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Computer-aided modeling for efficient and innovative product-process engineering

Computer-aided modeling for efficient and innovative product-process engineering. Supervisors: Gürkan Sin and Rafiqul Gani (CAPEC) Peter Glarborg (CHEC) Department of Chemical and Biochemical Engineering Danmarks Tekniske Universitet. Outline. Project motivation, Methodology,

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Computer-aided modeling for efficient and innovative product-process engineering

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  1. Computer-aided modeling for efficient and innovative product-process engineering Supervisors: Gürkan Sin and Rafiqul Gani (CAPEC) Peter Glarborg (CHEC) Department of Chemical and Biochemical Engineering Danmarks Tekniske Universitet

  2. Outline • Project motivation, • Methodology, • Modeling Tool, • Case Studies, • Conclusions.

  3. Motivation Goal: Development of a computer-aided modeling framework • Computer-aided modelling of increasing importance to face the future challenges product-process design. -> reduce number of experiments -> systematize trial-and-error approach -> integrated product and process design -> monitor product-process behavior • Model development step often time-consuming and non-trivial -> Provide a computer-aided framework for the model development, analysis, identification and application.

  4. Motivation Computer-aided modelling framework: • Systematically guide the user through the needed work-flows for the model development and application. • At each step of the work-flows integrate the required expertise, tools, database and library connections. • Benefits • -> increase efficiency of the model developing process (time+resources) • -> incorporate state-of-the-art methods • -> provide structure, guidance and support

  5. Methodology Structure of computer-aided modelling framework based on work-flows: Model Development Model Application Different work-flows for modelling – depending on: Single or multi-scale Current state of model Desired application

  6. Methodology

  7. Modeling Tool ICAS-MoT MoT

  8. Modeling Tool Documentation and re-use : (steps 1 and 2) Model development: (step 3) Model analysis: (step 3) Generation of multi-scale scenario: (step 4) Validation/ Evaluation/ Application: (steps 5, 6) Identified features of modeling tool for work-flow steps: Model documentation interface Connection to equation generation tools Linking schemes for models of different scales Scenario manager (-> store, access and compare all scenarios) Variable classi-fication and DOF analysis Enter model equations in txt-format Report generation incidence matrix Connection to property prediction tools Generic solver/ plot and export of results Connection to thermodyn. Libraries Translation and generation of model object, PDE discretization methods Optimal equation ordering / solution strategy Connection to process simulator Parameter estimation Model libraries Eigenvalue analysis Model aggregation Sensitivity analysis Save models/ scenarios for re-use Uncertainty analysis Interface to MS-Excel (and vPPDL)

  9. Modeling Tool

  10. Case Study 1 Thermal treatment of off-gas stream of adipic acid production ->decrease N2O -concentration N2O, NO, H2O, O2, N2, CO N2O, NO, H2O, O2, O3, N2, NH CO, OH, N H, H2, O, HO2, H2O2, NO2 NO thermal treatment in flow reactor adipic acid production off-gas product and other outputs Objective: Are model parameters identifiable by available experimental data? • System information: • 76 measurements of N2O concentration after different residence times and for different initial conditions • Details of model: 15 non-inert compounds, 44 reactions in the system, 157 unknown parameters (initial values from NIST chemical database)

  11. Case Study 1 Sensitivity analysis: Results for top 15 parameters:

  12. Case Study 1 Identifiability analysis: -> 502 possible parameter subsets, 134 identifiable Overview over results: -> max. identifiable parameter subset size: 5

  13. Case Study 2 Vapour Spraying of a fragrance product Mixture of active ingredients, solvents, additives and propellants is released from a pressurized can to atmosphere (Collaboration with Firmenich Inc., Plainsboro, NJ, USA) Liquid droplets Eddy diffusion Vsed h

  14. Case Study 2 Spraying of a fragrance product -> Modeling of the system/modeling goal can be divided into 2 parts: Part I: Release from can -> amount, composition and temperature of liquid and vapor, droplet size distribution Part II: Fate of the droplets (transport and evaporation) Status: • Models have been developed applying modeling framework • Experiments for validation are being performed (by Firmenich) • Future: • Validation and improvement of models • Idea to integrate in MS-Excel based vPPDL

  15. Conclusions Conclusions: • Computer – aided modeling tool for systematic development of single and multi-scale models, their analysis, interconnection, identification as well as solution and application has been developed. • Development and validation of modeling tool based on case studies from different areas in chemical engineering

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