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  <doc>
    <id>32832</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>Vol. 178</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-02-12</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Catalytic Fast Pyrolysis of Lignocellulosic Biomass: Recent Advances and Comprehensive Overview</title>
    <abstract language="eng">Using biomass as a renewable resource to produce biofuels and high-value chemicals through fast pyrolysis offers significant application value and wide market possibilities, especially in light of the current energy and environmental constraints. Bio-oil from fast-pyrolysis has various conveniences over raw biomass, including simpler transportation and storage and a higher energy density. The catalytic fast pyrolysis (CFP) is a complex technology which is affected by several parameters, mainly the biomass type, composition, and the interaction between components, process operation, catalysts, reactor types, and production scale or pre-treatment techniques. Nevertheless, due to its complicated makeup, high water and oxygen presence, low heating value, unstable nature, elevated viscosity, corrosiveness, and insolubility within conventional fuels, crude bio-oil has drawbacks. In this context, catalysts are added to reactor to decrease activation energy, substitute the output composition, and create valuable compounds and higher-grade fuels. The study aim is to explore the suitability of lignocellulosic biomasses as an alternative feedstock in CFP for the optimization of bio-oil production. Furthermore, we provide an up-to-date review of the challenges in bio-oil production from CFP, including the factors and parameters that affect its production and the effect of used catalysis on its quality and yield. In addition, this work describes the advanced upgrading methods and applications used for products from CFP, the modeling and simulation of the CFP process, and the application of life cycle assessment. The complicated fluid dynamics and heat transfer mechanisms that take place during the pyrolysis process have been better understood due to the use of CFD modeling in studies on biomass fast pyrolysis. Zeolites have been reported for their superior performance in bio-oil upgrading. Indeed, Zeolites as catalyses have demonstrated significant catalytic effects in boosting dehydration and cracking process, resulting in the production of final liquid products with elevated H/C ratios and small C/O ratios. Combining ex-situ and in-situ catalytic pyrolysis can leverage the benefits of both approaches. Recent studies recommend more and more the development of pyrolysis-based bio-refinery processes where these approaches are combined in an optimal way, considering sustainable and circular approaches.</abstract>
    <parentTitle language="eng">Journal of Analytical and Applied Pyrolysis</parentTitle>
    <identifier type="doi">10.1016/j.jaap.2024.106390</identifier>
    <identifier type="issn">0165-2370</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Artikelnummer">106390</enrichment>
    <enrichment key="Fprofil">1 Energiewende und Dekarbonisierung / Energy Transition and Decarbonisation</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-García</lastName>
    </author>
    <submitter>
      <firstName>Harvey</firstName>
      <lastName>Arellano-García</lastName>
    </submitter>
    <author>
      <firstName>Hassan</firstName>
      <lastName>El Bari</lastName>
    </author>
    <author>
      <firstName>Casimir</firstName>
      <lastName>Kalibe Fanezoune</lastName>
    </author>
    <author>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </author>
    <author>
      <firstName>Thokozani</firstName>
      <lastName>Majozi</lastName>
    </author>
    <author>
      <firstName>Yasser</firstName>
      <lastName>Elhenawy</lastName>
    </author>
    <author>
      <firstName>Oussama</firstName>
      <lastName>Bayssi</lastName>
    </author>
    <author>
      <firstName>Ayoub</firstName>
      <lastName>Hirt</lastName>
    </author>
    <author>
      <firstName>Jorge</firstName>
      <lastName>Peixinho</lastName>
    </author>
    <author>
      <firstName>Asma</firstName>
      <lastName>Dhahak</lastName>
    </author>
    <author>
      <firstName>Mamdouh A.</firstName>
      <lastName>Gadalla</lastName>
    </author>
    <author>
      <firstName>Nourhan H.</firstName>
      <lastName>Khashaba</lastName>
    </author>
    <author>
      <firstName>Fatma</firstName>
      <lastName>Ashour</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Catalytic fast pyrolysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Lignocellulosic Biomass</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bio-Oil</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Modelling</value>
    </subject>
    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
  </doc>
  <doc>
    <id>33773</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>20240</pageFirst>
    <pageLast>20253</pageLast>
    <pageNumber/>
    <edition/>
    <issue>14</issue>
    <volume>2024</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-06-25</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Development of Ni-doped A-site lanthanides-based perovskite-type oxide catalysts for CO2 methanation by auto-combustion method</title>
