Integrated design of processes and products: Optimal renewable fuels

Air-liquid interface exposure of A549 human lung cells to characterize the hazard potential of a gaseous bio-hybrid fuel blend

Gaseous and semi-volatile organic compounds emitted by the transport sector contribute to air pollution and have adverse effects on human health. To reduce harmful effects to the environment as well as to humans, renewable and sustainable bio-hybrid fuels are explored and investigated in the cluster of excellence "The Fuel Science Center" at RWTH Aachen University. However, data on the effects of bio-hybrid fuels on human health is scarce, leaving a data gap regarding their hazard potential. To help close this data gap, this study investigates potential toxic effects of a Ketone-Ester-Alcohol-Alkane (KEAA) fuel blend on A549 human lung cells. Experiments were performed using a commercially available air-liquid interface exposure system which was optimized beforehand. Then, cells were exposed at the air-liquid interface to 50-2000 ppm C3.7 of gaseous KEAA for 1 h. After a 24 h recovery period in the incubator, cells treated with 500 ppm C3.7 KEAA showed significant lower metabolic activity and cells treated with 50, 250, 500 and 1000 ppm C3.7 KEAA showed significant higher cytotoxicity compared to controls. Our data support the international occupational exposure limits of the single KEAA constituents. This finding applies only to the exposure scenario tested in this study and is difficult to extrapolate to the complex in vivo situation.

An algorithmic framework for synthetic cost-aware decision making in molecular design

Small molecules exhibiting desirable property profiles are often discovered through an iterative process of designing, synthesizing and testing sets of molecules. The selection of molecules to synthesize from all possible candidates is a complex decision-making process that typically relies on expert chemist intuition. Here we propose a quantitative decision-making framework, SPARROW, that prioritizes molecules for evaluation by balancing expected information gain and synthetic cost. SPARROW integrates molecular design, property prediction and retrosynthetic planning to balance the utility of testing a molecule with the cost of batch synthesis. We demonstrate, through three case studies, that the developed algorithm captures the non-additive costs inherent to batch synthesis, leverages common reaction steps and intermediates, and scales to hundreds of molecules.

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

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.

Chemical data intelligence for sustainable chemistry

This study highlights new opportunities for optimal reaction route selection from large chemical databases brought about by the rapid digitalisation of chemical data. The chemical industry requires a transformation towards more sustainable practices, eliminating its dependencies on fossil fuels and limiting its impact on the environment. However, identifying more sustainable process alternatives is, at present, a cumbersome, manual, iterative process, based on chemical intuition and modelling. We give a perspective on methods for automated discovery and assessment of competitive sustainable reaction routes based on renewable or waste feedstocks. Three key areas of transition are outlined and reviewed based on their state-of-the-art as well as bottlenecks: (i) data, (ii) evaluation metrics, and (iii) decision-making. We elucidate their synergies and interfaces since only together these areas can bring about the most benefit. The field of chemical data intelligence offers the opportunity to identify the inherently more sustainable reaction pathways and to identify opportunities for a circular chemical economy. Our review shows that at present the field of data brings about most bottlenecks, such as data completion and data linkage, but also offers the principal opportunity for advancement.

Performance of Anisole and Isobutanol as Gasoline Bio-Blendstocks for Spark Ignition Engines

Several countries have set ambitious targets for the transport sector that mandate a gradual increase in advanced biofuel content in the coming years. The current work addresses this transition and indicates two promising gasoline bio-blendstocks: Anisole and isobutanol. The whole value chains of these bio-components were considered, focusing on end-use performance, but also analyzing feedstock and its conversion, well-to wheel (WTW) greenhouse gas (GHG) emissions and costs. Three alternative fuels, namely a ternary blend (15% anisole, 15% isobutanol, 70% fossil gasoline on an energy basis) and two binary blends (15% anisole with fossil gasoline and 30% isobutanol with fossil gasoline), were tested, focusing on their drop-in applicability in spark ignition (SI) engines. The formulated liquid fuels performed well and showed the potential to increase brake thermal efficiency (BTE) by 1.4% on average. Measured unburned hydrocarbons (HC) and carbon monoxide (CO) emissions were increased on average by 12–29% and 17–51%, respectively. However, HC and CO concentrations and exhaust temperatures were at acceptable levels for proper catalyst operation. The studied blends were estimated to bring 11–22% of WTW GHG emission reductions compared to base gasoline. Additionally, the fleet performance and benefits of flexi-fuel vehicles (FFV) were modeled for ternary blends.