Reliable Kinetic Models Research Articles

Non-Invasive Quantification of the Brain [¹⁸F]FDG-PET Using Inferred Blood Input Function Learned From Total-Body Data With Physical Constraint.

Full quantification of brain PET requires the blood input function (IF), which is traditionally achieved through an invasive and time-consuming arterial catheter procedure, making it unfeasible for clinical routine. This study presents a deep learning based method to estimate the input function (DLIF) for a dynamic brain FDG scan. A long short-term memory combined with a fully connected network was used. The dataset for training was generated from 85 total-body dynamic scans obtained on a uEXPLORER scanner. Time-activity curves from 8 brain regions and the carotid served as the input of the model, and labelled IF was generated from the ascending aorta defined on CT image. We emphasize the goodness-of-fitting of kinetic modeling as an additional physical loss to reduce the bias and the need for large training samples. DLIF was evaluated together with existing methods in terms of RMSE, area under the curve, regional and parametric image quantifications. The results revealed that the proposed model can generate IFs that closer to the reference ones in terms of shape and amplitude compared with the IFs generated using existing methods. All regional kinetic parameters calculated using DLIF agreed with reference values, with the correlation coefficient being 0.961 (0.913) and relative bias being 1.68±8.74% (0.37±4.93%) for [Formula: see text] ( [Formula: see text]. In terms of the visual appearance and quantification, parametric images were also highly identical to the reference images. In conclusion, our experiments indicate that a trained model can infer an image-derived IF from dynamic brain PET data, which enables subsequent reliable kinetic modeling.

Catalytic hydrogenation reaction micro-kinetic model for dibenzyltoluene as liquid organic hydrogen carrier

The implementation of the liquid organic hydrogen carrier (LOHC) technology for efficient energy storage requires the development of a reliable kinetic model for both hydrogenation and dehydrogenation processes. In this research study, the catalytic hydrocarbon saturation for a dibenzyltoluene (DBT) mixture solution, containing dibenzylbenzene (DBB), dibenzylethylbenzene (DBEB) and impurities has been performed in the presence of Ru/Al2O3 particles. The influence of different reaction conditions, such as temperature, pressure, initial reactant concentration, catalyst amount and stirring speed has been examined. A measurement-based system micro-kinetics, based on the Langmuir–Hinshelwood mechanism with dissociative H2 surface adsorption, has been derived. H2 thermodynamic solubility equilibrium was defined through Henry's law. The adsorbing, desorption and reactivity of inert solvent molecules was not considered to be relevant. The mass transfer resistance over 1000 rpm stirring speed was negligible. Relative- and mean squared error of representation were 40.9% and 1.00×10−4, respectively. Expressions gave an excellent data prediction for the profile period trends with a relatively accurate estimation of H2 intermediates' rate selectivity, H2-covered area approximation and pathway rate-determining steps. Due to the lack of commercially available standard chemical compounds for quantitative analysis techniques, a novel experiment-based numerical calibration method was developed. Mean field (micro)kinetics represent an advancement in the mesoscale mechanistic understanding of physical interface phenomena. This also enables catalysis structure–activity relationships, unlocking the methodology for new LOHC reaching beyond traditional, such as ammonia, methanol and formate, which do not release H2 alone. Integrated multiscale simulations could include fluidics later on.

Enhancing the treatment efficiency of three-dimensional electro-Fenton system for organic wastewater: An analysis from the comprehensive perspective of H2O2 generation

For optimizing H2O2 generation in three-dimensional electro-Fenton (3D-EF) to enhance efficacy in treating organic wastewater, a comprehensive analysis from various perspectives of H2O2 generation was provided. By quantifying the generation of H2O2 within the reaction system and comparing it with the changing trends in organic wastewater treatment efficiency, a close correlation between H2O2 generation and organic wastewater treatment was revealed. A reliable and accurate kinetic model for H2O2 generation was formulated by building upon the reactions of H2O2 with electrodes and coexisting substances. The mathematical formulation of the kinetic model pinpointed pivotal factors influencing H2O2 generation, including current density, dissolved oxygen concentration, and the Fe catalytic activity substance. The experimental data and fitted results under various conditions indicated that the model could reasonably and accurately describe the relationship between pivotal factors and the quantity of generated H2O2. Crucial strategies were proposed to enhance H2O2 generation in the 3D-EF system by drawing from the impact rules of pivotal factors on H2O2 generation. These strategies primarily focused on improving electrode performance, optimizing reactor conditions, and refining particle electrode preparation processes.