    <abstract language="eng">Engineering the interfacial interaction between the active metal element and support material is a promising strategy for improving the performance of catalysts toward CO2 methanation. Herein, the Ni-doped rare-earth metal-based A-site substituted perovskite-type oxide catalysts (Ni/AMnO3; A = Sm, La, Nd, Ce, Pr) were synthesized by auto-combustion method, thoroughly characterized, and evaluated for CO2 methanation reaction. The XRD analysis confirmed the perovskite structure and the formation of nano-size particles with crystallite sizes ranging from 18 to 47 nm. The Ni/CeMnO3 catalyst exhibited a higher CO2 conversion rate of 6.6 × 10−5 molCO2 gcat−1 s−1 and high selectivity towards CH4 formation due to the surface composition of the active sites and capability to activate CO2 molecules under redox property adopted associative and dissociative mechanisms. The higher activity of the catalyst could be attributed to the strong metal–support interface, available active sites, surface basicity, and higher surface area. XRD analysis of spent catalysts showed enlarged crystallite size, indicating particle aggregation during the reaction; nevertheless, the cerium-containing catalyst displayed the least increase, demonstrating resilience, structural stability, and potential for CO2 methanation reaction.</abstract>
    <parentTitle language="eng">RSC Advances</parentTitle>
    <identifier type="url">https://pubs.rsc.org/en/content/articlelanding/2024/ra/d4ra02106a</identifier>
    <identifier type="doi">10.1039/d4ra02106a</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="RelationnotEU">03SF0678</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <enrichment key="Fprofil">1 Energiewende und Dekarbonisierung / Energy Transition and Decarbonisation</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-García</lastName>
    </author>
    <submitter>
      <firstName>Harvey</firstName>
      <lastName>Arellano-García</lastName>
    </submitter>
    <author>
      <firstName>Muddasar</firstName>
      <lastName>Safdar</lastName>
    </author>
    <author>
      <firstName>Nasir</firstName>
      <lastName>Shezad</lastName>
    </author>
    <author>
      <firstName>Farid</firstName>
      <lastName>Akhtar</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>perovskite</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>CO2 methanation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>lanthanide</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>auto-combustion method</value>
    </subject>
    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
  </doc>
  <doc>
    <id>34730</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>2</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject_ref</type>
    <publisherName>Ruhr Universität</publisherName>
    <publisherPlace>Bochum</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-11-27</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Homogeneous modeling for laminar flows in structured catalysts: CO2 methanation</title>
    <parentTitle language="eng">Book of Abstracts zur Jahrestagung der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik, 04. – 06. März 2024</parentTitle>
    <identifier type="url">https://www-docs.b-tu.de/fg-stroemungsmodellierung/public/Medina_2024_AbstractDechemaFluidverfahrenstechnik_Catalysts.pdf</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Publikationsweg">Open Access</enrichment>
    <enrichment key="Fprofil">1 Energiewende und Dekarbonisierung / Energy Transition and Decarbonisation</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Juan Ali</firstName>
      <lastName>Medina Méndez</lastName>
    </author>
    <submitter>
      <firstName>Juan Alí</firstName>
      <lastName>Medina Méndez</lastName>
    </submitter>
    <author>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </author>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-García</lastName>
    </author>
    <collection role="institutes" number="3504">FG Numerische Strömungs- und Gasdynamik</collection>
    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
  </doc>
  <doc>
    <id>34731</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>1</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>image</type>
    <publisherName/>
    <publisherPlace>Bochum</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-11-27</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Homogeneous modeling for laminar flows in structured catalysts: CO2 methanation</title>
    <identifier type="url">https://www-docs.b-tu.de/fg-stroemungsmodellierung/public/Medina_2024_Poster_DECHEMA2024_Fluidverfahrenstechnik.pdf</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Publikationsweg">Open Access</enrichment>
    <enrichment key="Fprofil">1 Energiewende und Dekarbonisierung / Energy Transition and Decarbonisation</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Juan Ali</firstName>
      <lastName>Medina Méndez</lastName>
    </author>
    <submitter>
      <firstName>Juan Alí</firstName>
      <lastName>Medina Méndez</lastName>
    </submitter>
    <author>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </author>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-García</lastName>
    </author>
    <collection role="institutes" number="3504">FG Numerische Strömungs- und Gasdynamik</collection>
    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
  </doc>
  <doc>
    <id>34681</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume>2899/2024</volume>
    <type>articler</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-11-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Revisiting homogeneous modeling with volume averaging theory: structured catalysts for steam reforming and CO2 methanation</title>
    <abstract language="eng">Progress in the modeling of structured catalysts is crucial for enhancing efficiency and scalability in industrial applications. Extensive research has investigated reactive flows over catalyst surfaces, covering chemical kinetics analysis and (direct) numerical simulations of the complete fluid flow in fixed-bed or structured catalysts. Nonetheless, this comes at a high computational cost. This study focuses on the homogeneous modeling of structured catalysts utilizing volume-averaging theory (VAT) as a more efficient method for representing the behaviour of such systems. We discuss modeling strategies for both 1-D and 3-D simulations. For steady 1-D flow simulations, we assess the influence of simplified gas chemical kinetics versus detailed surface chemistry, comparing with experimental data from the literature for a CO2 methanation processes. We also simulate 3-D flows of a steam reforming process, previously studied in the literature, using models which rely on different assumptions regarding the nature of the porous catalyst. Our findings reveal significant discrepancies based on different modeling assumptions, underscoring the necessity for accurate modeling of permeability and diffusivity tensors in homogeneous models.</abstract>
