Browsing by Author "Borhani, Tohid N."

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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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    Integration of solid-oxide fuel cells and absorption refrigeration for efficient combined cooling, heat and power production
    (Oxford University Press, 2020-12-22) Matuszny, Krzysztof; Borhani, Tohid N.; Nabavi, Seyed Ali; Hanak, Dawid P.
    Combined cooling, heating and power (CCHP) systems are characterized by a substantially higher energyutilization efficiency compared to standalone systems. In this study, an integrated system comprising a solidoxide fuel cell (SOFC), hot-water storage tank (HWST) and absorption refrigeration (AR) cycle is considered. The SOFC model was developed in Aspen Plus®. It was used to determine the thermodynamic properties of the exhaust gas that was then used to provide heat for the HWST and to drive the AR cycle. Thermodynamic models for the AR cycles were developed in Engineering Equation Solver, considering LiBr–H2 O and NH3 –H2 O as working fluids. The sensitivity analysis of a number of SOFC output parameters has been carried out. The most optimal case was characterized with the coefficient of performance (COP) and CCHP efficiency of 0.806 and 85.2% for the LiBr–H2 O system, and 0.649 and 83.6% for the NH3 –H2 O system, respectively. Under such optimal operating conditions, the SOFC was characterized by the net electrical efficiency of 57.5% and the net power output of 123.66 kW. Data from the optimal solution were used to perform the thermodynamic study and sensitivity analysis to assess the influence of different absorption cycle operating conditions and to identify possible applications for the considered integrated systems
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    Molecular simulation techniques as applied to silica and carbon-based adsorbents for carbon capture
    (MDPI, 2023-06-28) Wadi, Basil; Golmakani, Ayub; Borhani, Tohid N.; Manovic, Vasilije; Nabavi, Seyed Ali
    There has been ongoing interest in research to mitigate climate change through carbon capture (CC) by adsorption. This guideline is meant to introduce computational chemistry techniques in CC by applying them to mesoporous structures and disordered morphologies. The molecular simulation techniques presented here use examples of literature studies on silica and carbon-based adsorbents. An initial summary of molecular simulation techniques and concepts is first presented. This is followed by a section on molecular simulation applications in mesoporous amorphous silica, both functionalized and not. Novel strategies to validate and output useful results are discussed, specifically when modelling chemisorption. The use of computational chemistry to build upon experimental results is reviewed, and a similar summation is presented for carbon-based adsorbents. The final section provides a short review of computational chemistry methods in novel applications and highlights potential complications. Computational chemistry techniques provide a streamlined method of gathering data across a range of conditions. Alongside experimental studies, these techniques can provide valuable information on underlying molecular mechanisms. This paper aims to be a starting point for navigating these numerical methods by providing an initial understanding of how these techniques can be applied to carbon capture while clarifying the current and inherent limitations present.