Browsing by Author "Nkulikiyinka, Paula"
Now showing 1 - 5 of 5
Results Per Page
Sort Options
Item Open Access Aspen Plus raw data(Cranfield University, 2020-11-19 15:56) Nkulikiyinka, PaulaAspen Plus raw data of the sensitivity analysisItem Open Access Data supporting: 'Prediction of Combined Sorbent and Catalyst Materials for SE-SMR, Using QSPR and Multitask Learning'(Cranfield University, 2022-09-01 15:56) Nkulikiyinka, PaulaDatabases and keyItem Open Access Prediction of combined sorbent and catalyst materials (CSCM) for SE-SMR, using QSPR and multi-task learning(American Chemical Society, 2022-06-23) Nkulikiyinka, Paula; Wagland, Stuart T.; Manovic, Vasilije; Clough, Peter T.The process of sorption enhanced steam methane reforming (SE-SMR) is an emerging technology for the production of low carbon hydrogen. The development of a suitable catalytic material, as well as a CO2 adsorbent with high capture capacity, has slowed the upscaling of this process to date. In this study, to aid the development of a combined sorbent catalyst material (CSCM) for SE-SMR, a novel approach involving quantitative structure–property relationship analysis (QSPR) has been proposed. Through data-mining, two databases have been developed for the prediction of the last cycle capacity (gCO2/gsorbent) and methane conversion (%). Multitask learning (MTL) was applied for the prediction of CSCM properties. Patterns in the data of this study have also yielded further insights; colored scatter plots were able to show certain patterns in the input data, as well as suggestions on how to develop an optimal material. With the results from the actual vs predicted plots collated, raw materials and synthesis conditions were proposed that could lead to the development of a CSCM that has good performance with respect to both the last cycle capacity and the methane conversion.Item Open Access Prediction of sorption enhanced steam methane reforming products from machine learning based soft-sensor models(Elsevier, 2020-11-11) Nkulikiyinka, Paula; Yan, Yongliang; Güleç, Fatih; Manovic, Vasilije; Clough, Peter T.Carbon dioxide-abated hydrogen can be synthesised via various processes, one of which is sorption enhanced steam methane reforming (SE-SMR), which produces separated streams of high purity H2 and CO2. Properties of hydrogen and the sorbent material hinder the ability to rapidly upscale SE-SMR, therefore the use of artificial intelligence models is useful in order to assist scale up. Advantages of a data driven soft-sensor model over thermodynamic simulations, is the ability to obtain real time information dependent on actual process conditions. In this study, two soft sensor models have been developed and used to predict and estimate variables that would otherwise be difficult direct measured. Both artificial neural networks and the random forest models were developed as soft sensor prediction models. They were shown to provide good predictions for gas concentrations in the reformer and regenerator reactors of the SE-SMR process using temperature, pressure, steam to carbon ratio and sorbent to carbon ratio as input process features. Both models were very accurate with high R2 values, all above 98%. However, the random forest model was more precise in the predictions, with consistently higher R2 values and lower mean absolute error (0.002-0.014) compared to the neural network model (0.005-0.024).Item Open Access Python neural network and random forest code(Cranfield University, 2020-11-19 15:57) Nkulikiyinka, PaulaNeural network and random forest coding in the Python IDE