overview

Research efforts focus on the development of high-performance, model-based control systems that enable safe and effective operation of processes. Energy-related applications are the target of these efforts. Recent research has focused on the development of optimal control systems for energy production from biomass, and in particular, anaerobic digestion processes. Globally stabilizing control algorithms for anaerobic digesters have been developed, that enable operation around optimal conditions. Current and future research efforts include energy from biomass applications, and also, control and optimization problems related to both upstream and downstream operations in the petroleum industry.

selected publications
Academic Articles132
  • Venkateswaran, S., Liu, Q., Wilhite, B. A., & Kravaris, C. (2022). Design of linear residual generators for fault detection and isolation in nonlinear systems. International Journal of Control. 95(3), 804-820.
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  • Yu, M., Pasman, H., Erraguntla, M., Quddus, N., & Kravaris, C. (2022). A framework to identify and respond to weak signals of disastrous process incidents based on FRAM and machine learning techniques. Process Safety and Environmental Protection. 158, 98-114.
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  • Sheriff, M. Z., Karim, M. N., Kravaris, C., Nounou, H. N., & Nounou, M. N. (2022). An operating economics-driven perspective on monitoring and maintenance in multiple operating regimes: Application to monitor fouling in heat exchangers. Chemical Engineering Research and Design. 184, 233-245.
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  • Sheriff, M. Z., Karim, M. N., Kravaris, C., Nounou, H. N., & Nounou, M. N. (2022). An operating economics-driven perspective on monitoring and maintenance in multiple operating regimes: Application to monitor fouling in heat exchangers. CHEMICAL ENGINEERING RESEARCH & DESIGN. 184, 233-245.
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  • Shah, P., Sheriff, M. Z., Bangi, M., Kravaris, C., Kwon, J., Botre, C., & Hirota, J. (2022). Deep neural network-based hybrid modeling and experimental validation for an industry-scale fermentation process: Identification of time-varying dependencies among parameters. Chemical Engineering Journal. 135643-135643.
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Books3
  • Kravaris, C., & Kookos, I. K. (2021). Understanding Process Dynamics and Control. Cambridge University Press.
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  • Cruz Bournazou, M. N., Hooshiar, K., Arellano-Garcia, H., Lyberatos, G., Kravaris, C., & Wozny, G. (2011). Optimization of a Sequencing Batch Reactor process for waste water treatment using a two step nitrification model. Elsevier.
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Chapters2
  • Kazantzis, N., & Kravaris, C. (2006). A New Model Reduction Method for Nonlinear Dynamical Systems Using Singular PDE Theory. Model Reduction and Coarse-Graining Approaches for Multiscale Phenomena. 3-15. Springer Berlin Heidelberg.
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  • Seinfeld, J. H., & Kravaris, C. (1982). DISTRIBUTED PARAMETER IDENTIFICATION IN GEOPHYSICS PETROLEUM RESERVOIRS AND AQUIFERS. Distributed Parameter Control Systems. 367-390. Elsevier.
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Conference Papers87
  • Venkateswaran, S., Wilhite, B. A., & Kravaris, C. (2021). Functional observers with linear error dynamics for discrete-time nonlinear systems. 2021 60th IEEE Conference on Decision and Control (CDC), 2021 60th IEEE Conference on Decision and Control (CDC). 00, 6161-6166.
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  • Sheriff, M. Z., Karim, M. N., Kravaris, C., Nounou, H. N., & Nounou, M. N. (2021). Improved Multiscale Multivariate Process Monitoring Methods. 2021 AMERICAN CONTROL CONFERENCE (ACC), 2021 American Control Conference (ACC). 00, 3614-3619.
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  • Ling, C., & Kravaris, C. (2019). Multi-rate Sampled-data Observer Design for Nonlinear Systems with Asynchronous and Delayed Measurements. 2019 American Control Conference (ACC), 2019 American Control Conference (ACC). 2019-July, 1128-1133.
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  • Ling, C., & Kravaris, C. (2017). Multi-rate sampled-data observers based on a continuous-time design. 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017 IEEE 56th Annual Conference on Decision and Control (CDC). 2018-January, 3664-3669.
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  • Duan, Z., & Kravaris, C. (2017). Robust stabilization of a two-stage anaerobic bioreactor system. 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017 IEEE 56th Annual Conference on Decision and Control (CDC). 2018-January, 2083-2088.
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Repository Documents / Preprints9
  • Shah, P., Sheriff, Z., Bangi, M., Kravaris, C., Kwon, J., Botre, C., & Hirota, J. (2021). Deep Neural Network-Based Hybrid Modeling and Experimental Validation for a Full-Scale Bio-Fermentation Process: Identification of Time-Varying Dependencies Among Parameters. SSRN Electronic Journal.
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  • Venkidasalapathy, J. A., & Kravaris, C.. (2021). Hidden Markov Model Based Approach for Diagnosing Cause of Alarm Signals.
chaired theses and dissertations
Email
kravaris@tamu.edu
First Name
Costas
Last Name
Kravaris
mailing address
Texas A&M University; Chemical Engineering; 3122 TAMU
College Station, TX 77843-3122
USA