Ph.D.
AI-driven stochastic control of energy systems
Data-efficient offline reinforcement learning for safe and explainable chemical process control
Adaptive and self-explainable AI framework for intelligent manufacturing systems
Multi-timescale decision-making in renewable integrated process
Techno-economic-environmental analysis methodology of bio-fuel production process
Optimal design of gas management system integrated with reliquefaction process for LNG carrier
Process monitoring with transformer integrating multimodal data, incremental updates, and explainability
Robust adaptation strategies of data-driven models for chemical process system
Minimizing CO2 emission in the ironmaking process: A plant-scale model for an alternative ironmaking process based on fluidized bed reactors
Optimization modeling language-based framework for steady-state and dynamic simulation and superstructure optimization of large-scale processes ..
Techno-economic analysis and energy management system for renewable energy micro-grid under climatic variability
Model calibration and composition search for catalytic system
Data-driven soft sensor development for multi-grade and time-varying processes
Acceleration of high-performance catalyst development using machine learning techniques
Model predictive control for uncertain systems and its integration with reinforcement learning
Online synchronization for data-based monitoring and control of uneven batch process
Computationally efficient modeling strategies for chemical reactor system
Reliability assessment-based safety distance analysis for high-pressure gas pipelines
Optimal design of reactor via multi-scale modeling for distributed hydrogen production
Probabilistic machine learning approach to process systems engineering through parametric distribution approximation
Simulation-based framework to improve feasibility of intensified eco-friendly chemical processes
Data-driven fault detection and diagnosis using machine learning techniques and information theory
Construction and online adaptation of nonlinear semi-batch process model for digital twin under limited data
Computationally efficient multilinear model-based control combined with data-driven trajectory optimization
Long-time dynamic simulation of industrial-scale multiphase chemical reactor using computational fluid dynamics
Optimality enhancement in move-blocked model predictive control and offset-free model predictive control
Model-based reinforcement learning for process control and optimization
Risk-based inherent safety approach to process design and optimization
Computationally efficient simulation and optimization strategies for design of multiphase chemical reactors with complex dynamics
Model-based experimental design for computationally efficient parameter estimation of fed-batch bioreactors
Backstepping model predictive control and stability-oriented learning algorithm for nonlinear optimal control
Modeling, simulation, structural analysis and feed characterization of a fluid catalytic cracking process
Genetic algorithm using d-θ clustering, entropy analysis and Z-control
Design and optimization of industrial-scale compression system for its efficient and robust operation
Optimal design and operation of transcritical CO2 vapor compression system for ship transport in CCS chain
Integration of planning and scheduling using approximate dynamic programming
Data-driven approaches to fault detection and diagnosis under multiple faults
Modifier adaptation schemes for data-driven optimization of chemical and biological processes under model-plant mismatch
(A) novel iterative learning control method combined with model predictive control for tracking specific points
Cooperative estimation and control of large-scale proceses networks
Optimal control strategies for gas cooling systems using geometric design and model predictive control
Optimal design and control of silane off-gas recovery process with dividing wall column under periodic disturbances from parallel batch reactors..
Near real-time estimation and optimization of microalga photobioreactor system for productivity improvement
M.S.
Deep learning-augmented genetic algorithm for hydrogen liquefaction optimization
Development of robust fault detection algorithm using clustering method for multivariate time-series process data
Machine learning based approaches to estimation of quality variables in batch processes
Integration of population balance model with computational fluid dynamics for estimation of oxygen mass transfer rate in bioreactor
Process flowsheeting program development for hydrogen isotope separation system
Model predictive control for reducing NOx emissions from diesel exhaust aftertreatment system
Fault diagnosis of an industrial plant using maintenance record and multivariate analysis
Fundamental modeling and experimental investigation of polymer washing batch process
Model predictive control of mixed refrigerant liquefaction process for stable and economic operation in FLNG
Temperature and pressure control scheme for precooling process of carbon dioxide storage tank
Integrated simulation and optimization for the whole chain of CCS
A bayesian approach to robust parameter estimation of physiologically based pharmacokinetics model with drug dissolution model
Optimal design and operation of CO2 liquefaction process considering variation in cooling water temperature
Design of gas antisolvent recrystallization process
Development of soft sensor based on Raman spectroscopy for on-line monitoring of glucose concentrations in microalgal production system ..
Nonlinear model predictive control for gas antisolvent recrystallization process
Postdoc.
Undergraduate
