Process systems engineering is concerned with investigation on decision-making methodology for creation and operation of the chemical supply chain. This course covers the fundamentals of systems approach (modeling, simulation and optimization) in the fields of analysis, synthesis and operation of process systems. This course introduces chemical engineering application of the systems approach.
In recent years, problems that should be solved by chemical engineering become diversified and complicated, which is faced for building a sustainable society that enables to maintain development of economies by considering improvement of environment, safety and health. An aim of this course is to facilitate students' understanding of a wide variety of systems method and its application to analysis, synthesis and operation of process systems. Students will have the chance to tackle practical problems by applying knowledge acquired through the lecture.
At the end of this course, students will be able to:
1) Have an understanding of concept of systems thinking for analysis, synthesis and operation of chemical process systems, and deal with mathematical method related to modeling and simulation.
2) Deal with typical numerical solution for optimization problem that is essential to evaluation and decision-making.
3) Apply the above-mentioned mathematical methods to solve problem facing in the chemical engineering field.
process systems, modeling and simulation, optimization, process analysis and synthesis
|✔ Specialist skills||Intercultural skills||Communication skills||✔ Critical thinking skills||✔ Practical and/or problem-solving skills|
In the latter part of lecture for modeling & simulation and optimization of process systems, students are given practice problems related to what is taught on that day. Students are also given assignments in the lecture related to chemical engineering applications. Before coming to class, students should check what topics will be covered. Required learning should be completed outside of the classroom for preparation and review purposes.
|Course schedule||Required learning|
|Class 1||Systems and process systems||Students must be able to explain definition of process systems and basic concept of system thinking.|
|Class 2||The nature of optimization problems and problem formulation||Students must be able to explain characteristics of optimization problems and overviews of procedures of solving the problems in analysis and synthesis of process systems.|
|Class 3||Fitting models by least squares||Understanding of basic idea of least squares estimation is required.|
|Class 4||Applicaiton of least squares for analysis and synthesis of chemical processes||Understanding of application methods of least squares estimation for analysis and synthesis of chemical processes is required. Students must be able to estimate values of parameters for the regression model.|
|Class 5||Rigorous model：Modeling of lumped parameter systems and distributed parameter systems||Understanding of basic idea of rigorous models for analysis and synthesis of process systems is required. Students must be able to explain application methods of modeling of lumped parameter systems and distributed parameter systems.|
|Class 6||Dynamic analysis of chemical processes using rigorous models||Understanding of numerical methods for performing rigorous model calculations is required. Students must be able to explain characteristics of nonlinear behavior of chemical processes.|
|Class 7||Basic concepts of optimization||Understanding of characteristics of quadratic optimization problem is required. Students must be able to analyze characteristics of the optimization problem mathematically.|
|Class 8||Single variable optimization for analysis and synthesis of chemical processes||Students must be able to solve single variable optimization problems for analysis and synthesis of chemical processes , by using Newton's method.|
|Class 9||Unconstrained multivariable optimization of chemical processes||Students must be able to solve unconstrained multivariable optimization problems for analysis and synthesis of chemical processes, by using gradient method.|
|Class 10||Global Optimization of chemical processes using heuristic search methods||Understanding of characteristics of heuristic search methods (e.g. genetic algorithms) is required. Students must acquire basic concepts of chemical engineering application of the heuristic search methods.|
|Class 11||Basics of linear programming||Understanding of basic idea of linear programming is required.|
|Class 12||Applications of linear programming to synthesis of chemical processes||Students must be able to solve the optimization problems for synthesis of chemical processes, by linear programming.|
|Class 13||Basic concepts of data analysis of complex process systems||Students must understand the complexity of process systems and the basic concepts of data analysis of complex system.|
|Class 14||Applications of machine learning to analysis and modeling of chemical processes||Understanding of characteristics of machine learning methods (e.g. neural networks) is required. Students must acquire basic concepts of chemical engineering applications of the machine learning methods.|
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to course material or reference book.
Use the course materials to be distributed.
Kuroda, Chiaki ed.. Systems Analysis. Tokyo: Asakura Shoten. ISBN-13:978-4254256048
Students’ course scores are based on submitted reports to subjects and exercise problems.
Knowledge of process models related to unit operations and chemical reaction engineering is required to take this lecture.
If you have not studied the basics of chemical engineering in your undergraduate course, we recommend that you take other lectures on chemical engineering and then take “Process Systems Engineering” next year.
Students will use MATLAB/Simulink and Phython to deepen understanding of methods for modeling, simulation and optimization that they will learn in this lecture. We recommend that you prepare in advance to use MATLAB/Simulink and Phython.
※In Tokyo Tech, students can freely use MATLAB with the Campus-Wide license.
(We also recommend using Python.)