Industrial pragmatic modelling

Transcription

1 Industrial pragmatic modelling How select the proper level of complexity for industrial challenges? Stein Tore Johansen SINTEF NTNU 1

2 2 What is a model? A contextual and quantitative relation, explaining the relation between well defined and measurable quantities. A model can predict about future events Weather forecasts Tidal motion Planetary motion All models has some uncertainty A model can be based on: Pure theoretical relations Experiments Combination of experiments and theory Empirical input always needed 2

3 3 Why do modelling at all? - we have access to experience Experience is the natural base for all designs But, when we move outside the area of experience the experience must be replenished Very expensive and time consuming Examples: HF cleaning in Årdal in the early 1990s Original boiler design at Thamshavn was a disaster 3

4 4 Why do modelling at all? - we have access to experience Modelling may never be a driver to develop new processes, but should be a significant part of the toolbox As online process support and control becomes more and more popular, modelling is becoming a crucial element in successful industrial operation 4

5 5 Model example Refining of aluminium Melt refining reactor for H and Na removal Water model experiments Operational data from cast house Theoretical model derived, based on bubble size, mass transfer sub models, bubble residence time and experimental solubility data. Bubble size model developed, based on experiments Two competing strategies Make linear regressions on the experimental data, to be used as prediction model Tune the uncertain coefficients in the theoretically based model, based on the experimental data 5

6 6 Model example Refining of aluminium (cont) The theoretical model shows that the relations between variables are complex and highly non-linear Only the theoretical model has the hope to be fitted to the experimental data The linear fitting strategy cannot be defended as a model in this case, even if the fit can have a good correlation with the data!! 6

7 7 Recent example of combining theory and measurements Virtual metering of flow rates in oil & gas pipelines Use of transient 1D multiphase flow model (1D is a fully pragmatic model) Physics in model is as good as possible Field data has transient data from a few pressure and temperature sensors + accumulated volume data. (The more data available, the better) Simulation model continuously tuned to predict the measurements as close as possible. Method has been deemed more accurate than fiscal measurements!! 7

8 8 Pragmatism origin The purpose of modelling is twofold One is to form the new knowledge as mathematical relations, thereby saving the knowledge and making it easily available as mathematical relations and numerical models Second is to create prediction models needed by industry or to serve public needs 8

9 9 Pragmatism origin Pragmatic modelling starts always with a given application. The pragmatic model is the simplest model which can give fast, and sufficiently good answers. There could be a short step from a pragmatic model to online process control and operation support tools. A pragmatic model starts with the simplest possible model which has value for the user. 9

10 10 The pragmatic approach A model should have a well defined purpose Step 1: Describe accurately the purpose of the model and what quantitative output data the model shall produce Give time constraints: what is acceptable time to wait for the output (final result)? Give accuracy requirements: Result within +- 30%?, - or accuracy requirements higher for certainly events. This step should in general include: User Story: The purchaser of the model should describe the context of the model. Purpose of the model, how will it be used, consequences of mispredictions, expected input parameters. 10

11 11 The pragmatic approach Why accuracy requirements? Need to identify the model elements that has largest accuracy issues May be that what I personally wish to do has a marginal impact on the requested model output. The focus of the work must be on what is relevant in the actual context and not on personal preferences! Some time we spend huge resources on improving a model by 50 %, while input parameters may have order of magnitude issues. Accuracy can be quantified on many levels, but ultimately, only experiments can settle the final accuracy. At the same time we must deal with the experimental inaccuracy. Accuracy is a complex matter: We have statistical accuracy versus prediction power Weather forecasts has poorer prediction accuracy than predicting same weather as previous day. BUT, previous day weather as forecast has zero prediction power. 11

12 12 The pragmatic approach Why time requirements? All activity must have a given time frame Time from request to answer may range from microseconds to "infinite" (years) When time requirement is given: Some experiments and time consuming computations may be out of the question Make simplifications, use available information Estimate accuracy: Results may be delivered in time, but accuracy may not be reached Better accuracy may be developed over time: Well planned experiments Parametrized results from time consuming heavy computations 12

13 13 The pragmatic modelling System Architect Responsible for defining the tasks needed to deliver the final model results Break down the model challenge into physical, mathematical, computational, experimental and organizational elements. Equivalent with ICT description: " Such design includes a breakdown of the system in components, how these components interact together, and generally what technologies they employ." Delegating responsibilities to team leaders, assessed to have the necessary competence to return the requested information The System Architect is responsible for the modelling work to be completed without unexpected delays The Systems Architect is a senior with extensive experience in physics, numerics and experimental work. 13

14 14 Pragmatic elements Standards and tools Model may comprise a hierarchy of models and experiments Data exchange formats Tools/methods for trial planning Model sensitivity analyses evaluated against physical experiments Executing design tools (ModeFRONTIER/Dakota) Methods for parametrization / formats 14

15 15 Planning and analyses of experiments and computations Factorial designs High/low reduced test matrix High/low full matrix ANOVA (analyses of variance) Tools: Statistica, R, ++ 15

16 16 How quantify uncertainty? Quite new discipline Where is the uncertainty? Everywhere! Experiments All input data The physics of the model is crappy The numerical solution does not represent the physical model The model prediction has no relevance to solve the problem 16

17 17 Two cases Pragmatic: Elkem / Finnfjord / Fesil hood designs Non-pragmatic : Coupled multi scale computing, from atom scale to industrial scale, for online process control. 17

18 18 Summary of Industrial pragmatic modelling The requested output from the model is well defined, with required accuracy and time response The model is the simplest possible which can reach the requirements Requirements may be developed in steps: First the time requirements are fulfilled Next model is improved step by step to fulfil accuracy requirements (multi scale, parametrized) 18

19 19 Summary of Industrial pragmatic modelling What was the point here? Systematic use of knowledge: Start with simple but useful models Must be easy to reuse and build on to a model We put model usefulness first!! We may over time develop both complex and fast models, by using old information, new data and sub-model results in a systematic manner 19

20 20 Selftest: Is what we see here a pragmatic modeling example? Every second image are experiments compared to simulation. 20

21 21 References [1] J. Zoric, S. T. Johansen, K. T. Einarsrud, and A. Solheim, On pragmatism in industrial modeling, in CFD th International Conference on Computational Fluid Dynamics in the Oil & Gas, Metallurgical and Process Industries, Trondheim, 2014, pp [2] W. L. Oberkampf, S. M. DeLand, B. M. Rutherford, K. V. Diegert, and K. F. Alvin, Error and uncertainty in modeling and simulation, Reliab. Eng. Syst. Saf., vol. 75, no. 3, pp , [3] C. J. Roy and W. L. Oberkampf, A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing, Comput. Methods Appl. Mech. Eng., vol. 200, no , pp , Jun [4] J. C. Helton, J. D. Johnson, and W. L. Oberkampf, An exploration of alternative approaches to the representation of uncertainty in model predictions, Reliab. Eng. Syst. Saf., vol. 85, no. 1 3, pp , Jul