Reliable Kinetic Models Research Articles

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.

Incremental Model Identification of Bio-processes from Data: Application to Microbial Production of Hyaluronic Acid

The development of reliable kinetic models of bioprocesses from data is a challenging task. In this work, a systematic approach to develop a kinetic model of bioprocess involving single biomass from concentration data is proposed without imposing any kinetic model a priori. The proposed incremental model identification approach decomposes the model-building task into a set of sub-tasks such as determining the yield coefficients and maintenance coefficient, specific growth rate structure identification, and parameter estimation. It is shown that the proposed approach allows identifying the mechanism of product formation. The proposed approach is applied to the microbial production of Hyaluronic acid (HA), an important biopolymer, using a recombinant Lactococcus lactis MKG6. An unstructured kinetic model is developed for the HA production from data. It is shown that HA production is a growth-associated process. Further, the specific growth rate of HA production is identified from a set of rate candidates. It is revealed that the specific growth rate in the HA production follows the non-competitive HA inhibition model. The parameters obtained by the incremental identification are further refined to obtain statistically optimal estimates using the simultaneous model identification. Validation of the identified kinetic model of HA production on new experimental data shows that the proposed approach leads to a reliable kinetic model with the optimal parameter estimates.

Assessment of higher-order singular value decomposition denoising methods on dynamic hyperpolarized [1-13C]pyruvate MRI data from patients with glioma

BackgroundReal-time metabolic conversion of intravenously-injected hyperpolarized [1-13C]pyruvate to [1-13C]lactate and [13C]bicarbonate in the brain can be measured using dynamic hyperpolarized carbon-13 (HP-13C) MRI. However, voxel-wise evaluation of metabolism in patients with glioma is challenged by the limited signal-to-noise ratio (SNR) of downstream 13C metabolites, especially within lesions. The purpose of this study was to evaluate the ability of higher-order singular value decomposition (HOSVD) denoising methods to enhance dynamic HP [1-13C]pyruvate MRI data acquired from patients with glioma. MethodsDynamic HP-13C MRI were acquired from 14 patients with glioma. The effects of two HOSVD denoising techniques, tensor rank truncation-image enhancement (TRI) and global–local HOSVD (GL-HOSVD), on the SNR and kinetic modeling were analyzed in [1-13C]lactate data with simulated noise that matched the levels of [13C]bicarbonate signals. Both methods were then evaluated in patient data based on their ability to improve [1-13C]pyruvate, [1-13C]lactate and [13C]bicarbonate SNR. The effects of denoising on voxel-wise kinetic modeling of kPL and kPB was also evaluated. The number of voxels with reliable kinetic modeling of pyruvate-to-lactate (kPL) and pyruvate-to-bicarbonate (kPB) conversion rates within regions of interest (ROIs) before and after denoising was then compared. ResultsBoth denoising methods improved metabolite SNR and regional signal coverage. In patient data, the average increase in peak dynamic metabolite SNR was 2-fold using TRI and 4–5 folds using GL-HOSVD denoising compared to acquired data. Denoising reduced kPL modeling errors from a native average of 23% to 16% (TRI) and 15% (GL-HOSVD); and kPB error from 42% to 34% (TRI) and 37% (GL-HOSVD) (values were averaged voxelwise over all datasets). In contrast-enhancing lesions, the average number of voxels demonstrating within-tolerance kPL modeling error relative to the total voxels increased from 48% in the original data to 84% (TRI) and 90% (GL-HOSVD), while the number of voxels showing within-tolerance kPB modeling error increased from 0% to 15% (TRI) and 8% (GL-HOSVD). ConclusionPost-processing denoising methods significantly improved the SNR of dynamic HP-13C imaging data, resulting in a greater number of voxels satisfying minimum SNR criteria and maximum kinetic modeling errors in tumor lesions. This enhancement can aid in the voxel-wise analysis of HP-13C data and thereby improve monitoring of metabolic changes in patients with glioma following treatment.

