Engineering topics
Publications and source records attributed to Jeong, Keunhong.
Computational predictions of vaporization properties aid the de novo design of green chemicals, including clean alternative fuels, working fluids for efficient thermal energy recovery, and polymers that are easily degradable and recyclable.
Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.
Deep eutectic solvents (DESs) are effective solvents for biomass conversion and have been proposed as alternatives to ionic liquids (ILs). In this paper, we first report an effective pretreatment of woody biomass using a novel hardwood lignin-based DES prepared from p -hydroxybenzoic acid (PB) and choline chloride (ChCl), which are used as the hydrogen bond donor and acceptor, respectively. The impacts of the ChCl-PB DES on structural properties and enzymatic hydrolysis of poplar wood were comprehensively evaluated. The performance of ChCl-PB DES was also compared with two other DESs with p -coumaric acid (PCA) and 4-hydroxybenzaldehyde (PHA) as the hydrogen bond donors, which have been successfully applied to herbaceous biomass conversion in previous studies. The results showed that hemicellulose and lignin were effectively fractionated during the proposed ChCl-PB DES pretreatment under a relatively mild temperature. The ChCl-PB DES pretreatment resulted in up to 69% of delignification, and the pretreated poplar residues had over 90% of glucose yield by a 72 h-enzymatic hydrolysis. Transformation of poplar biomass during the DES pretreatment was evaluated by gel permeation chromatography (GPC), two-dimensional heteronuclear single quantum coherence (2D-HSQC), and 31 P nuclear magnetic resonance (NMR) spectroscopies. The recovered lignin showed relatively high purity, narrow molecular weight distribution (PDI < 1.6), and relatively low molecular weight (~1400 g/mol for M w ). A sustainable process was achieved by recycling DES as well as the utilization of PB, which is a producible aromatic compound from hardwood lignin, for the proposed DES formation in the system. This study indicates that the proposed novel DES with a hardwood lignin-based PB compound is a promising pretreatment solvent to achieve an economically feasible biomass conversion process.