Hector Budman

Hector Budman

Chemical Engineering, Professor

Biography

Hector Budman is a Professor in the Department of Chemical Engineering at the University of Waterloo.

Professor Budman’s research is multi-disciplinary, leading him to undertake collaborative work in pharmaceuticals, water treatment, minerals processing, and pulp and paper industries.

Typically when a system is modeled, there is some difference between the actual process and the model. As a result, control systems based on process models have to be designed to compensate for the difference. Professor Budman’s research investigates the application of robust control theory to advance control techniques such as Model Predictive Control, Inferential Control and Non-Linear Control. In order to deal with the model inaccuracy, Professor Budman studies methods for quantifying the model uncertainty from experimental data. He also designs steady state estimators so that controlled variables can be inferred from secondary measurements; such as controlling concentrations using temperatures.

Furthermore, chemical processes are generally non-linear, especially when operated in a wide range of operating conditions. Hence, these processes need to be controlled using non-linear controllers. The obstacle that Professor Budman’s work seeks to overcome when designing non-linear controllers is to obtain a simple model of the processes under study. Typical processes studied are bioreactions, solidification of metal, cells and tissues.

In addition to his research work, Professor Budman has authored and co-authored over 150 journal publications in process modelling and controls with applications to biotechnology and reaction systems. He also co-authored the book “Periodic Operations of Chemical Reactors” and chaired the October 2014 Canadian Conference in Chemical Engineering.

Research

Research Interests

  • Process Control
  • Modelling of Chemical Systems
  • Monitoring of Chemical Processes
  • Inferential Sensors
  • Process Systems Engineering
  • Robust Control
  • Modelling and Control of Nonlinear Processes
  • Chemical Process Control
  • Model Predictive Control
  • Systems Biology
  • Modeling
  • Control and Optimization of Biological Processes
  • Metabolic Flux Analysis
  • Dynamic Metabolic Flux Models
  • Run-to-run (batch to batch) Optimization of Pharmaceutical Process
  • Monitoring/Fault Detection of Chemical and Biological/Biotechnological Processes
  • Biochemical and Biomedical Engineering
  • Multivariate Statistical Methods for Monitoring and Fault Detection
  • Inferential (soft) Sensors

Application Areas

  • Data Science

Technology Areas

  • Computational Modelling
  • Sensors

Discipline Areas

  • Bioprocessing/Biochemical Engineering
  • Biotechnology