Plasma-catalyst modeling for materials selection: challenges and opportunities in nitrogen oxidation

Reliable kinetic models are essential to rationalize observations and to guide the selection of appropriate plasma-catalyst combinations. Kinetic models coupling plasma- and heterogeneous-catalytic-chemistries, however, are primitive. Here, we combine reduced plasma chemistries and density functional theory parameterized surface reactions to model NO formation from N2 oxidation, an alternative nitrogen fixation process. We first show plasma excited species can enhance turnover frequencies of both Pt and Au. The enhancing potential is a function of plasma species and their densities. We then compare NO production between plasma/Pt and plasma/Au with an integral reactor model and explore the dependence of NO production as a function of plasma conditions, reactor configurations and chemical compositions. Plasma/Pt generates more NO than plasma/Au under most thermal and plasma parametric conditions. Plasma/Au outperforms plasma/Pt under high plasma excitations at low temperatures. These results highlight that selection of plasma-catalyst combinations should consider the properties of plasmas, materials and the coupling of the two.

A comprehensive review on catalytic O-alkylation of phenol and hydroquinone

ABSTRACT The alkylation process involves two competitive paths of O- and C-alkylation and achieving better selectivity for desired products is a very challenging problem. The development of new process for synthesis of O-methylated products of phenol and dihydric phenols is a subject of high industrial and academic interest. Alkyl phenyl ethers, especially anisole and 4-methoxyphenol, have captivated significant interest due to their increasing applications in pharmaceutical industries. The main emphasis of the present review is to explore the recent development in two catalytic O-alkylation processes. The first process is O-methylation of phenol into anisole and another is selective mono O-methylation of hydroquinone into 4-methoxyphenol. The present article covers O-alkylation methods with methanol and dimethyl carbonate as alkylating agent over various acidic and basic catalytic systems. The catalyst systems analyzed involves Bronsted and Lewis acidic and basic ionic liquids, conventional acids, metal oxides, solid acid and basic catalysts, hydrotalcites, various zeolites and heteropolyacids. The mechanistic behavior of alkylation reactions in presence of different catalytic system is reviewed critically which is important to design new and/or modified catalyst in order to maximize the yield of desired product. Additionally, an influence of reaction parameters, role of catalyst and their active sites on product distribution is described. The review paper gives useful insight for researchers in the field of catalysis and reaction engineering of alkylation reactions. Understandings of the reaction pathways will help in developing reliable kinetic models necessary for process scale-up to industrial scale reactor system.

Insights into the release mechanisms of antioxidants from nanoemulsion droplets.

The therapeutic effects of antioxidant-loaded nanoemulsion can be often optimized by controlling the release rate in human body. Release kinetic models can be used to predict the release profile of antioxidant compounds and allow identification of key parameters that affect the release rate. It is known that one of the critical aspects in establishing a reliable release kinetic model is to understand the underlying release mechanisms. Presently, the underlying release mechanisms of antioxidants from nanoemulsion droplets are not yet fully understood. In this context, this review scrutinized the current formulation strategies to encapsulate antioxidant compounds and provide an outlook into the future of this research area by elucidating possible release mechanisms of antioxidant compounds from nanoemulsion system.

Optimal parameter estimation of PEM fuel cell using slime mould algorithm

For the modeling and analysis of hydrogen fuel cells, reliable kinetic models are of utmost important. Proton exchange membrane fuel cell (PEMFC) is a complicated, multivariable device. Mathematical simulation of the PEMFC typically results in nonlinear parameter estimation issues, often involving more than one local minima. This paper proposes to solve the PEMFC model estimation issues with a slime mould (SM)-based optimization algorithm. Findings on several benchmark test functions show that, the SMA outperforms the other rest of the compared algorithms. Furthermore, an experimental finding indicates that the predictive outcomes of the model are in better alignment with the real experimental results. The SMA therefore is a valuable and effective method for estimating the parameters of PEMFC models and can be used for other specific fuel cell issues. After estimating the parameter of fuel cell, nonparametric test is obtained and from this test it is justified that the SMA is better than the rest of the compared algorithms.

