Creating a machine learning based surrogate model for modelling of a carbon capture plant
Abstract
Dagens metoder for å gjøre en parameterstudie av et karbonfangstanlegg er ofteen treg prosess som krever mye innsats og manuell input. For å gjøre denne pros-essen mindre arbeidskrevende ble et neuralt nettverk konstruert i keras/tensorflowog trent på data produsert av CO2Sim. Modellen hadde en MAPE på 3.8103%, ogbåde responsvariablene og den økonomiske analysen gav verdier som var innen-for det man skulle forvente når man sammenlignet med verdier fra eksisterendeforskning. When conducting case studies of carbon capture plants, the current solutions thatexist are both tedious and time consuming. In order to speed up this process, aneural network model can be trained on data produced by a conventional simu-lation tool, and then in turn perform the case study. The neural network modelwas created using CO2Sim and keras/tensorflow and had a mean average percent-age error of 3.8103%, and returned both plant specifications and economic datathat are within the bounds of what one would expect when compared to existingliterature.