Process Systems Engineering Laboratory

The DChE Process Systems Engineering Laboratory (PSEL) envisions to develop relevant research in the (1) design; (2) operation; (3) optimization; and, (4) control of chemical, physical, and biological processes using computer-aided approaches. These aspects correspond to the 4 quadrants in the lab’s logo.


In a broad sense, research in chemical engineering can grow in two ways: towards analysis or synthesis [1]Analysis research tends to study more in-depth into the details of processes: from equipment design, to transport phenomena, to films and particles, and molecules. Synthesis research, on the other hand, tends to the study of increasing scope from process networks, to the plant, to the enterprise, and the biosphere or ecosystem. The same chemical engineering concepts are used: mass/energy balances, thermodynamics, physical laws, and kinetics. However, they now relate a more significant number of variables across larger systems.

                    Process Systems Engineering moves in the direction of synthesis research.

The primary driver in this research direction is the fact that human understanding is limited to a particular scale. Engineering decisions that make sense in a plant-wide scale might be detrimental on the ecosystem level [2]. Thus, only by increasing the scope of study can one truly understand how changes across different scales affect each other. It is said that PSE is in the best position to integrate all areas of chemical engineering. 

At the UP Department of Chemical Engineering, the PSE Laboratory (PSEL) only started to exist consciously in 2012. It is marked by the return of Dr. Jose Munoz to the UP teaching faculty, from his PhD in Taiwan. Since then, Dr. Munoz has been producing MS and PhD graduates under the auspices of PSEL.

Researchers in PSEL are encouraged to acquire skills in:
(1) Computer Science, (2) Applied Math, and/or (3) Statistics

(1) Computers are necessary for PSE research because of a large amount of data that needs to be handled to solve a system problem. A problem involving hundreds of equations or thousands of variables can only be efficiently solved using known algorithms and data-handling methods. Data mining and knowledge discovery (machine learning) in computer science are also currently applied to chemical engineering.

(2) Choosing the method of solution is where Applied Math comes in. Most of the time, problems in PSE are posed as optimization problems. Thus, the researcher might need some knowledge of mathematical programming, differential calculus, and matrix algebra. These concepts can appear in various problems: solving a set of differential-algebraic system of equations for process simulation; minimizing costs in sustainable process design while constrained by environmental limits and mass balances; and, representing systems as state-space models for process control.

(3) Lastly, PSE methods can be either model-driven or data-driven. Model-driven methods involve writing and solving rigorous first-principles models to simulate or predict actual system events. Data-driven methods, in contrast, rely on previous process system data with good enough quality to take the place of first-principles models for the same purpose. Either class has its own pros and cons. But apparently, Statistics is the key to proceed with the latter. Examples include the use of exploratory data analysis in extracting information from a plant’s history of measured data; multivariate statistical process monitoring for assessing plant health and fault detection; and, principal components analysis for dimensionality reduction of data in life cycle analysis.

Pursuant to the thrusts of the PSE laboratory, our researchers have interests in the following researches:

JCM – Dr. Jose Co Munoz
KSP – Karl Ezra S. Pilario
JOA – Jhud Mikhail O. Aberilla
BFS – Bemboy Nino F. Subosa
MMR – Miguel Francisco M. Remolona

  1. Mathematical Programming Methods [JCM, KSP, JOA]
  2. Evolutionary Optimization Methods [KSP, MMR]
  3. Sustainable Systems Design & Process Integration [JOA, KSP]
  4. Industrial Ecology & Life Cycle Assessment [JOA]
  5. Process Intensification [JOA, BFS]
  6. Computer-aided Process Modelling & Simulation [JOA, KSP, BFS]
  7. Sensor Development [JCM]
  8. Process Control & Process Monitoring [JCM, KSP]
  9. Big Data Analytics / Data Mining [KSP, MMR]
  10. Fault Detection and Diagnosis [JCM, KSP]

Now, to the young Chemical Engineering researcher, here are some journals that might further motivate you to pursue a research career in PSE:

  • Bakshi, B. R., & Fiksel, J. (2003). The Quest for Sustainability: Challenges for Process Systems Engineering. AIChE Journal, 49(6), 1350–1358.
  • Christofides, P. D., Davis, J. F., El-Farra, N. H., Clark, D., Harris, K. R. D., & Gipson, J. N. (2007). Smart Plant Operations: Vision, Progress, and Challenges. AIChE Journal53(11), 2734–2741. doi:10.1002/aic.11320
  • Grossmann, I. E., & Westerberg,  A. W. (2000). Research challenges in Process Systems Engineering. AIChE Journal, 46(9), 1700–1703.
  • Klatt, K., & Marquardt, W. (2009). Perspectives for process systems engineering — Personal views from academia and industry. Computers & Chemical Engineering, 33, 536–550.
  • Sargent, R. (2005). Process systems engineering: A retrospective view with questions for the future. Computers and Chemical Engineering, 29(6 SPEC. ISS.), 1237–1241.
  • Stephanopoulos, G., & Reklaitis, G. V. (2011). Process systems engineering: From Solvay to modern bio- and nanotechnology. A history of development, successes, and prospects for the future. Chemical Engineering Science, 66(19), 4272–4306. doi:10.1016/j.ces.2011.05.049