Study programme 2023-2024Français
Process Modeling
Programme component of Master's in Chemical Engineering ansd Materials Science (MONS) (day schedule) à la Faculty of Engineering

CodeTypeHead of UE Department’s
contact details
Teacher(s)
UI-M1-IRCHIM-008-MCompulsory UEVITRY VéroniqueF601 - Métallurgie
  • DELAUNOIS Fabienne
  • VITRY Véronique
  • SIEBERT Xavier

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français121200022.001st term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
I-META-023Process modelisation66000Q1
I-MARO-033Analyse des données66000Q1

Overall mark : the assessments of each AA result in an overall mark for the UE.
Programme component

Objectives of Programme's Learning Outcomes

  • Imagine, design, implement and operate compounds, products and materials to specific properties and physical, chemical and biochemical solutions/processes leading to obtaining these materials by integrating needs, contexts and issues (technical, economic, societal, ethical, safety and environmental).
    • Identify complex problems to be solved and formulate the specifications by integrating client needs, contexts and issues (technical, economic, societal, ethical and environmental).
  • Mobilise a structured set of scientific knowledge and skills and specialised techniques in order to carry out missions of chemical engineering and materials science, using their expertise and adaptability.
    • Master and appropriately apply knowledge, models, methods and techniques specific to the field of chemistry and materials science.
    • Analyse and model a problem/process/producing pathway by critically selecting theories and methodological approaches (modelling, calculations), and taking into account multidisciplinary aspects.
    • Assess the validity of models and results in view of the state of science and characteristics of the problem.
  • Plan, manage and lead projects in view of their objectives, resources and constraints, ensuring the quality of activities and deliverables.
    • Respect deadlines and the work plan, and adhere to specifications.
  • Communicate and exchange information in a structured way - orally, graphically and in writing, in French and in one or more other languages - scientifically, culturally, technically and interpersonally, by adapting to the intended purpose and the relevant public.
    • Argue to and persuade customers, teachers and a board, both orally and in writing
    • Select and use the written and oral communication methods and materials adapted to the intended purpose and the relevant public.
    • Use and produce scientific and technical documents (reports, plans, specifications, etc.) adapted to the intended purpose and the relevant public.
  • Adopt a professional and responsible approach, showing an open and critical mind in an independent professional development process.
    • Show an open and critical mind by bringing to light technical and non-technical issues of analysed problems and proposed solutions.
    • Exploit the different means available in order to inform and train independently.
  • Contribute by researching the innovative solution of a problem in engineering sciences.
    • Adequately interpret the results taking into account the reference framework within which the research was developed.

Learning Outcomes of UE

Interpret the results of modelling with a critical mind; Interact with the people responsible for modelling to improve its quality ; Extract the significant data of a process to prepare modelling;
Being familiar with the most important modeling methods and their domains of application. Approach to process modeling by concrete examples from chemical engineering and materials science.
Being conscient of the role of modeling in industry and of the validation of models by experimental data (by the way of design of experiments).

UE Content: description and pedagogical relevance

Data analysis; Physical modeling; Analytical modeling methods: CALPHAD methods, ab initio modeling, molecular dynamics; Introduction to design of experiments.

Prior Experience

Not applicable

Type(s) and mode(s) of Q1 UE assessment

  • Written examination - Face-to-face

Q1 UE Assessment Comments

Integrated assessment: a single written exam for the 2 AA of the UE, including aspects of all aprts of the course. 

Method of calculating the overall mark for the Q1 UE assessment

Exam : 90%; reports from practicals: 10%

Type(s) and mode(s) of Q1 UE resit assessment (BAB1)

  • N/A - Néant

Q1 UE Resit Assessment Comments (BAB1)

-

Method of calculating the overall mark for the Q1 UE resit assessment

-

Type(s) and mode(s) of Q3 UE assessment

  • Written examination - Face-to-face

Q3 UE Assessment Comments

Integrated assessment: a single written exam for the 2 AA of the UE, including aspects of all aprts of the course. 

Method of calculating the overall mark for the Q3 UE assessment

Exam : 90%; reports from practicals: 10%

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
I-META-023
  • Cours magistraux
  • Travaux pratiques
I-MARO-033
  • Cours magistraux
  • Travaux pratiques
  • Projet sur ordinateur

Mode of delivery

AAMode of delivery
I-META-023
  • Face-to-face
I-MARO-033
  • Face-to-face

Required Learning Resources/Tools

AARequired Learning Resources/Tools
I-META-023Not applicable
I-MARO-033Slides and notes for practical sessions

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
I-META-023copies of presentations.
I-MARO-033Not applicable

Other Recommended Reading

AAOther Recommended Reading
I-META-023Introduction to materials modelling, ed. Zoe H. Barber, Maney, London, 2005
Computational Thermodynamics - The Calphad Method,  hans Lukas, Suzana Fries, Bo Sundman, Cambridge University Press, London, 2007.
I-MARO-033R.O.Duda, P.E.Hart, D.G.Stork. "Pattern Classification". John Wiley and Sons, 2000.
Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006.
R.E.Walpole, R.H.Myers, S.L.Myers, K.Ye, "Probability and Statistics for Engineers and Scientists", Prentice Hall, 2012
K P Murphy. Machine learning: a probabilistic perspective. MIT press, 2012.
(*) HT : Hours of theory - HTPE : Hours of in-class exercices - HTPS : hours of practical work - HD : HMiscellaneous time - HR : Hours of remedial classes. - Per. (Period), Y=Year, Q1=1st term et Q2=2nd term
Date de dernière mise à jour de la fiche ECTS par l'enseignant : 14/05/2023
Date de dernière génération automatique de la page : 27/04/2024
20, place du Parc, B7000 Mons - Belgique
Tél: +32 (0)65 373111
Courriel: info.mons@umons.ac.be