| Language of instruction : English |
| Exam contract: not possible |
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Sequentiality
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Advising sequentiality bound on the level of programme components
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Group 1 |
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Following programme components are advised to also be included in your study programme up till now.
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Chemical design and safety (3313)
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4.0 stptn |
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ICT tools for Chemical Engineers (4033)
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3.0 stptn |
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Process control (2624)
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3.0 stptn |
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Reactor engineering (3301)
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3.0 stptn |
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Separation processes (4335)
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3.0 stptn |
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Or group 2 |
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Following programme components are advised to also be included in your study programme up till now.
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Chemical design and safety (3313)
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4.0 stptn |
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Coding & Scripting transition (4293)
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3.0 stptn |
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ICT tools for Chemical Engineers (4033)
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3.0 stptn |
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Process control (2624)
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3.0 stptn |
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Reactor engineering (3301)
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3.0 stptn |
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Separation processes (4335)
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3.0 stptn |
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| Degree programme | | Study hours | Credits | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | Master of Chemical Engineering Technology optie duurzame procestechnologie | Compulsory | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical |  |
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| | | Learning outcomes |
- EC
| EC2 - The holder of the degree masters a comprehensive set of chemical techniques and technologies and is able to creatively conceptualise, plan and execute these as an integrated part of a methodologically and systematically ordered series of actions within a multidisciplinary project with a significant research and/or innovation component. | | | - DC
| DC1 - The student has knowledge of the basic concepts, structures and coherence.
| | | | - BC
| Student is able to understand the theory behind hybrid modelling, its techniques and tools. | | | - DC
| DC3 - The student can recognize problems, plan activities and perform accordingly.
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| Student is able to understand when the AI is a viable tool. | | | | - BC
| Student is able to select the adequate tool for modelling and control. | | | - DC
| DC4 - The student can gather, measure or obtain information and refer to it correctly.
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| Student is able to locate and use the adequate algorithms/libraries for modelling and control. | | | - DC
| DC7 - The student can use selected methods and tools to implement solutions and designs.
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| Student is able to implement the selected AI techniques for chemical process modelling and control problems. | - EC
| EC5 - The holder of the degree has advanced or specialist knowledge of and insight in most unit operations in the (bio)chemical industry and can integrate this knowledge and insight to creatively conceptualise and autonomously control and simulate chemical processes and to develop process optimisations within a multidisciplinary design context and with attention to topical technological developments and innovations. | | | - DC
| DC1 - The student has knowledge of the basic concepts, structures and coherence.
| | | | - BC
| The student knows the rules of pinch technology and can apply them. | | | - DC
| DC2 - The student has insight in the basic concepts and methods.
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| Student is able to criticize the response of the AI and can troubleshoot it. | | | | - BC
| The student shows insight in the application of pinch technology rules. | | | - DC
| DC3 - The student can recognize problems, plan activities and perform accordingly.
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| Student is able to recognize adequate scenarios/problems to implement AI techniques. | | | - DC
| DC5 - The student can analyze problems, logically structure and interpret them.
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| The student analyses the given process in order to design a heat exchanger network and analyses the obtained results. | | | - DC
| DC6 - The student can select methods and make calculated choices to solve problems or design solutions.
| | | | - BC
| The student can design a heat exchanger network for a given process. | - EC
| EC6 - The holder of the degree has advanced or specialist knowledge of, insight in and proficiency within a self-selected domain of specialisation of (bio)chemical process technology, materials, food and/or packaging. | | | - DC
| DC1 - The student has knowledge of the basic concepts, structures and coherence.
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| The data driven modelling tools, heuristics, code syntax is learned. | | | - DC
| DC2 - The student has insight in the basic concepts and methods.
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| The student can estimate which algorithm and solvers to use and can code accordingly with and without AI support. | | | - DC
| DC3 - The student can recognize problems, plan activities and perform accordingly.
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| The student can estimate which algorithm and solvers to use, which heuristic optimizer to apply and can code accordingly with and without AI support. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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DATA DRIVEN MODELLING AND CONTROL
1. INTRODUCTION
- Course introduction: teaching group, course material, suggested videos and books, timeline, evaluation method
- AI introduction: what is AI and its advantages, the categories of AI and applications, optimization in chemical engineering, AI methods applied to modeling, AI methods applied to control, AI methods applied to optimizations
- Python introduction: Python documents and learning materials, introduction to environments and IDEs, Python installation, introduction to python libraries used during the course
2. DATA PROCESSING
- Scaling techniques: scaling data to a range, scaling sparse data, scaling data with outliers, normalization
- Data cleaning from industrial processes: how a database is done, data flow on a plant, case specific measurement issues and how to overcome them, data filtering
3. BLACK BOX MODELLING
- Artificial Neural network: applications in chemical engineering, activation functions, multilayer perceptron, backpropagation algorithm, overfitting and early stopping, cross-validation
- Multivariate rational function: approximation of a function, ideal distribution for approximation, distribution in chemical engineering case, the structure of an multivariate rational function, difference between polynomial and multivariate rational function
- Heuristic optimization: particle swarm optimization and application in chemical engineering, differential evolution and application in chemical engineering
- Gradient-based optimization: differences with an heuristic optimization, gradient descent, stochastic gradient descent, learning rate, linear programming
4. HYBRID MODELLING
- Build and application of hybrid model in chemical case: different stucture of an hybrid model, the advantages to use an hybrid model rather a pure black-box or white-box, hybrid model application in chemical engineering
5. CONTROL
6. AI IN CHEMICAL CASES
- Reaction case
- Separation case
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Pinch technology: introduction & solving problems using Excel
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Application Lecture ✔
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Small group session ✔
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Exercises ✔
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Group work ✔
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Homework ✔
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Period 1 Credits 4,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 40 % |
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| Additional information | Almost every week there are very small graded homeworks.