Development of a kinetic model for low temperature Fischer-Tropsch synthesis

A challenge to the simulation of the Fischer–Tropsch reaction system is the lack of reliable kinetic models which correctly account for the deviations from ideal polymerisation behaviour. The current trend in literature is to attribute these deviations to kinetic effects only. This ignores that the reactions in Fischer–Tropsch synthesis contain many aspects of an equilibrium-controlled process. This arises since the rate of chain growth is much faster than the rate of monomer formation from CO. Consequently, the production distribution and the observed trends in reaction behaviour can be described by thermodynamics. A model has been developed to describe the reaction kinetics wherein each rate expression for n-paraffin and 1-olefin formation is expressed as equilibrium-limited. This arises because of the linear variation of the ideal gas Gibbs free energy of formation with carbon number. It was therefore shown that the high methane yield, low ethene yield and the decrease in olefin-to-paraffin ratio with carbon number could effectively be captured in a light weight and tractable manner. The model preserves the polymerisation character of reaction and has been extended to include products of carbon number up to C81. The equilibrium basis of the model also successfully describes the dominant trends in the product distribution as a function of CO conversion, temperature and pressure.

Towards predictive combustion kinetic models: Progress in model analysis and informative experiments

One of the key tasks of combustion chemistry research is to develop accurate and robust combustion kinetic models for practical fuels. An accurate and robust kinetic model yields predictions that are highly consistent with experimental measurements over a wide range of operating conditions, with prediction uncertainties that are acceptable. Reliable experimental data generated by various powerful diagnostic techniques continue to play an essential role in the development of such models. This review focuses on the contributions of synchrotron-based species measurements in combustion systems, on model validation, model structure development, and model parameter optimization. Special emphasis is placed on recently reported strategies for informative and reliable experimental data generation, including combustion kinetic model input parameter evaluation, computational cost reduction for model analysis, model-analysis-based experimental design, experimental data treatment and error reduction. Particularly, the active-subspace-based method (ASSM) can reduce the dimensionality of combustion kinetic models and the aritificial-neural-network-based surrogates (ANN-HDMR and ANN-MCMC) can reduce the computational cost significantly. Global-sensitivity-based experimental design methods including sensitivity entropy and surrogate model similarity (SMS) can guide kinetics-information-enriched experimental data generation. Model-analysis-based calibration for experimental errors and feature extraction of experimental targets can improve the experimental data quality. A computational framework (OptEx) enabling the integration of experimental data with mechanism development, experimental design and model optimization, provides a new means to develop reliable kinetic models more efficiently and effectively.

Microbial Modeling Needs for the Nonthermal Processing of Foods

Year 2019 marked the 20th anniversary of the Nonthermal Processing Division (NPD) of the Institute of Food Technologists (IFT). The first 20 years served to understand the extents and limitations of nonthermal food processing technologies (NTP) that evolved from emerging trends to well-established commercial operations. It is now time for a new generation of contributors to rise up to the challenge of establishing safe harbors for NTP and deepening food safety regulations that meet industrial practices, where the development of simple, reliable kinetic models plays a major role. The review presents evidence against the still accepted assumption of linear microbial inactivation kinetics modeling in NTP, and questions the contributions of traditional kinetic parameters derived from this belief like the decimal reduction time (D) and z-values, in the development of NTP guidelines. Moreover, research findings continue to support the Weibull model and derived mathematical expressions as simple, reliable equations to predict microbial inactivation in thermal and NTP, allowing the reinterpretation of some kinetic parameters (D, z) within the Weibullian context. The review also aims to serve as a starting point for readers interested in mathematical modeling by summarizing related guidelines and statistical criteria, which goes beyond simply fitting equations to experimental data. Mathematical modeling is an integral approach involving careful experimental planning, knowledge of model properties and assumptions, data visualization, evaluation of model performance, assessment of model parameter estimate uncertainty and inherent data variability, model selection, and most importantly, critical judgment and common sense to provide the model a practical context.

