), pp.402-407.

  44. Daemi, A., Kodamana, H. and Huang, B., 2019. Gaussian process modelling with Gaussian mixture likelihood. Journal of Process Control, 81, pp.209-220

  45. Fang, M., Kodamana, H. and Huang, B., 2019. Real-time Mode Diagnosis for Processes with Multiple Operating Conditions Using Switching Conditional Random Fields. IEEE Transactions on Industrial Electronics, 67(6), pp.5060-5070

  46. Fang, M., Ibrahim, F., Kodamana, H., Huang, B., Bell, N. and Nixon, M., 2019. Hierarchically Distributed Monitoring for the Early Prediction of Gas Flare Events. Industrial & Engineering Chemistry Research, 58(26), pp. 11352-11363

  47. Liu, Z., Kodamana, H., Afacan, A. and Huang, B., 2019. Dynamic Prediction of Interface Level Using Spatial Temporal Markov Random Field. Computers & Chemical Engineering, 128, pp. 301-311

  48. Fan, L., Kodamana, H. and Huang, B., 2019. Semi‐supervised dynamic latent variable modeling: I/O probabilistic slow feature analysis approach. AIChE Journal, 65(3), pp.964-979

  49. Raveendran, R., Kodamana, H. and Huang, B., 2018. Process monitoring using a generalized probabilistic linear latent variable model. Automatica, 96, pp.73-83.

  50. Kodamana, H., Huang, B., Ranjan, R., Zhao, Y., Tan, R., and Sammaknejad, N., 2018. Approaches to Robust Process Identification : A Review and Tutorial of Probabilistic Methods. Journal of Process Control, 66 (2018), pp. 68–83

  51. Alipouri, Y., Huang, B., and Kodamana, H., 2018. Minimum Variance Bound and Minimum Variance Controller for Convex Nonlinear Systems with Input Constraints. IET Control Theory & Applications. 12(6), pp. 761 – 769

  52. Fang, M., Kodamana, H., Huang, B. and Sammaknejad, N., 2018. A Novel Approach to Process Operating Mode Diagnosis Using Conditional Random Fields in the Presence of Missing Data. Computers & Chemical Engineering, 111, pp. 149-163

  53. Kodamana, H., Raveendran, R. and Huang, B., 2017. Mixtures of probabilistic PCA with common structure latent bases for process monitoring. IEEE Transactions on Control Systems Technology, 27(2), pp.838-846. (Accepted)

  54. Fang, M., Kodamana, H. and Huang, B., 2018, December. Switching Conditional Random Field Approach to Process Operating Mode Diagnosis for Multi-Modal Processes. In 2018 IEEE Conference on Decision and Control (CDC) (pp. 5146-5151). IEEE.

  55. Fan, L., Kodamana, H. and Huang, B., 2017. Identification of robust probabilistic slow feature regression model for process data contaminated with outliers. Chemometrics and Intelligent Laboratory Systems, 173, pp. 1-13

  56. Guo, F., Kodamana, H., Zhao, Y., Huang, B. and Ding, Y., 2017. Robust Identification of Nonlinear Errors-in-Variables Systems With Parameter Uncertainties Using Variational Bayesian Approach. IEEE Transactions on Industrial Informatics, 13(6), pp.3047-3057.

  57. Guo, F., Hariprasad, K., Huang, B. and Ding, Y.S., 2017. Robust identification for nonlinear errors-in-variables systems using the EM algorithm. Journal of Process Control, 54, pp.129-137.

  58. Guo, F., Wu, O., Kodamana, H., Ding, Y. and Huang, B., 2017. An augmented model approach for identification of nonlinear errors-in-variables systems using the EM algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(11),1968--1978

  59. Hariprasad, K. and Huang, B., 2017, Commentary on the article Statistical process monitoring with independent component analysis, Virtual Special Issue on the 25th Anniversary of Journal of Process Control

  60. Hariprasad, K. and Bhartiya, S., 2017. An Efficient and Stabilizing Model Predictive Control of Switched Systems. IEEE Transactions on Automatic Control, 62(7), pp.3401-3407.