    <parentTitle language="eng">Journal of Physics: Conference Series</parentTitle>
    <identifier type="doi">10.1088/1742-6596/2899/1/012004</identifier>
    <identifier type="issn">1742-6596</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="Artikelnummer">012004</enrichment>
    <enrichment key="Publikationsweg">Open Access</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Fprofil">1 Energiewende und Dekarbonisierung / Energy Transition and Decarbonisation</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Juan Ali</firstName>
      <lastName>Medina Méndez</lastName>
    </author>
    <submitter>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </submitter>
    <author>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </author>
    <author>
      <firstName>Heiko</firstName>
      <lastName>Schmidt</lastName>
    </author>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-García</lastName>
    </author>
    <collection role="institutes" number="3504">FG Numerische Strömungs- und Gasdynamik</collection>
    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
  </doc>
  <doc>
    <id>34736</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>2</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject_ref</type>
    <publisherName>University of Technology</publisherName>
    <publisherPlace>Warsaw</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-11-27</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Revisiting homogeneous modeling with volume averaging theory: structured catalysts for steam reforming and CO2 methanation</title>
    <parentTitle language="eng">Book of Abstracts XXVI Fluid Mechanics Conference (FMC 2024), Warsaw, Poland, September 10-13, 2024</parentTitle>
    <identifier type="url">https://www-docs.b-tu.de/fg-stroemungsmodellierung/public/Medina_2024_AbstractFMC26_Catalysts.pdf</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Publikationsweg">Open Access</enrichment>
    <author>
      <firstName>Juan Alí</firstName>
      <lastName>Medina Méndez</lastName>
    </author>
    <submitter>
      <firstName>Juan Alí</firstName>
      <lastName>Medina Méndez</lastName>
    </submitter>
    <author>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </author>
    <author>
      <firstName>Heiko</firstName>
      <lastName>Schmidt</lastName>
    </author>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-García</lastName>
    </author>
    <collection role="institutes" number="3504">FG Numerische Strömungs- und Gasdynamik</collection>
    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
  </doc>
  <doc>
    <id>34525</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>3</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject_ref</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-11-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Novel parametric gradient calculation method for multistage systems with generalized constraints</title>
    <abstract language="eng">Sensitivity and gradient evaluations are essential for understanding the variability of a system subject to changes in input parameters, aiding in applications such as optimization, control, or decision-making processes (Castillo et al., 2008; Logsdon and Biegler, 1989, Horn and Tsai, 1967). Various approaches are available for the gradient evaluation in the simulation of large-scale steady-state systems, utilizing techniques such as automatic differentiation, sensitivity analysis, optimization or machine learning (Amaran et al., 2016). The term large-scale refers to problems with a substantial number of design variables, structural state variables, or constraint functions, or a combination thereof, necessitating significant high-performance parallel computing resources to solve within a reasonable timeframe (Kennedy and Martins, 2014).&#13;
&#13;
However, the evaluation of gradients in large-scale multistage systems simulation poses significant challenges due to computational complexity, numerical instability, scalability issues, and the limitations of the traditional differentiation techniques. Additionally, model complexity, sensitivity to noise, and data requirements of machine learning-based approaches further amplify these challenges. Overcoming these obstacles necessitates the development of efficient, scalable and robust gradient evaluation techniques that can effectively handle the characteristics of large-scale systems while offering reliable insights for a wide array of applications.&#13;
&#13;
&#13;
This contribution focuses on re-examining and advancing the evaluation of parametric sensitivities within the context of simulating highly complex, hierarchical multiscale modular systems of very large size. The models being analyzed may necessitate sensitivity evaluations concerning their response to parametric inputs. These evaluations serve not only to test and verify their robustness, but also to integrate them into modular structures within a comprehensive optimization framework. Such an optimization framework aims to enhance system performance based on selected criteria, while simultaneously adhering to essential optimality constraints.&#13;
&#13;
&#13;
