Integrated design of renewable fuels and their production processes: recent advances and challenges

König, Andrea; Marquardt, Wolfgang; Mitsos, Alexander; Viell, Jörn; Dahmen, Manuel

doi:10.1016/j.coche.2019.11.001

Cited by 17 publications

(12 citation statements)

References 48 publications

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“…A rather straightforward extension of that method would be the design of dual‐use fuels, that is, renewable fuels that in their pure form aim to exploit the efficiency and emissions potential of dedicated engines and in a blended state with a fixed amount of gasoline aim for compatibility with today's conventional engines. In the longer term, the integration of reduced‐order engine models into the optimization problem may allow consideration of fuel/engine interactions and thus contribute to further improvements in engine efficiency [37] . Another possible point of improvement is the harmonization of the global warming impact estimation in process network flux analysis (PNFA) and LCA.…”

Section: Discussionmentioning

confidence: 99%

“…In the longer term, the integration of reduced‐order engine models into the optimization problem may allow consideration of fuel/engine interactions and thus contribute to further improvements in engine efficiency. [37] Another possible point of improvement is the harmonization of the global warming impact estimation in process network flux analysis (PNFA) and LCA. Furthermore, to expand on the green toxicology approach, possible blend constituents should not only be pre‐screened for human toxicity but also for ecotoxicity.…”

Section: Discussionmentioning

confidence: 99%

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Designed to Be Green, Economic, and Efficient: A Ketone‐Ester‐Alcohol‐Alkane Blend for Future Spark‐Ignition Engines

et al. 2021

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Model‐based fuel design can tailor fuels to advanced engine concepts while minimizing environmental impact and production costs. A rationally designed ketone‐ester‐alcohol‐alkane (KEAA) blend for high efficiency spark‐ignition engines was assessed in a multi‐disciplinary manner, from production cost to ignition characteristics, engine performance, ecotoxicity, microbial storage stability, and carbon footprint. The comparison included RON 95 E10, ethanol, and two previously designed fuels. KEAA showed high indicated efficiencies in a single‐cylinder research engine. Ignition delay time measurements confirmed KEAA's high auto‐ignition resistance. KEAA exhibits a moderate toxicity and is not prone to microbial infestation. A well‐to‐wheel analysis showed the potential to lower the carbon footprint by 95 percent compared to RON 95 E10. The findings motivate further investigations on KEAA and demonstrate advancements in model‐based fuel design.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Designed to Be Green, Economic, and Efficient: A Ketone‐Ester‐Alcohol‐Alkane Blend for Future Spark‐Ignition Engines

et al. 2021

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“…Engine efficiencies can be increased significantly by increasing the compression ration or turbo charging, as shown in laboratory experiments. − In advanced spark-ignition (SI) engine concepts, the achievable engine efficiency strongly depends on the autoignition tendency of the fueland thus the fuel’s molecular structure or composition. Accordingly, alternative fuels have been found to vastly outperform RON95 gasoline in single-cylinder research engines. − The molecular structure of alternative, renewable fuels can be tailored in-silico by computer-aided molecular design (CAMD) methods, representing a special case of product design. , CAMD methods combine molecule databases or molecular structure generation with predictive models to assess the suitability of a candidate molecule for a given application based on physicochemical and thermodynamic properties. , To date, fuels are usually designed for individual physicochemical property targets as surrogate measures for engine performance rather than the expected engine efficiency itself …”

Section: Introductionmentioning

confidence: 99%

Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning

et al. 2023

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Co-design of alternative fuels and future spark-ignition (SI) engines allows very high engine efficiencies to be achieved. To tailor the fuel's molecular structure to the needs of SI engines with very high compression ratios, computer-aided molecular design (CAMD) of renewable fuels has received considerable attention over the past decade. To date, CAMD for fuels is typically performed by computationally screening the physicochemical properties of single molecules against property targets. However, achievable SI engine efficiency is the result of the combined effect of various fuel properties, and molecules should not be discarded because of individual unfavorable properties that can be compensated for. Therefore, we present an optimization-based fuel design method directly targeting SI engine efficiency as the objective function. Specifically, we employ an empirical model to assess the achievable relative engine efficiency increase compared to conventional RON95 gasoline for each candidate fuel as a function of fuel properties. For this purpose, we integrate the automated prediction of various fuel properties into the fuel design method: Thermodynamic properties are calculated by COSMO-RS; combustion properties, indicators for environment, health and safety, and synthesizability are predicted using machine learning models. The method is applied to design pure-component fuels and binary ethanol-containing fuel blends. The optimal pure-component fuel tert-butyl formate is predicted to yield a relative efficiency increase of approximately 8% and the optimal fuel blend with ethanol and 3,4-dimethyl-3-propan-2-yl-1-pentene of 19%.

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“…With the current methods, new feedstocks have not been validated because they fall outside of the application range. Data-driven models that link the prediction of pure component properties and mixture properties are, however, not yet available …”

Section: Introductionmentioning

confidence: 99%

“…Data-driven models that link the prediction of pure component properties and mixture properties are, however, not yet available. 38 In industrial laboratories, experimental equipment is sometimes available, but this is not always the case in academic research groups. However, gas chromatography is typically available.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Physicochemical Property Prediction of Complex Hydrocarbon Mixtures

Dobbelaere

Ureel

Vermeire

et al. 2022

Ind. Eng. Chem. Res.

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Machine learning has proven effective for predicting properties of pure compounds from molecular structures, but properties of mixtures, in particular oil fractions, are rarely dealt with. At best, the bulk properties are estimated based on pure compound properties, linear mixing rules, and a reconstructed composition of the feedstock. As the detailed composition of such mixtures is rarely well determined and often approximated by lumps, the accuracy of the estimated bulk properties can be improved. In this work, we demonstrate for a naphtha case study our bulk property estimation method. First, a detailed PIONA composition is delumped into a molecule-level composition, and a machine learning-based approach is used to predict properties of those molecules, which are further combined in another deep neural network for the prediction of bulk properties. The latter machine learning models are trained on mixture properties using vectors that represent the mixture. The first vector is a linear combination of the molecular representation vectors and the representation of the molecular geometries that make up the mixture. The second vector applies linear mixing rules on boiling temperatures, critical temperatures, liquid densities, and vapor pressures that are predicted with machine learning. The last vector consists of a learned distillation curve. We show that an integrated machine learning approach that starts from the molecular structures in the mixture offers significant improvements in predicting mixture properties over existing approaches applied in industry and academia.

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Integrated design of renewable fuels and their production processes: recent advances and challenges

Cited by 17 publications

References 48 publications

Designed to Be Green, Economic, and Efficient: A Ketone‐Ester‐Alcohol‐Alkane Blend for Future Spark‐Ignition Engines

Designed to Be Green, Economic, and Efficient: A Ketone‐Ester‐Alcohol‐Alkane Blend for Future Spark‐Ignition Engines

Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning

Machine Learning for Physicochemical Property Prediction of Complex Hydrocarbon Mixtures

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