  61. Wu, W., Kodamana, H., Li, J., Huang, B., Forbes, F.J., 2017. Identification of LPV Error-In-Variables systems in state space form using the EM algorithm. (In the proceedings of Asian Control Conference (2017))

  62. Fan, L., Kodamana, H. and Huang, B., 2017. Robust Identification of Switching Markov ARX Models Using EM Algorithm. IFAC-PapersOnLine, 50(1), pp.9772-9777.

  63. Wu, O., Kodamana, H., Jan, N.M., Tan, R. and Huang, B., 2017. Robust soft sensor development using multi-rate measurements. IFAC-PapersOnLine, 50(1), pp.10190-10195.

  64. Vignesh, S.V., Hariprasad, K., Athawale, P., Siram, V. and Bhartiya, S., 2016. Optimal strategies for transitions in simulated moving bed chromatography. Computers & Chemical Engineering, 84, pp.83-95.

  65. Hariprasad, K. and Bhartiya, S., 2016. A computationally efficient robust tube based MPC for linear switched systems. Nonlinear Analysis: Hybrid Systems, 19, pp.60-76.

  66. Wu, O., Hariprasad, K., Huang, B. and Forbes, J.F., 2016, December. Identification of linear dynamic errors-in-variables systems with a dynamic uncertain input using the EM algorithm. In Decision and Control (CDC), 2016 IEEE 55th Conference on (pp. 1229-1234). IEEE.

  67. Vignesh, S.V., Hariprasad, K., Athawale, P. and Bhartiya, S., 2016. An optimization-driven novel operation of simulated moving bed chromatographic separation. IFAC-PapersOnLine, 49(7), pp.165-170.

  68. Sharma, G., Vignesh, S.V., Hariprasad, K. and Bhartiya, S., 2015. Control-relevant Multiple Linear Modeling of Simulated Moving Bed Chromatography. IFAC-PapersOnLine, 48(8), pp.477-482.

  69. Hariprasad, K. and Bhartiya, S., 2014, December. Adaptive robust model predictive control of nonlinear systems using tubes based on interval inclusions. In Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on (pp. 2032-2037). IEEE.

  70. Hariprasad, K. and Bhartiya, S., 2014. A Computationally Efficient Stabilizing Model Predictive Control of Switched Systems. IFAC Proceedings Volumes, 47(1), pp.607-613.

  71. Hariprasad, K. and Bhartiya, S., 2013. A dual-terminal set based robust tube MPC for switched systems. IFAC Proceedings Volumes, 46(32), pp.93-98. (Won best paper award in the session)

  72. Athawale, P., Hariprasad, K., Vinod, S. and Bhartiya, S., 2013. Optimal operating strategies for SMBC. IFAC Proceedings Volumes, 46(32), pp.457-462.

  73. Hariprasad, K., Bhartiya, S. and Gudi, R.D., 2012. A multiple linear modeling approach for nonlinear switched systems1. IFAC Proceedings Volumes, 45(15), pp.63-68.

  74. Hariprasad, K., Bhartiya, S. and Gudi, R.D., 2012. A gap metric based multiple model approach for nonlinear switched systems. Journal of process control, 22(9), pp.1743-1754.

  75. Balasubramanian, G., Hariprasad, K., Sivakumaran, N. and Radhakrishnan, T.K., 2009. Adaptive control of multivariable process using recurrent neural networks. Instrumentation Science and Technology, 37(6), pp.615-630.

  76. Balasubramanian, G., Hariprasad, K., Sivakumaran, N. and Radhakrishnan, T.K., 2009. Adaptive control of neutralization process using recurrent neural networks. Instrumentation Science and Technology, 37(4), pp.383-396.

CAPS Lab

Hariprasad Kodamana, IIT Delhi.