A5089303298- Advanced Control Systems Optimization
- Process Optimization and Integration
- Fault Detection and Control Systems
- Smart Grid Energy Management
- Building Energy and Comfort Optimization
- Carbon Dioxide Capture Technologies
- Catalysts for Methane Reforming
- Control Systems and Identification
- Energy Efficiency and Management
- Microbial Metabolic Engineering and Bioproduction
- Innovative Microfluidic and Catalytic Techniques Innovation
- Advanced Thermodynamics and Statistical Mechanics
- Scheduling and Optimization Algorithms
- Ionic liquids properties and applications
- Phase Equilibria and Thermodynamics
- Nonlinear Dynamics and Pattern Formation
- Gene Regulatory Network Analysis
- Advanced Statistical Process Monitoring
- Reservoir Engineering and Simulation Methods
- Integrated Energy Systems Optimization
- Phase Change Materials Research
- Catalytic Processes in Materials Science
- Model Reduction and Neural Networks
- Catalysis and Oxidation Reactions
- Mineral Processing and Grinding
The University of Texas at Austin
2016-2025
Carnegie Mellon University
2023
University of Delaware
2023
University of Pitesti
2019
Institute of Chemical Engineering
2007-2015
University of Minnesota
2004-2008
GE Global Research (United States)
2007
University of Minnesota System
2006
Babeș-Bolyai University
2000-2002
The novel coronavirus SARS-CoV-2 and resulting COVID-19 disease have had an unprecedented spread continue to cause increasing number of fatalities worldwide. While vaccines are still under development, social distancing, extensive testing, quarantining confirmed infected subjects remain the most effective measures contain pandemic. These carry a significant socioeconomic cost. In this work, we introduce optimization-based decision-making framework for managing outbreak in US. This includes...
Chemical companies are constantly seeking new, high‐margin growth opportunities, the majority of which lie in high‐grade, specialty chemicals, rather than bulk sector. To realize these manufacturers increasingly considering decentralized, flexible production facilities: large‐scale units uneconomical for innovative products with a short lifespan and volatile markets. Small modular plants have low financial risks, can respond rapidly to changes demand. Logistics costs be also reduced by...
Today's fast-changing markets often require the granularity of production schedules to be refined time scales comparable constants a chemical process. Consequently, process dynamics must considered explicitly in scheduling. High dimensionality, nonlinearity, and associated computational complexity make incorporating dynamic models scheduling calculations challenging. We propose novel approach based on scheduling-oriented low-order identified from historical operating data. introduce...
The economic circumstances that define the operation of chemical processes (e.g., product demand, feedstock and energy prices) are increasingly variable. To maximize profit, changes in production rate grade must be scheduled with increased frequency. do so, process dynamics considered scheduling calculations, schedules should recomputed when updated information becomes available. In this article, need is addressed by introducing a novel moving horizon closed‐loop approach. Process...
Abstract In the past decades, process engineers are facing increasingly more data analytics challenges and having difficulties obtaining valuable information from a wealth of variable trends. The raw different formats stored in databases not useful until they cleaned transformed. Generally, cleaning consists four steps: missing imputation, outlier detection, noise removal, time alignment delay estimation. This paper discusses available methods that can be used pre-processing help overcome “Big Data”.
The integration of production management and process control decisions is critical for improving economic performance the chemical supply chain. A novel framework integrating scheduling model predictive (MPC) continuous processes proposed. Our predicated on using a low‐dimensional time scale‐bridging (SBM) that captures closed‐loop dynamics over longer scales are relevant to calculations. SBM used as constraint in mixed‐integer dynamic formulation problem. To synchronize MPC calculations,...
Discovering the governing laws underpinning physical and chemical phenomena entirely from data is a key step towards understanding ultimately controlling systems in science engineering. Noisy measurements complex, highly nonlinear underlying dynamics hinder identification of such laws. In this work, we introduce machine learning framework rooted moving horizon optimization for identifying equations form ordinary differential noisy experimental sets. Our approach evaluates sequential subsets...
Tight integration through material and energy recycling is essential to the efficiency economic viability of process systems. Equation‐oriented (EO) steady‐state simulation optimization are key enablers in optimal design integrated processes. A new modeling concept based on pseudo‐transient continuation introduced. An algorithm for reformulating models unit operations as differential‐algebraic equation systems that statically equivalent with original model presented. These improve...