While gradient-free optimization methods have been successfully applied to important design problems, their applications typically involve no more than O(102) design variables, and these methods exhibit very poor scalability with the dimensionality of the design variables (Kennedy and Martins, 2014). For large-scale, high-fidelity applications, gradient-based methods are deemed more suitable, although the challenges related to computational time and accuracy need to be addressed. To address these challenges, the use of either sensitivities or appropriately generalized adjoint equations for efficient calculation of constraint and objective functions gradients for generalized multistage systems, irrespective of whether they are dynamic in nature or they are steady-state.&#13;
&#13;
&#13;
The proposed approach adopts a generalized modular strategy suitable for any type of system, starting from a traditional sensitivity-based calculations initially, and subsequently developing a novel generalized adjoint-based method. The resulting algorithm comprises a sequence of forward and backward sweep computational steps, which are entirely equivalent, and serve as a generalization of the adjoint-based calculation methods for gradients of constraints. These methods find application in various numerical analysis computations related to dynamical systems, including optimal control problems.&#13;
&#13;
&#13;
It has to be noted that the model is regarded as a general modular representation of any coupled system, without making a distinction between dynamic or steady-state systems. In this context, a dynamic system is perceived as having state profiles as private internal variables, while interacting with its external environment through the input of initial conditions and parameter values. Its output consists of final conditions or any internal trajectory points that require reporting to the external environment during dynamic simulation. The proposed strategy using a novel adjoint scheme generalizes this approach to any multistage system model, of which the stages need not be of dynamic nature, such as in the use of adjoint equations in optimal control of multistage Differential- Algebraic Equation (DAE) systems (Morison and Sargent, 1986).&#13;
&#13;
&#13;
The choice between the use of the adjoint- and the sensitivity-based approach depends on the balance between the number of constraints/functions requiring gradient evaluation, and the number of states in the underlying dynamical system. The adjoint-based approach may be advantageous when dealing with a smaller number of constraints than state variables that require gradient evaluation, whereas the sensitivity-based approach could be more computationally efficient for a larger number of constraints than state variables in the modular treatment of the underlying dynamic system.&#13;
&#13;
&#13;
The simulation of a multistage system is demonstrated using an example consisting of steady-state feedforward blocks, employing both the sensitivity- and the proposed adjoint-based approach. The results obtained reveal that the numerical values derived from the gradient evaluation are identical for both methods. Therefore, it can be concluded that the newly introduced approach for general multistage sequential systems is entirely non-restrictive. This indicates its effectiveness and applicability, offering flexibility and robustness in gradient evaluation for such systems.</abstract>
    <parentTitle language="eng">2024 AIChE Annual Meeting</parentTitle>
    <identifier type="url">https://www.researchgate.net/publication/388185581_Novel_parametric_gradient_calculation_method_for_multistage_systems_with_generalized_constraints</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Fprofil">1 Energiewende und Dekarbonisierung / Energy Transition and Decarbonisation</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </author>
    <submitter>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </submitter>
    <author>
      <firstName>Vassileios</firstName>
      <lastName>Mappas</lastName>
    </author>
    <author>
      <firstName>Vassilios S.</firstName>
      <lastName>Vassiliadis</lastName>
    </author>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-García</lastName>
    </author>
    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
  </doc>
  <doc>
    <id>34527</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-11-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Advancements in CO2 methanation: customized heterogeneous Ni-Perovskite catalyst for sustainable SNG production</title>
    <parentTitle language="eng">Annual Meeting on Reaction Engineering and Electrochemical Processes 2024</parentTitle>
    <identifier type="url">https://www.researchgate.net/publication/388185658_Advancements_in_CO2_methanation_Customized_Heterogeneous_Ni-Perovskite_Catalyst_for_Sustainable_SNG_Production</identifier>
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    <author>
      <firstName>Muddasar</firstName>
      <lastName>Safdar</lastName>
    </author>
    <submitter>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
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    <author>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </author>
    <author>
      <firstName>Jefferson</firstName>
      <lastName>Santos da Silva</lastName>
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    <author>
      <firstName>Artur Jose</firstName>
      <lastName>Santos Mascarenhas</lastName>
    </author>
    <author>
      <firstName>Karen</firstName>
      <lastName>Valverde Pontes</lastName>
    </author>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-García</lastName>
    </author>
    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
  </doc>
  <doc>
    <id>34528</id>
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    <publishedYear>2024</publishedYear>
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    <completedDate>2024-11-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A unified modular framework for modeling multiphase reactors</title>
    <parentTitle language="eng">Annual Meeting of Process Engineering and Materials Technology 2024</parentTitle>
