Browsing by Author "Nkulikiyinka, Paula"

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    Application of machine learning in hydrogen production via the process of sorbent enhanced steam methane reforming.
    (Cranfield University, 2023-06) Nkulikiyinka, Paula; Clough, Peter T.; Manovic, Vasilije; Wagland, Stuart
    This thesis is focused on the exploration of the use of machine learning and computational methods for modelling process conditions and for materials screening within the process of sorbent enhanced steam methane reforming (SE- SMR) for carbon-abated hydrogen production. Hydrogen is a clean, abundant and versatile energy carrier that can be used for a wide range of applications. However, the production of hydrogen is still largely dependent on fossil fuels, which presents a significant challenge for achieving a truly sustainable energy system. The purpose of this study is to address this challenge by exploring novel approaches to hydrogen production, namely using machine learning, thermodynamic simulations, theoretical modelling, and the proposal of new methodologies and materials for low-carbon hydrogen production. Three main areas of work were conducted within this thesis, which include 1) two surrogate models have been developed and used to predict and estimate variables that would otherwise be difficult direct measured.; 2) applying machine learning, namely quantitative structure–property relationship analysis (QSPR) has been employed in the exploration of combined sorbent catalyst material (CSCM) for SE-SMR; and 3) applying machine learning to screen suitable metal organic frameworks (MOFs) for the storage of the produced blue hydrogen. Firstly, a surrogate model, was developed which was done by firstly simulating the model in Aspen Plus, applying a sensitivity analysis to gather a large dataset, then applying two multiple linear regression model, to observe the accuracy of predicting the gas concentration outputs. Two models were successfully developed with both models were accurate with high R² values, all above 98%. Secondly, the novel approach of QSPR with inductive transfer learning and datamining, was applied to develop two large databases of sorbent and catalyst properties, respectively. Then the developed machine learning models from these databases were applied, to predict the optimal conditions and precursor materials for the highest performing CSCM, in terms of last cycle capacity and methane conversion. Lastly, a similar approach was applied for the screening of MOFs for the storage of hydrogen by using multiple linear regression, simple geometric descriptors, and patterns in data to identify a better performing MOF than the currently reported experimental MOFs in literature.
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    Aspen Plus raw data
    (Cranfield University, 2020-11-19 15:56) Nkulikiyinka, Paula
    Aspen Plus raw data of the sensitivity analysis
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    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, Paula
    Databases and key
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    Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – A state-of-the-art review
    (Royal Society of Chemistry, 2021-11-01) Yan, Yongliang; Borhani, Tohid N.; Subraveti, Sai Gokul; Pai, Kasturi Nagesh; Prasad, Vinay; Rajendran, Arvind; Nkulikiyinka, Paula; Asibor, Jude Odianosen; Zhang, Zhien; Shao, Ding; Wang, Lijuan; Zhang, Wenbiao; Yan, Yong; Ampomah, William; You, Junyu; Wang, Meihong; Anthony, Edward J.; Manovic, Vasilije; Clough, Peter T.
    Carbon capture, utilisation and storage (CCUS) will play a critical role in future decarbonisation efforts to meet the Paris Agreement targets and mitigate the worst effects of climate change. Whilst there are many well developed CCUS technologies there is the potential for improvement that can encourage CCUS deployment. A time and cost-efficient way of advancing CCUS is through the application of machine learning (ML). ML is a collective term for high-level statistical tools and algorithms that can be used to classify, predict, optimise, and cluster data. Within this review we address the main steps of the CCUS value chain (CO2 capture, transport, utilisation, storage) and explore how ML is playing a leading role in expanding the knowledge across all fields of CCUS. We finish with a set of recommendations for further work and research that will develop the role that ML plays in CCUS and enable greater deployment of the technologies.
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    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.
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    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).
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    Python neural network and random forest code
    (Cranfield University, 2020-11-19 15:57) Nkulikiyinka, Paula
    Neural network and random forest coding in the Python IDE