Subgraph Isomorphic Decision Tree to Predict Radical Thermochemistry with Bounded Uncertainty Estimation.

Detailed chemical kinetic models offer valuable mechanistic insights into industrial applications. Automatic generation of reliable kinetic models requires fast and accurate radical thermochemistry estimation. Kineticists often prefer hydrogen bond increment (HBI) corrections from a closed-shell molecule to the corresponding radical for their interpretability, physical meaning, and facilitation of error cancellation as a relative quantity. Tree estimators, used due to limited data, currently rely on expert knowledge and manual construction, posing challenges in maintenance and improvement. In this work, we extend the subgraph isomorphic decision tree (SIDT) algorithm originally developed for rate estimation to estimate HBI corrections. We introduce a physics-aware splitting criterion, explore a bounded weighted uncertainty estimation method, and evaluate aleatoric uncertainty-based and model variance reduction-based prepruning methods. Moreover, we compile a data set of thermochemical parameters for 2210 radicals involving C, O, N, and H based on quantum chemical calculations from recently published works. We leverage the collected data set to train the SIDT model. Compared to existing empirical tree estimators, the SIDT model (1) offers an automatic approach to generating and extending the tree estimator for thermochemistry, (2) has better accuracy and R2, (3) provides significantly more realistic uncertainty estimates, and (4) has a tree structure much more advantageous in descent speed. Overall, the SIDT estimator marks a great leap in kinetic modeling, offering more precise, reliable, and scalable predictions for radical thermochemistry.

Extensive analysis of the applicability range of the linear kinetic approaches in the case of the pressure driven gas mixture flows

The study of gas mixture flows through micro- and macro-channels remains a very attractive area for theorists and experimentalists worldwide, mainly due to their great practical applicability in several aspects of science and industry. The present work includes a comparative study between the linear (McCormack model) and the nonlinear (DSMC method) kinetic theories of binary gas mixture flows through channels over a wide range of the involved parameters. The results show that the McCormack model is a reliable kinetic model for predicting the gas mixture flow behavior. Specific criteria with respect to the applicability range of the linear kinetic theory of short and long capillaries are proposed. The analysis shows that the separation phenomenon remains strong even in the case of small pressure drops. The applicability range of the linear kinetic theory for binary gas mixture flows driven by large pressure drops is obtained to be smaller compared to that of single gases but still wide enough covering a wide range of pressure ratios. Furthermore, the present work shows that the long capillary theory remains a very powerful tool for studying the gas mixture flow behavior under weak and strong nonequilibrium conditions. This work provides a kinetic database of the linear kinetic data (as supplementary material), which until today is still missing from the literature, and as it is shown throughout this work, it can be used far beyond the restrictions defined by the linear kinetic theory.

CO2 capture and utilization by biocatalytic synthesis of carboxylic acids: a process design study

The search for less energy-intensive processes is pushing toward a diversification of Carbon-Capture and Utilization/Sequestration (CCU/S) pathways. Biocatalytic processes offer sustainable solutions characterized by extremely mild operating conditions. Over the last decade, post-combustion CO2 capture based on enzymatic reactive absorption experienced remarkable growth from the laboratory to the demonstration scale. The present study explores a novel CCU concept based on phenol carboxylation catalyzed by cofactor-free decarboxylases. The design of the CCU process includes CO2 capture by alkaline solvents boosted by carbonic anhydrase followed by bicarbonate fixation via enzymatic phenol carboxylation. Phenols generated from fractional pyrolysis of biomass were assumed as carboxylation substrates. Two process layouts have been developed and modelled, and reliable experimental data and kinetic model supported the computations. Simulated best cases prove to be feasible options for integrating the enzyme cascade with the conventional CO2 absorption/desorption loop and lignocellulose biomass-derived substrates.