There will be 2 larger individual homeworks during the semester. (Average of all homeworks - Weekly and larger : 15% of final grade-FG)
There will also be a test (scored practice session, closed book with open formulas 25% FG)
At the end of the semester the students will deliver reports. (Group work /50% FG)
This report will also be defended via a presentation (10% FG) |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Explanation (English) | The report (50% Final Grade-FG) can be corrected and send as second chance. The exam (25% FG) can be repeated. The other grades remain the same, no second chance for that part. |
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| Recommended course material |
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1. Links:
2. Books:
- “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurelien Geron
- “Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython” by Wes McKinney
- “Hybrid Modeling in Process Industries” by Jarka Glassey
- “Artificial intelligence in chemical engineering” by Thomas E. Quantrille
3. Papers:
- Venkatasubramanian, V. The Promise of Artificial Intelligence in Chemical Engineering: Is It Here, Finally? (2018) doi:10.1002/aic.16489.
- von Stosch, M., Oliveira, R., Peres, J. & Feyo de Azevedo, S. Hybrid semi-parametric modeling in process systems engineering: Past, present and future. Comput. Chem. Eng. 60, 86–101 (2014).
- Chiang, L., Lu, B. & Castillo, I. Big Data Analytics in Chemical Engineering. Annu. Rev. Chem. Biomol. Eng. 8, 63–85 (2017).
4. Software:
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Chemical process: design and integration, Robin Smith, Wiley, 9781119094418
Documents on electronic learning platform (Blackboard Ultra) |
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| Remarks |
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Orientation in the curriculum:
This course is part of the learning domain Proces design and engineering in the optie duurzame procestechnologie en kunststoffen. |
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| Exchange Programme Engineering Technology | Optional | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical | |
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DATA DRIVEN MODELLING AND CONTROL
1. INTRODUCTION
- Course introduction: teaching group, course material, suggested videos and books, timeline, evaluation method
- AI introduction: what is AI and its advantages, the categories of AI and applications, optimization in chemical engineering, AI methods applied to modeling, AI methods applied to control, AI methods applied to optimizations
- Python introduction: Python documents and learning materials, introduction to environments and IDEs, Python installation, introduction to python libraries used during the course
2. DATA PROCESSING
- Scaling techniques: scaling data to a range, scaling sparse data, scaling data with outliers, normalization
- Data cleaning from industrial processes: how a database is done, data flow on a plant, case specific measurement issues and how to overcome them, data filtering
3. BLACK BOX MODELLING
- Artificial Neural network: applications in chemical engineering, activation functions, multilayer perceptron, backpropagation algorithm, overfitting and early stopping, cross-validation
- Multivariate rational function: approximation of a function, ideal distribution for approximation, distribution in chemical engineering case, the structure of an multivariate rational function, difference between polynomial and multivariate rational function
- Heuristic optimization: particle swarm optimization and application in chemical engineering, differential evolution and application in chemical engineering
- Gradient-based optimization: differences with an heuristic optimization, gradient descent, stochastic gradient descent, learning rate, linear programming
4. HYBRID MODELLING
- Build and application of hybrid model in chemical case: different stucture of an hybrid model, the advantages to use an hybrid model rather a pure black-box or white-box, hybrid model application in chemical engineering
5. CONTROL
6. AI IN CHEMICAL CASES
- Reaction case
- Separation case
|
|
Pinch technology: introduction & solving problems using Excel
|
|
|
|
|
|
|
|
|
Application Lecture ✔
|
|
|
|
Small group session ✔
|
|
|
|
|
|
|
|
Exercises ✔
|
|
|
|
Group work ✔
|
|
|
|
Homework ✔
|
|
|
|
Period 1 Credits 4,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 40 % |
|
|
|
|
|
|
|
| Additional information | There will be 2 individual homework during the semester. (15% of final grade-FG)
There will also be a test (scored practice session, closed book with open formulas 25% FG)
At the end of the semester the students will deliver reports. (Group work /50% FG)
This report will also be defended via a presentation (10% FG) |
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | The report (50% Final Grade-FG) can be corrected and send as second chance. The exam (25% FG) can be repeated The other grades remain the same, no second chance for that part. |
|
|
|
|
|
| Recommended course material |
| |
1. Links:
2. Books:
- “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurelien Geron
- “Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython” by Wes McKinney
- “Hybrid Modeling in Process Industries” by Jarka Glassey
- “Artificial intelligence in chemical engineering” by Thomas E. Quantrille
3. Papers:
- Venkatasubramanian, V. The Promise of Artificial Intelligence in Chemical Engineering: Is It Here, Finally? (2018) doi:10.1002/aic.16489.
- von Stosch, M., Oliveira, R., Peres, J. & Feyo de Azevedo, S. Hybrid semi-parametric modeling in process systems engineering: Past, present and future. Comput. Chem. Eng. 60, 86–101 (2014).
- Chiang, L., Lu, B. & Castillo, I. Big Data Analytics in Chemical Engineering. Annu. Rev. Chem. Biomol. Eng. 8, 63–85 (2017).
4. Software:
|
| |
Chemical process: design and integration, Robin Smith, Wiley, 9781119094418
Documents on electronic learning platform (Blackboard Ultra) |
|
|
| Remarks |
| |
Orientation in the curriculum:
This course is part of the learning domain Proces design and engineering in the optie duurzame procestechnologie en kunststoffen. |
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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.16.9, section 2. |
3 Education, Examination and Legal Position Regulations art.15.1, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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