The combined process modeling, control and optimization programs have more than 25 full-time graduate students and several postdoctoral researchers in addition to the six faculty members supervising the ongoing research.
During the last five years, approximately 25 Ph.D. and 1 M.S. students graduated from the three universities in the area of process modeling, control and optimization. Graduates of the program are highly sought by industry and universities, often accepting jobs more than one year prior to completion of their degrees. Approximately 50 percent of the graduates are U.S. citizens.
Process control facilities available for research are outstanding. We employ a variety of control systems (Emerson Delta V, National Instruments) in experimental studies, and we also have numerous networks of PCs and high-performance computing clusters. Most students carry out both theoretical and experimental research.
Our program focuses on maintaining a balance between fundamentals and practice.
Overview of Research Areas
Control System Monitoring and Diagnosis: multivariable control performance assessment, performance diagnosis, loop auditing, minimum variance control benchmark, sensor and actuator diagnosis, MPC performance monitoring
Dynamic Modeling of Chemical Processes: microelectronics process modeling, solid phase formation and growth, crystallization, packed bed distillation, batch distillation, reactive distillation, packed bed catalytic reactors, advanced materials processing, parameter estimation software development, model reduction
Production Planning and Scheduling: mixed-integer programming models and solution methods, integration of scheduling and process automation, scheduling and blending in oil refineries, asset maintenance scheduling, integrated production planning and scheduling.
Supply Chain Management: network design, inventory routing, integrated production planning and distribution.
Materials Processing: chemical vapor deposition, measurement of film composition and growth rate, plasma etching, lithography, rapid thermal processing, particle formation, measurement of size and shape of particles.
Dynamic System Identification: subspace identification methods, prediction error methods, closed loop identification, identification for monitoring, design of experiments.
Model Predictive Control (MPC) and Moving Horizon Estimation (MHE): large-scale MPC and MHE, model-based control, constrained control, adaptive control, multivariable control.
Optimization Theory and Algorithms: interior-point algorithms for linear and nonlinear programming; algorithms for structured optimization problems in control; optimization under uncertainty; applications to cancer treatment, meteorology and other areas; algorithms for parallel computers; optimization software; applications to petroleum reservoirs and imaging, reformulations and decomposition methods for mixed-integer programming.
Statistical Process Monitoring and Fault Diagnosis: fault detection, fault identification and isolation, fault reconstruction, fault classification, data reconciliation, sensor validation, dynamic process fault diagnosis, process chemometrics, principal component analysis, canonical variate analysis, partial least squares, multivariate statistical quality control.
Chemical Process Synthesis: Synthesis and analysis of renewable energy systems (e.g., biofuels and solar fuels) as well as renewable chemicals (biomass-to-chemicals).