Kortelainen, Juha

Savolainen, Jouni

Tahkola, Mikko

English

VTT Research System

This document is downloaded from theVTT’s Research Information Portalhttps://cris.vtt.fiVTThttp://www.vtt.fiP.O. box 1000FI-02044 VTTFinlandBy using VTT’s Research Information Portal you are bound by thefollowing Terms & Conditions.I have read and I understand the following statement:This document is protected by copyright and other intellectualproperty rights, and duplication or sale of all or part of any of thisdocument is not permitted, except duplication for research use oreducational purposes in electronic or print form. You must obtainpermission for any other use. Electronic or print copies may not beoffered for sale.VTT Technical Research Centre of FinlandAI-SURSPEED - AI for faster process simulationTahkola, Mikko; Savolainen, Jouni; Kortelainen, JuhaPublished: 26/11/2019Document VersionPublisher's final versionLink to publicationPlease cite the original version:Tahkola, M., Savolainen, J., & Kortelainen, J. (2019). AI-SURSPEED - AI for faster process simulation. Postersession presented at AI Day 2019, Espoo, Finland.Download date: 13. Oct. 2022Conclusions✓ High accuracy can be achieved using artificial neural networks in the modelling of dynamic chemical processes.✓ A modular model could increase the overall accuracy; in another case, a single model was enough for very high accuracy (avg. NRMSE: ARX 0.07%, NARX 0.09%).✓ Generating data with higher sampling rate in the case might enhance the accuracy.✓ 300 x speed-up was achieved with the method. However, the achievable speed-up depends on the model complexity.IntroductionSystem simulation has been used as a tool in multipleapplications such as system design, optimisation and plantoperator training tasks. At the same time, more powerfulhardware is available all the time. This makes the simulationtools even more useful – but also makes applying AI to solveproblems easier. One challenge in system simulation is thatfor some applications, models might be too slow to run,limiting the usability. Two kinds of applications require orbenefit from accelerated simulation speed. Firstly, e.g. trainingsimulators require at least real-time speed. Other applicationsare such in which huge amount of simulations are executed.Machine learning in surrogate modellingIn this study, AI, or more specifically machine learning andartificial neural networks, are used to create a surrogate modelof a dynamic physics-based model to accelerate simulationspeed. Surrogate models in general are simplified versions oforiginal models. Although the physics-based models in thisstudy were built in Apros®, this method is not tied to a specificsoftware. The used surrogate modelling pipeline is presentedin Fig 1.More details of the study and the results can be found in [1].Figure 1. Overview of the surrogate modelling pipeline.ContactMikko TahkolaTel. +358 40 196 6541mikko.tahkola@vtt.fiVTT – beyond the obviouswww.vttresearch.com[1] Tahkola, M 2019. “Developing dynamic machine learning surrogate models ofphysics-based industrial process simulation models”. Master’s thesis, University of Oulu.Available http://jultika.oulu.fi/files/nbnfioulu-201906042313.pdf.[2] Götz, M et al. 2016. “Renewable Power-to-Gas : A Technological and EconomicReview.”, Renewable Energy 2016; 85:1371–90.AI-SURSPEED – AI for faster process simulationMikko Tahkola[1], Jouni Savolainen[2], Juha Kortelainen[1][1] VTT Technical Research Centre of Finland Ltd[2] Semantum LtdNeural networks in dynamic modellingSuitability of ARX, NARX, LSTM and GRU neural networks in multi-step-ahead predicting is studied. The modelling pipeline utilises Keras Python library and has following features.• Random hyperparameter optimisation.• Models are instantly tested in multi-step-ahead configuration.• Only values from one previous timestep were used.• Models were built using data with a 5s sampling time.Two case studies – Controlled water tank and methanation reactor in a power-to-gas processThe study included two case study models. The case 1 model isa simple water tank with an inlet and outlet pipe. The liquid levelof the tank is controlled by adjusting the inlet flow with a controlvalve and the outlet flow is adjusted using a pump. The case 2model was more complex – a methanation reactor in a power-to-gas process. Principle of a power-to-gas process is shown inFig 2 in which the studied methanation unit operation is markedwith dashed line.This project has received fundingfrom the European Union’sHorizon 2020 research andinnovation programme undergrant agreement No 723661Figure 2. Process diagram of a power-to-gas process, showing how methane is produced using H2 and CO2/CO (adapted from [2]).ResultsCase 1:▪ Both ARX and NARX had almost zero error compared to the original physics-based model (0.07 and 0.09 %, respectively).▪ Good generalization ability, i.e. handled un seen situations well.Case 2:▪ Average error of the 12 predicted variables on test data:• ARX 2.55 %, NARX 2.41 %, LSTM 1.94 %, GRU 3.60 %.• Some variables were predicted more accurately with thelinear ARX, some with nonlinear models.▪ Simulation speed:• In Apros: 50 × real-time.• Surrogate: 15 000 × real-time (the Keras model).ImpactAI methods can be used as a tool to build fast dynamicsurrogate models. This makes a single physics-based modelusable in wider range of applications and less work hours areneeded for remodelling. Faster simulation speed also contributeto getting results faster in engineering and R&D work,potentially increasing the overall work efficiency.

AI-SURSPEED - AI for faster process simulation

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AI-SURSPEED - AI for faster process simulation

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