Browsing by Author "Asibor, Jude Odianosen"

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    Assessment of optimal conditions for the performance of greenhouse gas removal methods
    (Elsevier, 2021-06-18) Asibor, Jude Odianosen; Clough, Peter T.; Nabavi, Seyed Ali; Manovic, Vasilije
    In this study, a comparative literature-based assessment of the impact of operational factors such as climatic condition, vegetation type, availability of land, water, energy and biomass, management practices, cost and soil characteristics was carried out on six greenhouse gas removal (GGR) methods. These methods which include forestation, enhanced weathering (EW), soil carbon sequestration (SCS), biochar, direct air capture with carbon storage (DACCS) and bioenergy with carbon capture and storage (BECCS) were accessed with the aim of identifying the conditions and requirements necessary for their optimum performance. The extent of influence of these factors on the performance of the various GGR methods was discussed and quantified on a scale of 0–5. The key conditions necessary for optimum performance were identified with forestation, EW, SCS and biochar found to be best deployed within the tropical and temperate climatic zones. The CCS technologies (BECCS and DACCS) which have been largely projected as major contributors to the attainment of the emission mitigation targets were found to have a larger locational flexibility. However, the need for cost optimal siting of the CCS plant is necessary and dependent on the presence of appropriate storage facilities, preferably geological. The need for global and regional cooperation as well as some current efforts at accelerating the development and deployment of these GGR methods were also highlighted.
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    A country-level assessment of the deployment potential of greenhouse gas removal technologies
    (Elsevier, 2022-09-13) Asibor, Jude Odianosen; Clough, Peter T.; Nabavi, Seyed Ali; Manovic, Vasilije
    The deployment of greenhouse gas removal (GGR) technologies has been identified as an indispensable option in limiting global warming to 1.5 °C by the end of the century. Despite this, many countries are yet to include and promote this option in their long-term plans owing to factors such as uncertainty in technical potential, deployment feasibility and economic impact. This work presents a country-level assessment of the deployment potential of five GGR technologies, including forestation, enhanced weathering (EW), direct air carbon capture and storage (DACCS), bioenergy with carbon capture and storage (BECCS) and biochar. Using a multi criteria decision analysis (MCDA) approach consisting of bio-geophysical and techno-economic factors, priority regions for the deployment of these GGR technologies were identified. The extent of carbon dioxide removable by 2100 via these technologies was also estimated for each of the 182 countries considered. While the obtained results indicate the need for regional cooperation among countries, it also provides useful evidence on the need for countries to include and prioritise GGR technologies in their revised nationally determined contributions (NDCs).
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    Country-level assessment of the deployment potential of greenhouse gas removal technologies.
    (Cranfield University, 2023-07) Asibor, Jude Odianosen; Clough, Peter T.; Nabavi, Sayed Ali; Manovic, Vasilije
    The deployment of greenhouse gas removal (GGR) technologies has been identified as an indispensable option in meeting the warming target of 1.5 °C by the end of the century. Despite the importance of this pathway, the Nationally Determined Contributions (NDCs) of countries indicates a low intent to deploy these technologies. Among the major factors responsible for this low level of inclusion is the lack of robust country-level bio-geophysical and techno-economic feasibility assessments to ascertain national GGR deployment potential. Herein lies the challenge that this thesis aimed to address. This study investigated the potential of 182 countries to deploy five of the most promising GGR technologies, including forestation, enhanced weathering, direct air carbon capture and storage, bioenergy with carbon capture and storage, and biochar. A comparative literature-based assessment was carried out to identify and rank the major factors required for optimum performance of these GGR methods. Based on the bio-geophysical and techno-economic characteristics, Machine Learning (ML) was applied to identify the range of GGR technologies that respective countries can suitably and effectively deploy. ML models were also developed for predictive locational resource mapping of these technologies. Furthermore, the extent of carbon dioxide removable by 2100 via these technologies for each country (national potential) was evaluated using a Multi Criteria Decision Analysis approach. An assessment of domestic and regional sufficiency was also carried out to provide an evidence base for international collaboration. Priority regions for the deployment of these GGR technologies were identified, with Latin America and Sub-Saharan Africa regions found to have surplus potentials, and thus, expected to serve as a major hub to support other regions of the world. While the obtained results indicate the need for regional cooperation among countries, it also provides useful evidence on the need for countries to include and prioritise GGR technologies in their revised NDCs.
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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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    A machine learning approach for country-level deployment of greenhouse gas removal technologies
    (Elsevier, 2023-10-19) Asibor, Jude Odianosen; Clough, Peter T.; Nabavi, Seyed Ali; Manovic, Vasilije
    The suitability of countries to deploy five greenhouse gas removal technologies was investigated using hierarchical clustering machine learning. These technologies include forestation, enhanced weathering, direct air carbon capture and storage, bioenergy with carbon capture and storage and biochar. The use of this unsupervised machine learning model greatly minimises the likelihood of human bias in the assessment of GGR technology deployment potentials and instead takes a more holistic view based on the applied data. The modelling utilised inputs of bio-geophysical and techno-economic factors of 182 countries, with the model outputs highlighting the potential performance of these GGR methods. Countries such as USA, Canada, Brazil, China, Russia, Australia as well as those within the EU and Sub-Saharan Africa were identified as key areas suitable to deploy these GGR technologies. The level of certainty of the obtained deployment suitability categorisation ranged from 65 to 98 %. While the results show the need for regional collaboration between nations, they also highlight the necessity for nations to prioritise and integrate GGR technologies in their revised nationally determined contributions.
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    A machine learning approach for resource mapping analysis of greenhouse gas removal technologies
    (Elsevier, 2023-07-25) Asibor, Jude Odianosen; Clough, Peter T.; Nabavi, Seyed Ali; Manovic, Vasilije
    In this study, machine learning (ML) was applied to investigate the suitability of a location to deploy five greenhouse gas removal (GGR) methods within a global context, based on a location's bio-geophysical and techno-economic characteristics. The GGR methods considered are forestation, enhanced weathering (EW), direct air carbon capture and storage (DACCS), bioenergy with carbon capture and storage (BECCS) and biochar. An unsupervised ML (hierarchical clustering) technique was applied to label the dataset. Seven supervised ML algorithms were applied in training and testing the labelled dataset with the k-Nearest neighbour (k-NN), Artificial Neural Network (ANN) and Random Forest algorithms having the highest performance accuracies of 96%, 98% and 100% respectively. A case study of Scotland's suitability to deploy these GGR methods was carried out with obtained results indicating a high correlation between the ML model results and information in the available literature. While the performance accuracy of the ML models was typically high (76 - 100%), an assessment of its decision-making logic (model interpretation) revealed some limitations regarding the impact of the various input variables on the outputs.