An experimental and kinetic modeling study on nitric oxide formation in premixed C3 alcohols flames

This study provides new quantitative NO concentrations measurements in n-propanol + air and i-propanol + air flames together with a new combustion kinetic model. The heat flux method was employed to stabilize propyl alcohols flames and the initial gas conditions were set to 323 K, 1 atm, and Φ=0.7–1.4. Saturated laser-induced fluorescence was employed to measure NO concentration in the post-combustion region. The presented and literature models, namely the POLIMI and Bohon et al. (2018) kinetic mechanisms, were assessed against new experimental data. Experimental results showed a higher NO formation in the thermal zone for n-propanol flames, whereas i-propanol flames indicate a higher amount of NO formed at fuel-rich conditions. Overall among the tested models, the present mechanism exhibited the best agreement in emulating NO experimental profiles; conversely, numerical simulations from the POLIMI model showed significant inconsistencies at fuel-rich conditions and the Bohon et al. (2018) model was unable to reproduce the measured data, notably underpredicting experimental values at all investigated conditions. However, the present model manifested some uncertainties in reproducing NO formation in the prompt region; therefore, in connection with this important aspect, the new experimental data obtained in this work will provide a valid support to further develop more reliable kinetic models.

Aromatics oxidation in the furnace of sulfur recovery units: Model development and optimization

The frequent changes in the composition of acid gas feed to the Claus process cause flame instability and lead to the incomplete destruction of aromatic contaminants in feed that are benzene, toluene, ethylbenzene, and xylenes (BTEX). These aromatics cause frequent catalyst deactivation. The current literature lacks a reliable global kinetic model that can ease the computational burden of optimizing the process parameters to enhance flame stability and support BTEX destruction. In this paper, a kinetic model for BTEX oxidation is developed for the first time using a detailed reaction mechanism and is validated using plant data. The model is used to evaluate the effects of inlet air preheating, methane co-firing, and oxygen enrichment on BTEX destruction and furnace temperature. The simulation results ascertain that a favorable increase in the furnace temperature can be achieved with appropriate feed parameters such as feed temperature and O2 concentration to decrease BTEX emission from the furnace below 6 ppm. The model is used to conduct an optimization study that successfully demonstrates its capability to predict optimized inlet parameters for effective BTEX destruction, a high sulfur recovery efficiency, a low emission of CO and SO2, and a reduced fuel gas consumption in the Claus process plants.

Experimental and modeling assessment of sulfur release from coal under low and high heating rates

Coal combustion releases elevated amounts of pollutants to the atmosphere including SOX. During the pyrolysis step, sulfur present in the coal is released to the gas phase as many different chemical species such as H2S, COS, SO2, CS2, thiols and larger tars, also called SOX precursors, as they form SOX during combustion. Understanding the sulfur release process is crucial to the development of reliable kinetic models, which support the design of improved reactors for cleaner coal conversion processes. Sulfur release from two bituminous coals, Colombian hard coal (K1) and American high sulfur coal (U2), were studied in the present work. Low heating rate (LHR) experiments were performed in a thermogravimetric analyzer coupled with mass spectrometry (TG-MS), allowing to track the mass loss and the evolution of many volatile species (CO, CO2, CH4, SO2, H2S, COS, HCl and H2O). High heating rate (HHR) experiments were performed in an entrained flow reactor (drop-tube reactor – DTR), coupled with MS and nondispersive infrared sensor (NDIR). HHR experiments were complemented with CFD simulation of the multidimentional reacting flow field. A kinetic model of coal pyrolysis is employed to reproduce the experiments allowing a comprehensive assessment of the process. The suitability of this model is confirmed for LHR. The combination of HHR experiments with CFD simulations and kinetic modeling revealed the complexity of sulfur chemistry in coal combustion and allowed to better understand of the individual phenomena resulting in the formation of the different SOX precursors. LHR and HHR operating conditions lead to different distribution of sulfur species released, highly-dependent on the gas-phase temperature and residence time. Higher retention of total sulfur in char is observed at LHR (63%) when compared to HHR (37–44%), at 1273 K. These data support the development of reliable models with improved predictability.