    <identifier type="url">https://www.researchgate.net/publication/388143505_A_unified_modular_framework_for_modeling_multiphase_reactors</identifier>
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    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Vassileios</firstName>
      <lastName>Mappas</lastName>
    </author>
    <submitter>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </submitter>
    <author>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </author>
    <author>
      <firstName>Norbert</firstName>
      <lastName>Heinzelmann</lastName>
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    <author>
      <firstName>Klaus</firstName>
      <lastName>Schnitzlein</lastName>
    </author>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-García</lastName>
    </author>
    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
  </doc>
  <doc>
    <id>34529</id>
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    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
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    <completedDate>2024-11-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards efficient material design: use of machine learning to predict chemical reactions and retrosynthesis</title>
    <parentTitle language="eng">Annual Meeting of Process Engineering and Materials Technology 2024</parentTitle>
    <identifier type="url">https://www.researchgate.net/publication/388143384_Towards_efficient_material_design_Use_of_Machine_Learning_to_predict_chemical_reactions_and_retrosynthesis</identifier>
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    <enrichment key="opus.source">publish</enrichment>
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    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Mitra</firstName>
      <lastName>Jafari</lastName>
    </author>
    <submitter>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
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    <author>
      <firstName>Parisa</firstName>
      <lastName>Shafiee</lastName>
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    <author>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
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    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-García</lastName>
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    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
  </doc>
  <doc>
    <id>34530</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <completedDate>2024-11-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Optimal design of a thermochemical hydrogen storage and release system via the reversible redox of iron oxide/iron</title>
    <parentTitle language="eng">Annual Meeting of Process Engineering and Materials Technology 2024</parentTitle>
    <identifier type="url">https://www.researchgate.net/publication/388143925_Thermochemical_Hydrogen_Storage_via_the_Reversible_Reduction_and_Oxidation_of_Metal_Oxides</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
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    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Richard</firstName>
      <lastName>Yentumi</lastName>
    </author>
    <submitter>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
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    <author>
      <firstName>Constantin</firstName>
      <lastName>Jurischka</lastName>
    </author>
    <author>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </author>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-García</lastName>
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    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
  </doc>
  <doc>
    <id>34531</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <completedDate>2024-11-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A novel approach to staggered training of deep learning networks</title>
    <parentTitle language="eng">Annual Meeting of Process Engineering and Materials Technology 2024</parentTitle>
    <identifier type="url">https://www.researchgate.net/publication/388143628_A_novel_approach_to_staggered_training_of_deep_learning_networks</identifier>
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    <enrichment key="opus.source">publish</enrichment>
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    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </author>
    <submitter>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </submitter>
    <author>
      <firstName>Vassilios S.</firstName>
      <lastName>Vassiladis</lastName>
    </author>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-García</lastName>
    </author>
    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
  </doc>
  <doc>
    <id>34532</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <completedDate>2024-11-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Process intensification by additive manufacturing strategies for power-to-X conversion application: Case studies</title>
    <parentTitle language="eng">16th International Conference on Gas–Liquid and Gas–Liquid–Solid Reactor Engineering</parentTitle>
    <identifier type="url">https://www.researchgate.net/publication/388185473_Process_Intensification_by_Additive_Manufacturing_Strategies_for_Power-to-X_Conversion_Application_Case_Studies</identifier>
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    <enrichment key="opus.source">publish</enrichment>
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    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Muddasar</firstName>
      <lastName>Safdar</lastName>
    </author>
    <submitter>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </submitter>
    <author>
      <firstName>Mutahar</firstName>
      <lastName>Safdar</lastName>
    </author>
    <author>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </author>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-García</lastName>