Boosting anionic dyes removal performance of polyacrylonitrile nanofibers by incorporating nitrogen-rich conjugated polymer

In this study, poly(1,5-diaminonaphthalene) (PDAN) as nitrogen-rich conjugated polymer was successfully synthesized and incorporated into polyacrylonitrile nanofibers (PAN NFs) by one-step-electrospinning technique. The fabricated poly(1,5-diaminonaphthalene)/polyacrylonitrile nanofiber composite (PDAN/PAN NFs) was characterized using various techniques. The modification of PAN NFs caused their surface to become rich in nitrogen-containing functional groups and conjugated CC bonds. Morphological characterization confirmed that the incorporation of PDAN into PAN NFs made their surface rougher and increased their average diameters from 197 ± 46–214 ± 46 nm. The incorporation of PDAN into PAN NFs boosted the adsorption capacity for the removal of anionic Amido Black 10B dye (AB10B) by 140 times (from 1 mg/g to 140 mg/g). The uptake of the AB10B dye from aqueous solutions by the PDAN/PAN NFs was investigated as a function of the pH value, the initial dye solution, temperature and contact period. The Dubbin-Radushkevich isotherm model was found to fit well with the equilibrium adsorption isotherm data, and the maximum adsorption capacity was determined to be 240.53 mg/g at pH 3 and 35 °C. The pseudo-second-order was a reliable kinetic model for predicting the adsorption of AB10B dye onto the PDAN/PAN NFs composite. The efficient and selective adsorption of the designed adsorbent is due to the nitrogen-rich functional groups and the π-conjugated system of PDAN. This study will pave the way for the boosting adsorption capacity and selectivity of membrane-type nanoadsorbent for the treatment of more toxic organic dyes.

Experimental investigation and modelling of the hydrate formation kinetics based on hydrate pore-scale distribution in porous media

Hydrate technology has a bright future for innovations in water, energy, and environmental applications. A reliable and applicable kinetic model of gas hydrate formation is essential for developing hydrate technology. In this work, the experiments of methane hydrate formation with different water saturation SA are conducted in the Cubic Hydrate Simulator (CHS), where the pressure profile and temperature distribution are measured. A new kinetic model integrating hydrate pore-scale distribution is established, assuming that the reaction area AS is controlled by the aqueous phase surface area along the gas-soluble direction. This new model is employed to replicate the experimental process numerically by the history-matching method, and the intrinsic reaction rate constant K0 is determined to be 850.0 mol/m2/Pa/s. In addition, we quantitatively distinguish the characteristics of the water pore-scale distribution in the excess-water, cementing-water, and excess-gas environments. The new model has been validated to be more excellent than those with four currently available models for the hydrate formation, and the sensitivities of the kinetic parameters are discussed in detail. The results show that the spatial distributions of the temperature T and hydrate saturation SH spatial distribution are heterogeneous in CHS due to the phase change and heat transfer effect. The excellent performance of the hydrate formation kinetics gives us strong confidence in the reliability and accuracy of the new model. This provides reliable predictions and mechanistic understandings of hydrate formation kinetics in the porous media.

Assessment of kinetic models for the production of γ-valerolactone developed in isothermal, adiabatic and isoperibolic conditions

The use of lignocellulosic biomass as raw materials for the production of biofuels is increasing. There are several potential processes valorizing these raw materials, but the shift from lab-scale to industrial scale requires the development of reliable and robust kinetic models. Usually, these models are developed in isothermal mode, limiting their use for thermal risk assessment or pinch analysis. We developed and assessed several kinetic models for the hydrogenation of butyl levulinate to γ-valerolactone over Ru/C in different thermal modes, i.e., isothermal, isoperibolic and adiabatic modes. The reaction calorimeter Mettler-Toledo RC1 was used to perform kinetic experiments. Bayesian inference was used during the regression stage to calculate the credible intervals. The validation stage was done by a holdout method. From the regression and validation stage, we found that the non-competitive Langmuir Hinshelwood with hydrogen non-dissociation and dissociation were the most reliable models. These models can predict the kinetics of this reaction system in different thermal modes.

Kinetic modelling: Regression and validation stages, a compulsory tandem for kinetic model assessment

AbstractThe development of robust and reliable kinetic models is vital to build safe, eco‐friendly, and cost‐competitive chemical processes. Establishing kinetic models for complex chemical systems such as biomass valorization is cumbersome because the kinetic modeller must test different models and fit several experimental observables (or concentrations). Usually, in chemical reaction engineering, kinetic model assessment is based solely on the regression stage outputs. The implementation of a validation stage can aid in choosing the most reliable kinetic models, essentially in the case of complex chemical systems. We studied the solvolysis of 5‐hydroxymethylfurfural (5‐HMF) to butyl levulinate (BL) as a model reaction constituting several consecutive and parallel reaction steps. From an existing kinetic model, we created 60 synthetic runs in batch conditions. In the first part, we tested four different models with 5 degrees of noise, and we carried out the modelling on the 60 synthetic runs. In the second part, two types of holdout methods were evaluated. In the last part, cross‐validation, namely the k‐fold method, was used. We found that the 10‐fold method allowed more efficient selection results even when the noise level was high. Besides, k‐fold allows for not scarifying experimental runs and selecting the most reliable model.

