Bioethanol production from lignocellulosic biomass offers a sustainable alternative to fossil fuels; however, challenges persist in pretreatment efficiency and process integration. This study presents a one-pot, microwave-assisted process for ethanol production from sugarcane bagasse using ternary deep eutectic solvents (DES), combining pretreatment, enzymatic hydrolysis, and fermentation in a single vessel. Two DES systems, Choline chloride:Ethylene glycol:Nickel chloride hexahydrate (CC:EG:Ni) and Choline chloride:Ethylene glycol:Magnesium chloride (CC:EG:Mg) were evaluated across six Australian sugarcane varieties with varying lignin and cellulose content. The results demonstrate that DES pretreatment significantly enhanced biomass digestibility, increasing average glucose content from 37.2% to 50.2% (+34.8%) and reducing total lignin from 19.5% to 10.2% (−47.6%). The highest ethanol yield (46.9 mg/g SCB) was achieved using CC:EG:Ni with the QS08-8662 variety, demonstrating the influence of genotype on process performance. Structural characterisation (SEM, FTIR, XRD) confirmed lignin disruption and increased cellulose accessibility, correlating with improved enzymatic hydrolysis. DES recyclability was demonstrated using a centrifugal vacuum concentrator, with <10% solvent loss and only a 10.8% decline in performance after two cycles. These results highlight the potential of DES-based one-pot systems as a scalable and environmentally sustainable approach for lignocellulosic ethanol production. The study underscores the importance of feedstock selection and structural traits, such as lignin content and crystallinity, in optimizing process efficiency. This genotype-resolved evaluation and solvent-recovery pathway highlight DES-enabled one-pot SSF as a promising route toward integrated, sustainable ethanol production from sugarcane residues.
Reduced chemical reaction mechanisms for ethanol and biodiesel surrogates (methyl-butanoate and methyldecanoate) are developed using element flux analysis and genetic algorithm optimization. The optimized mechanisms are validated against detailed mechanisms and experimental data over a wide range of temperatures, pressures, and equivalence ratios. Quantitative comparisons of ignition delay, peak temperature, and selected species show that the optimized mechanisms reproduce detailed-model predictions within 1-5% for ethanol and 5-15% for methyl-butanoate, while non-optimized reduced mechanisms exhibit significantly larger deviations. The methodology is further assessed in a lattice Boltzmann reactive flow test and a single-cylinder HCCI engine simulation, demonstrating improved agreement in pressure profile evolution with substantially reduced computational cost. The present work focuses on mechanism development and validation; full threedimensional CFD engine simulations and detailed emissions validation are not included and are considered future work. The reduced mechanisms and parameters are provided in the Supplementary Material to support reproducibility.
Numerous studies have analyzed airline itinerary choice behavior to understand passenger preferences and the trade-offs they make among key flight service attributes such as total elapsed time, number of connections, and fare. However, limited attention has been given to evaluating the influence of latent attitudinal constructs on itinerary choice decisions. This study examines the influence of latent attitudes and demographic characteristics on passengers' itinerary choice behavior and identifies distinct patterns of preference heterogeneity among air travelers. Two modelling approaches using stated preference data collected from 614 respondents are employed. We begin by estimating a multiple indicators multiple causes (MIMIC) model, which incorporates latent attitudes identified through exploratory and confirmatory factor analysis. The model reveals three latent attitudinal factors, viz “comfort and hygiene conscious”, “in-flight service seeking” and “time and punctuality-oriented”. To capture heterogeneity in preferences, we extend our analysis using a latent class choice model (LCCM), incorporating socio-demographic characteristics and latent variables as class membership covariates. The LCCM results reveal two distinct flyer segments, each exhibiting different sensitivity to airline itinerary attributes. Class 1 accounts for approximately 78% of the sample, while Class 2 comprises the remaining 22%. The first segment (Class 1) displays high preference for reduction in connection time and elapsed time while the second segment (Class 2) reflects greater sensitivity to service quality (legroom and meals). Besides, Class 1 flyers display a strong inclination to nonstop itineraries, while Class 2 flyers show increased reception for connected itineraries. The willingness to pay for connection time and elapsed time reductions are $14 and $16, respectively, for Class 1 air travelers. While flyers falling under Class 2 are willing to pay $10 and $13, respectively, for 1 h reduction in connection time and elapsed time. Moreover, elasticity analysis indicates that Class 1 passengers exhibit a strong aversion to extended connection times on direct itineraries. Additionally, a unit fare increase on direct flights leads to a 2.33% shift toward nonstop options. Females and highly educated individuals are slightly more represented in Class 1, indicating demographic influences on latent class membership. The findings demonstrate variations in flyer preferences especially in the context of latent attitudes of individuals. By accounting for passengers’ willingness to pay for connection time, and total elapsed time specific to different passenger segments, airlines can implement differentiated pricing strategies tailored to passenger sensitivities, thereby optimizing revenue generation while ensuring equity.
Differential microphone arrays (DMAs) utilize signal subtraction between closely spaced sensors to approximate the spatial derivative of the acoustic pressure field, enabling frequency-invariant beampatterns within compact array geometries. This operating principle, however, makes DMAs inherently sensitive to positional inaccuracies, where even small deviations in sensor placement can significantly degrade array performance. In this work, the effects of position errors arising from array fabrication and mounting imprecision are systematically investigated. A geometric error model is proposed to quantify these effects in planar DMAs (PDMAs) by characterising sensor position inaccuracies into translational and rotational errors. An analytical formulation of the quantized beampattern for a general first-order PDMA is developed, and the impact of these errors is evaluated relative to the ideal case. Results indicate that even sub-millimeter translational errors significantly degrade null depth (ND) thereby reducing the array’s interference suppression capability while introducing minor beampattern variations at angles farther away from the nulls. In contrast, rotational errors cause more severe ND degradation, reduce the directivity index, and induce a uniform angular shift and distortion of the beampattern, displacing both mainlobe and nulls. Insights into how geometric errors vary with signal frequency, inter-sensor spacing, and array steering are presented. A method for compensating known position errors is also discussed. Experimental results validate the proposed error model for the representative TL and RL error cases.
The constitutive response of granular sands under high-strain-rate loading governs the finite-element analysis of blast, projectile, and impact problems on geotechnical infrastructure, yet available frameworks either capture critical-state plasticity without rate dependence and grain breakage, or capture breakage without coupling to a rate-dependent overstress. This paper presents a unified Dafalias-Manzari, Perzyna, and breakage constitutive model framework for granular sands across the triaxial and split-Hopkinson pressure bar (SHPB) regimes. Three coupling laws are introduced, namely a viscosity-breakage decay with an admissible floor, a damage-coupled shear modulus with an analogous floor, and a rate-dependent bounding-surface lift, each verified against the dissipation inequality. The framework is implemented as an Abaqus/Explicit VUMAT subroutine with adaptive Sloan-Abbo-Sheng substepping and is calibrated against the experimental dataset of (Rathore et al., 2026), spanning three sands of distinct grain shape and gradation (one crushed quartz sand and two natural river sands), confining pressures from 100 to 400 kPa under quasi-static triaxial loading, and nominal strain rates from approximately 670 to 1543 s−1 under SHPB loading. Every test passes the ± 10 % peak-axial-stress acceptance criterion, and only three parameters carry the rate dependence. The framework is directly deployable in finite-element analyses of high-strain-rate granular response.