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    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
  </doc>
  <doc>
    <id>34533</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <completedDate>2024-11-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Sustainable aviation fuel production through Fischer-Tropsch synthesis and hydrocracking integration using Co bifunctional catalysts: Support effects</title>
    <abstract language="eng">Considering the increasing demand for clean and sustainable aviation fuel, in this study, cobalt bifunctional catalysts are used to convert syngas from biomass to aviation fuel.</abstract>
    <parentTitle language="eng">18th International Congress on Catalysis</parentTitle>
    <identifier type="url">https://www.researchgate.net/publication/388109868_Sustainable_aviation_fuel_production_through_Fischer-Tropsch_synthesis_and_hydrocracking_integration_using_Co_bifunctional_catalysts_Support_effects</identifier>
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    <enrichment key="opus.source">publish</enrichment>
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    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Mitra</firstName>
      <lastName>Jafari</lastName>
    </author>
    <submitter>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </submitter>
    <author>
      <firstName>Christel-Olivier Lenge</firstName>
      <lastName>Mbuya</lastName>
    </author>
    <author>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </author>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-García</lastName>
    </author>
    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
  </doc>
  <doc>
    <id>35026</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>583</pageFirst>
    <pageLast>588</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>114</volume>
    <type>articler</type>
    <publisherName/>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-01-08</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards Machine Learning-driven Catalyst Design and Optimization of Operating Conditions for the Production of Jet Fuel Via Fischer-Tropsch Synthesis</title>
    <abstract language="eng">Fischer-Tropsch synthesis (FTS) offers a promising route for producing sustainable jet fuels from syngas.&#13;
However, optimizing the catalyst design and operating conditions to maximize the desired C8-C16 jet fuel range&#13;
is a challenging task. This study introduces the application of a machine learning (ML) framework to guide the&#13;
design of Co/Fe-supported FTS catalysts and operating conditions for enhanced fuel selectivity. A&#13;
comprehensive dataset was constructed with 21 input features spanning catalyst structure, preparation method,&#13;
activation procedure, and FTS operating parameters. The random forest ML algorithm was evaluated for&#13;
predicting CO conversion and C8-C16 selectivity using this dataset. Feature engineering identified the most&#13;
significant descriptors influencing performance. A principal component analysis reduced the dataset&#13;
dimensionality prior to ML modelling. The random forest algorithm achieved high prediction accuracy for the&#13;
conversion of CO (R2 = 0.92) and C8-C16 selectivity (R2 = 0.90). In addition to confirming the known effects of&#13;
operating conditions, key roles of Co/Fe-supported properties were elucidated. This ML framework provides a&#13;
powerful tool for the rational design of FTS catalysts and operating windows to maximize jet fuel productivity</abstract>
    <parentTitle language="eng">Chemical Engineering Transactions</parentTitle>
    <identifier type="issn">2283-9216</identifier>
    <identifier type="url">https://www.cetjournal.it/cet/24/114/098.pdf</identifier>
    <identifier type="doi">10.3303/CET24114098</identifier>
    <enrichment key="Fprofil">1 Energiewende und Dekarbonisierung / Energy Transition and Decarbonisation</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <enrichment key="Fprofil">3 Globaler Wandel und Transformationsprozesse / Global Change and Transformation Processes</enrichment>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="Publikationsweg">Open Access</enrichment>
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    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <author>
      <firstName>Parisa</firstName>
      <lastName>Shafiee</lastName>
    </author>
    <submitter>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </submitter>
    <author>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </author>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-Garcia</lastName>
    </author>
    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
  </doc>
  <doc>
    <id>35027</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>577</pageFirst>
    <pageLast>582</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>114</volume>
    <type>articler</type>
    <publisherName/>
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    <completedDate>2025-01-08</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Multiphase Catalytic Reactors: a Modular Approach</title>
    <abstract language="eng">Currently, state-of-the-art approaches to simulating the behaviour of trickle-bed reactors (TBRs) have focused&#13;
solely on methods requiring high computational time and are unable to tackle systems with a large number of&#13;
particles. In this work, a modular methodology based on a Lagrangian approach to TBR modelling is presented,&#13;
which overcomes these drawbacks by implementing a simulation framework where different modules are&#13;
interconnected and relevant information is transferred between them. The novelty of this framework stems from&#13;
its adaptable configuration and its modular and unified setup, enabling it to accommodate both local and global&#13;
multiscale events. The proposed methodology includes modules for the packing generation, liquid flow&#13;