The initiation stage of thermochemical sulfate reduction: An isotopic and computational study

Thermochemical sulfate reduction (TSR) is one of the most important organic-inorganic reactions in petroleum reservoirs which significantly affects production and processing risks. The initiation (non-catalyzed) stage of TSR is critical in constructing a reliable kinetic model to predict this reaction. There is a large gap in our understanding of the mechanisms involved in this stage. In the present study, we used hydrous pyrolysis experiments with Na2SO4 and 1-dodecene as model compounds at pH= 1, and temperatures of 250 °C and 300 °C to simulate this early TSR stage. We measured and calculated the sulfur isotopic fractionations of sulfate, H2S and individual organic sulfur compounds (OSC). A thermodynamic model was used to determine the concentrations of the different sulfate species (H2SO4, HSO4¯, SO42-) at the experimental conditions. We then used ab initio calculations to determine possible radical and non-radical reaction mechanisms to explain the experimental results. During all the experiments, sulfate was consumed gradually while H2S and OSC were produced. The residual sulfate was 34S enriched up to 10.7‰ relative to the initial sulfate as pyrolysis time advanced. The produced H2S and OSC were typically about 21‰ 34S depleted relative to co-existing sulfate. The formation of OSC, such as C24 sulfides and C12 thiophenes, during the experiments and their similar δ34S values, suggest a common precursor and that radical mechanisms are involved in their formation. Calculated activation energies and free energies (Ea and ∆G‡, respectively) for the radical TSR mechanisms (20.6–34.1 and 32.3–44.4 kcal mol−1, respectively) yielded lower values than the non-radical TSR mechanisms (53.5–68.5 and 59.7–78.1 kcal mol−1, respectively). However, the calculated kinetic fractionation factors for the non-radical mechanism correlated better with the 11–12‰ experimental fractionation factor of current and previous studies. This similarity between the experimental and computational results indicates that these non-radical rather than radical mechanisms were likely involved in the reduction of sulfate in our experiments. Despite the lower activation energies, the radical TSR mechanisms are not dominant in our experimental system, probably due to competing reactions of the active radical species (alkyl and allyl radicals). Nevertheless, under natural conditions, the significantly lower amount of alkenes is expected to reduce the competition on the active alkyl and allyl radicals for alkylation, making these radical mechanisms competing pathways for the initiation stage of TSR.

Selective Oxidation of Methanol to Green Oxygenates – Feasibility Study of Fixed‐Bed and Membrane Reactors

AbstractA feasibility study of a conventional fixed‐bed reactor and a packed‐bed membrane reactor (PBMR) in distributor configuration is carried out for the selective oxidation of methanol to the oxygenates dimethoxymethane and methyl formate on a VOx/TiO2 catalyst. Kinetic experiments provided the reaction network and a reliable kinetic model of six main reactions involved. The PBMR offers significant advantages and potential for the formation of both target products due to an effective kinetic coupling of the oxidative dehydrogenation of methanol and the consecutive reaction steps to dimethoxymethane and methyl formate in a broad range of operation conditions.

In-Depth Kinetic Modeling and Chemical Analysis for the Epoxidation of Vegetable Oils in a Liquid–Liquid–Solid System

A heterogeneous catalyst for producing epoxidized vegetable oils, an important intermediate in the production of non-isocyanate polyurethanes, is essential for product separation and for decreasing the side-reaction, i.e., ring-opening reaction, via the Prileschajew method. The development of reliable kinetic models considering key variables for both phases and the mass transfer phenomena is missing in the literature. The reaction pathway for the ring-opening reaction is also under debate. Therefore, we studied the kinetics of epoxidation of cottonseed oil by perpropionic acid over the solid acid catalyst amberlite IR-120. An in-depth kinetic model was developed by using Bayesian inference. The reaction pathway for the ring opening was investigated. Propionic acid, a weak acid, allows for a decrease in the oxirane ring-opening side reaction.

Revisit the pathway and kinetics for transformation of light olefins over ZSM-5 modeling: Influence of steam-pretreatment

Previous kinetic studies on light olefins transformation over ZSM-5 catalyst were carried out either with pristine zeolite powders (research catalyst), or with commercial catalysts (technical catalyst) as-received without steam pretreatment, meanwhile, a rational procedure to discern the reaction network was lacking, thus a gap inevitably existed between the kinetic model established and the industrial practice. Herein, we revisited the kinetics of light olefins (C3–C7) interconversion over the commercial ZSM-5 catalyst, and special attention was paid to the effect of steam treatment. Comparative analysis was performed between the pristine and the steam-treated catalysts in terms of acid property, catalytic activity, and deactivation behavior. With the help of Delplot analysis, a rational and reliable kinetic model was developed for olefins interconversion as simple as possible, and a temporal kinetic model for catalyst deactivation was also proposed. These models can provide kinetic foundations for design and retrofit of MTO or MTP reactors.

Simulating the flow of interacting ferrofluids with multiparticle collision dynamics.

Ferrofluid flow is fascinating since its fluid properties can conveniently be manipulated by external magnetic fields. Novel applications in micro- and nanofluidics as well as in biomedicine have renewed the interest in the flow of colloidal magnetic nanoparticles with a focus on small-scale behavior. Traditional flow simulations of ferrofluids, however, often use simplified constitutive models and do not include fluctuations that are relevant at small scales. Here we address these challenges by proposing a hybrid scheme that combines the multiparticle collision dynamics method for modeling hydrodynamics with Brownian dynamics simulations of a reliable kinetic model describing the microstructure, magnetization dynamics, and resulting stresses. Since both multiparticle collision dynamics and Brownian dynamics are mesoscopic methods that naturally include fluctuations, this hybrid scheme presents a promising alternative to more traditional approaches, also because of the flexibility to model different geometries and modifying the constitutive model. The scheme was tested in several ways. Poiseuille flow was simulated for various model parameters and effective viscosities were determined from the resulting flow profiles. The effective, field-dependent viscosities are found to be in very good agreement with theoretical predictions. We also study Stokes' second flow problem for ferrofluids. For weak amplitudes and low frequencies of the oscillating plate, we find that the velocity profiles are well described by the result for Newtonian fluids at the corresponding, field-dependent viscosity. Furthermore, the time-dependent profiles of the nonequilibrum magnetization component are well approximated by their steady-state values in stationary shear when evaluated with the instantaneous local shear rate. Finally, we also apply our scheme to simulate ferrofluid shear flow over a rough surface. We find characteristic differences in the nonequilibrium magnetization components when the external field is oriented in flow and in a gradient direction.

Development of mathematical model for predicting methane-to-carbon dioxide proportion in anaerobic biodegradability of cattle blood and rumen content

• Prediction of methane-to-carbon dioxide within mesophilic range. • Production of methane for use as renewable energy source. • Optimal methane-to-carbon dioxide ratio in relation to temperature and hydraulic retention time. The potential of methane yield depends on the composition of feedstock which is characterized by a diversity of biodegradable fractions. This study aims at constructing and using the novel kinetic model as a tool to predict methane-to-carbon dioxide ratio from slaughterhouse solid waste using hydraulic retention time and temperature as parameters. The slurries prepared were composed of cattle blood, rumen content and requisite nutrient solution which were fed into a continuously stirred tank reactors. The process was monitored at regular intervals of 3 days for a retention time of 54 days for thermostatically controlled temperatures of 35, 37 and 40 °C. The percentage methane and carbon dioxide gas concentrations in the headspace were measured. The phenomenon was idealized with scatter plots and co-efficient of determination used to establish a reasonable correlation between the identified parameters. In this study, simple, robust and reliable kinetic models have been developed using the Microsoft Excel Add-in Solver Tool with the Generalized Reduced Gradient. The optimum solution of seven unknown constants represented by adjusted values where simulated values adequately matched the observed values for the three zones of this study. Validation of the models also showed that the phenomenon can be produced in a continuum at any fixed temperature.

Recent Progress in the Synergistic Bactericidal Effect of High Pressure and Temperature Processing in Fruits and Vegetables and Related Kinetics.

Traditional thermal processing is a widely used method to ensure food safety. However, thermal processing leads to a significant decline in food quality, especially in the case of fruits and vegetables. To overcome this drawback, researchers are extensively exploring alternative non-thermal High-Pressure Processing (HPP) technology to ensure microbial safety and retaining the sensory and nutritional quality of food. However, HPP is unable to inactivate the spores of some pathogenic bacteria; thus, HPP in conjunction with moderate- and low-temperature is employed for inactivating the spores of harmful microorganisms. Scope and approach: In this paper, the inactivation effect of high-pressure and high-pressure thermal processing (HPTP) on harmful microorganisms in different food systems, along with the bactericidal kinetics model followed by HPP in certain food samples, have been reviewed. In addition, the effects of different factors such as microorganism species and growth stage, process parameters and pressurization mode, and food composition on microbial inactivation under the combined high-pressure and moderate/low-temperature treatment were discussed. The establishment of a reliable bactericidal kinetic model and accurate prediction of microbial inactivation will be helpful for industrial design, development, and optimization of safe HPP and HPTP treatment conditions.