simulation, and of reaction system modelling within the reactor. To illustrate the procedure, a case study is&#13;
discussed while demonstrating the potential of the presented approach. The results were validated against data&#13;
obtained from a purpose-built experimental setup showing good agreement. The main advantages of this&#13;
approach lie in its efficiency, the interrelation between different modules, and its ability to capture a wide range&#13;
of information and phenomena.</abstract>
    <parentTitle language="eng">Chemical Engineering Transactions</parentTitle>
    <identifier type="issn">2283-9216</identifier>
    <identifier type="url">https://www.cetjournal.it/cet/24/114/097.pdf</identifier>
    <identifier type="doi">10.3303/CET24114097</identifier>
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    <enrichment key="Publikationsweg">Open Access</enrichment>
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    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <author>
      <firstName>Vasileios K.</firstName>
      <lastName>Mappas</lastName>
    </author>
    <submitter>
      <firstName>Dorneanu</firstName>
      <lastName>Bogdan</lastName>
    </submitter>
    <author>
      <firstName>Bogdan</firstName>
      <lastName>Dorneanu</lastName>
    </author>
    <author>
      <firstName>Norbert</firstName>
      <lastName>Heinzelmann</lastName>
    </author>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-Garcia</lastName>
    </author>
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  </doc>
  <doc>
    <id>35159</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>2024</volume>
    <type>bookpart</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-01-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Methods of indirect conversion of CO2 to methanol</title>
    <abstract language="eng">The promptly increasing CO2 concentration in the atmosphere causes a major climate change, requiring effective way of its mitigation. The indirect conversion of CO2 to methanol via syngas is a promising strategy to control greenhouse gas emissions and produce valuable feedstock's and chemicals. This chapter focuses on different indirect CO2 conversion methods to methanol, multistep processes that involve capturing of carbon dioxide, intermediate formation syngas, type of catalyst used, and then hydrogenation to methanol. Indirect conversion of CO2 involves two steps, the production of syngas which is known as a mixture of carbon monoxide and hydrogen followed by methanol integration and catalyst-based hydrogenation of CO2. The economic feasibility, the effectiveness of different methods, development, and optimization of catalysts along with reaction conditions are thoroughly discussed in this chapter. The chapter concluded with the direction of suitable methods to convert carbon dioxide into methanol along with the future research development in the methodology to reduce greenhouse emissions and advance the production of sustainable chemicals.</abstract>
    <parentTitle language="eng">Reference Module in Chemistry, Molecular Sciences and Chemical Engineering</parentTitle>
    <identifier type="doi">https://doi.org/10.1016/B978-0-443-15740-0.00155-5</identifier>
    <enrichment key="Fprofil">1 Energiewende und Dekarbonisierung / Energy Transition and Decarbonisation</enrichment>
    <enrichment key="Fprofil">3 Globaler Wandel und Transformationsprozesse / Global Change and Transformation Processes</enrichment>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
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    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <author>
      <firstName>Maria</firstName>
      <lastName>Jafar Khan</lastName>
    </author>
    <submitter>
      <firstName>Dorneanu</firstName>
      <lastName>Bogdan</lastName>
    </submitter>
    <author>
      <firstName>Muddasar</firstName>
      <lastName>Safdar</lastName>
    </author>
    <author>
      <firstName>Mitra</firstName>
      <lastName>Jafari</lastName>
    </author>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-Garcia</lastName>
    </author>
    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
  </doc>
  <doc>
    <id>35161</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>2024</volume>
    <type>bookpart</type>
    <publisherName/>
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    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-01-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Photocatalysts in CO2 direct conversion to methanol</title>
    <abstract language="eng">The escalating global industrialization has led to fossil fuel scarcity and environmental deterioration, with CO2 levels rising significantly. To combat climate change, researchers are focusing on renewable energy and carbon capture technologies. This chapter reviews recent progress on photocatalytic conversion of CO2 to methanol, a promising approach for greenhouse gas reduction and sustainable energy production. Methanol, a versatile chemical feedstock and potential renewable fuel, can be synthesized from CO2 using solar energy and semiconductor photocatalysts. This chapter covers the fundamentals, mechanisms, materials development, photocatalysts design strategies, and preparation processes for this technology. Despite challenges in achieving high efficiency, CO2 photocatalytic reduction to methanol offers an attractive green alternative to traditional fossil-based methanol production. This comprehensive overview consolidates the current research landscape, providing insights to guide future advancements towards scalable and economically viable CO2 photocatalytic methanol synthesis.</abstract>
    <parentTitle language="eng">Reference Module in Chemistry, Molecular Sciences and Chemical Engineering</parentTitle>
    <identifier type="doi">https://doi.org/10.1016/B978-0-443-15740-0.00121-X</identifier>