A new strategy for long-term complex oxidation of MAX phases: Database generation and oxidation kinetic model establishment with aid of machine learning

Owing to competitive behavior between oxidation products, complex oxidation commonly exists for MAX phases applied at high temperatures. Two major challenges remain to explain the oxidation law, i.e., acquirement of comprehensive oxidation data and establishment of reliable kinetic model. In this work, the long short-term memory recurrent neural network (LSTM-RNN) model is adopted combining the thermogravimetric (TG) experiment to generate the comprehensive oxidation database of MAX phases. By exploring the working principles of machine learning (ML) algorithms, a novel approach of combining real physical picture (RPP) model and sure independence screening and sparsifying operator (SISSO) method is proposed. The obtained machine learning-based real physical picture (ML-RPP) model can accurately deal with the long-term complex oxidation of various MAX phases. This work will provide a useful guideline for the cognition of complex oxidation of other ceramics and alloys.

A method for assigning pre-exponential factors for kerogen kinetics, calibrated with Easy%RoDL, and comparison with EASY%Ro

Modeling hydrocarbon-generation processes requires reliable kinetic models for the thermal decomposition of kerogens. To improve confidence and accuracy in modeling of generation, this study employs data from both natural and laboratory maturation to quantify thermal stress and strain for kerogens in 11 source-rock data sets. The method yields kinetic parameters (pre-exponential factor and Ea) for hydrocarbon generation that are constrained to make accurate predictions about thermal stress (quantified here as Ro-equivalent) and thermal strain (quantified as Hydrogen Index) under both laboratory and natural conditions. Methods for converting Tmax values to Ro-equivalents were examined and are discussed briefly. Vitrinite reflectance values were calculated at geological heating rates using the Easy%RoDL kinetic formulation, and were then compared with previous results obtained using EASY%Ro. The large differences observed between the EASY%Ro and Easy%RoDL evaluations are attributed to the differences in the pre-exponential factors in those two Ro-kinetic formulations. Understanding this relationship gives us a way to choose kinetic parameters for hydrocarbon generation that will work well for modeling under geological conditions. The single best A factor for hydrocarbon-generation when using EASY%Ro is 1e13 s−1 , while that for Easy%RoDL is 2e14 s−1 . The minor variation in A factors observed within each of the data sets may or may not be real. Using these results and concepts, more reliable hydrocarbon-generation windows in terms of either Ro-equivalent or Transformation Ratio can be achieved and cross-correlated. These results thus have the potential to increase both the accuracy of hydrocarbon-generation modeling, and the confidence in its results. Cited as: Waples, D. W., Yang, S. A method for assigning pre-exponential factors for kerogen kinetics, calibrated with Easy%RoDL, and comparison with EASY%Ro. Advances in Geo-Energy Research, 2023, 7(1): 1-6. https://doi.org/10.46690/ager.2023.01.01

Molecular reconstruction of vacuum gas oils using a general molecule library through entropy maximization

Vacuum gas oil (VGO) is the most important feedstock for hydrocracking processes in refineries, but its molecular composition cannot be fully acquired by current analysis techniques owing to its complexity. In order to build an accurate and reliable molecular-level kinetic model for reactor design and process optimization, the molecular composition of VGO has to be reconstructed based on limited measurements. In this study, a modified stochastic reconstruction-entropy maximization (SR-REM) algorithm was applied to reconstruct VGOs, with generation of a general molecule library once and for all via the SR method at the first step and adjustment of the molecular abundance of various VGOs via the REM method at the second step. The universality of the molecule library and the effectiveness of the modified SR-REM method were validated by fifteen VGOs (three from the literature) from different geographic regions of the world and with different properties. The simulated properties (density, elemental composition, paraffin-naphthene-aromatics distribution, boiling point distribution, detailed composition of naphthenes and aromatics in terms of ring number as well as composition of S-heterocycles) are in good agreement with the measured counterparts, showing average absolute relative errors of below 10% for each property.