    <enrichment key="Fprofil">1 Energiewende und Dekarbonisierung / Energy Transition and Decarbonisation</enrichment>
    <enrichment key="Fprofil">3 Globaler Wandel und Transformationsprozesse / Global Change and Transformation Processes</enrichment>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
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    <author>
      <firstName>Parisa</firstName>
      <lastName>Shafiee</lastName>
    </author>
    <submitter>
      <firstName>Dorneanu</firstName>
      <lastName>Bogdan</lastName>
    </submitter>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-Garcia</lastName>
    </author>
    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
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  <doc>
    <id>35162</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>2024</volume>
    <type>bookpart</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-01-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Heterogeneous and Homogeneous Catalysts in CO2 Direct Conversion to Methanol</title>
    <abstract language="eng">Catalytic conversion of CO2 into valuable products offers a promising solution to mitigate climate change by closing the carbon cycle. However, activating the thermodynamically stable and kinetically inert CO2 molecule remains a significant scientific challenge. This chapter focuses on homogeneous and heterogeneous catalysts for the direct conversion of CO2 to methanol, a valuable chemical feedstock and potential fuel. It introduces the importance of this process for reducing carbon emissions and outlines the chapter's objectives. The fundamentals of heterogeneous catalysis and catalyst design principles for methanol synthesis from CO2 are discussed. Various types of heterogeneous catalysts are examined, along with the mechanisms involved in CO2 activation and hydrogenation to methanol. Strategies to enhance catalyst selectivity, product distribution, and performance are explored, as well as challenges and future research directions. This comprehensive chapter serves as a guide to understanding the pivotal role of heterogeneous catalysts in the direct catalytic conversion of CO2 to methanol.</abstract>
    <parentTitle language="eng">Reference Module in Chemistry, Molecular Sciences and Chemical Engineering</parentTitle>
    <identifier type="doi">https://doi.org/10.1016/B978-0-443-15740-0.00119-1</identifier>
    <enrichment key="Fprofil">1 Energiewende und Dekarbonisierung / Energy Transition and Decarbonisation</enrichment>
    <enrichment key="Fprofil">3 Globaler Wandel und Transformationsprozesse / Global Change and Transformation Processes</enrichment>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
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    <author>
      <firstName>Parisa</firstName>
      <lastName>Shafiee</lastName>
    </author>
    <submitter>
      <firstName>Dorneanu</firstName>
      <lastName>Bogdan</lastName>
    </submitter>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-Garcia</lastName>
    </author>
    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
  </doc>
  <doc>
    <id>35156</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
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    <volume>2024</volume>
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    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-01-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">CO2 sources and features for direct CO2 conversion to methanol</title>
    <abstract language="eng">In recent years, global concern over climate change caused by the accumulation of atmospheric CO2 has intensified. While various technologies for capturing CO2 have been proposed, utilizing captured CO2 from power plants is gaining popularity due to the concerns about the safety and effectiveness of underground and ocean storage methods. This article explores several techniques for utilizing CO2 from exhaust gases emitted by power plants. It provides a comprehensive review of current and emerging technologies worldwide that aim to harness CO2 for beneficial purposes. The conversion of CO2 into chemicals and energy products represents a promising approach to not only mitigate CO2 emissions but also enhance economic value. However, since CO2 lacks hydrogen, which is essential for many chemical processes, the development of clean, sustainable, and cost-effective hydrogen sources is crucial. This chapter delves into the literature surrounding the production of biofuels derived from microalgae cultivated using captured CO2, the conversion of CO2 combined with hydrogen into various chemicals, specially methanol and the exploration of sustainable hydrogen sources. These efforts collectively underscore the potential of CO2 utilization as a pivotal strategy in the battle against climate change and for fostering sustainable industrial practices.</abstract>
    <parentTitle language="eng">Reference Module in Chemistry, Molecular Sciences and Chemical Engineering</parentTitle>
    <identifier type="doi">https://doi.org/10.1016/B978-0-443-15740-0.00127-0</identifier>
    <enrichment key="Fprofil">1 Energiewende und Dekarbonisierung / Energy Transition and Decarbonisation</enrichment>
    <enrichment key="Fprofil">3 Globaler Wandel und Transformationsprozesse / Global Change and Transformation Processes</enrichment>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
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    <author>
      <firstName>Mitra</firstName>
      <lastName>Jafari</lastName>
    </author>
    <submitter>
      <firstName>Dorneanu</firstName>
      <lastName>Bogdan</lastName>
    </submitter>
    <author>
      <firstName>Harvey</firstName>
      <lastName>Arellano-Garcia</lastName>
    </author>
    <collection role="institutes" number="3603">FG Prozess- und Anlagentechnik</collection>
  </doc>
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