Detailed combustion chemical mechanism for surrogates of representative jet fuels

Surrogate fuels are useful for computational fluid dynamics (CFD) simulations. However, there are two main challenges in surrogate fuel: defining an appropriate surrogate and designing compact and reliable kinetic models. Many works of defining an appropriate surrogate have been done in our previous work. In this study, we focus on obtaining compact and reliable kinetic models for jet surrogate fuels. A detailed mechanism was developed by combining n-dodecane, 2,5-dimethylhexane and toluene sub-mechanisms with AramcoMech 2.0 C0–C4 mechanism to mimic the combustion properties of three representative jet fuels. Extensive validation of the component mechanisms in surrogate is performed using abundant experimental data sets. Thereafter, various combustion properties of three jet fuels like ignition delay times, species concentrations in flow reactor and laminar flame speeds are simulated by using this surrogate kinetic mechanism and previous surrogate formulations. The simulations are compared with other well-known surrogate models and a wide range of experimental data. The comparisons show that the developed mechanism is substantially improved prediction accuracy of most considered combustion properties for different jet fuels in a wide range of conditions.

Experimental and numerical simulation to identify the thermal hazards and hazardous scenarios of N-Nitrodihydroxyethyl dinitrate

As a major high-energy plasticizer in the production of propellant, the destructive nature and thermal hazards of DINA are enormous. To evaluate the potential thermal hazards for its decomposition, the non-isothermal DSC and ARC experiments were performed to determine the decomposition kinetic model and parameters. It can be seen from the DSC results that DINA underwent a melting process before thermal decomposition, the endothermic peak and exothermic peak are identified at about 49−52℃ and 153−162℃, respectively. Combined with the results of DSC and ARC tests, the reliable kinetic model of DINA consists of four consecutive autocatalytic reaction has been created and verified. Subsequently, kinetic-based numerical simulations were executed to evaluate the thermal explosion hazards of DINA under the different scenarios. Process safety parameters under adiabatic conditions including time to maximum rate as well as induction period were consequently retrieved. Furthermore, inherent safety recommendations were proposed to forestall the process accidents in storage and the applications of DINA.

The influence of pressure and equivalence ratio on the NTC behavior of methane

Methane based polygeneration processes in piston engines offer the possibility of a controllable and flexible conversion of energy, to up-convert low value chemicals and to store energy. These processes preferably take place under fuel-rich conditions and at high pressures. Under fuel-rich conditions, there was one experimental report that a distinctive negative temperature coefficient (NTC) behavior occurs in methane oxidation (Petersen et al., 1999). To design a polygeneration process, reliable kinetic models are required to capture the impact of pressure and equivalence ratio variations on reactivity of the gas mixtures. Here, the experimental basis for methane oxidation is expanded to high pressures and very fuel-rich conditions and compared to literature models, both with special emphasis on the NTC behavior. The oxidation of methane/oxygen mixtures at 2 ≤ Φ≤ 20 and pressures ranging from 1 to 20 bar is investigated. The literature reaction mechanisms are assessed with respect to their ability to predict this phenomenon and used to identify reaction pathways. It is found that NTC behavior occurs in a temperature range between 700 and 1000 K and at pressures higher than 5 bar. The lower temperature limit is slightly shifted towards higher temperatures with decreasing equivalence ratio. In addition, the higher the equivalence ratio, the broader the pressure range, in which the NTC behavior is observed. In general, predictions of some models are in good agreement with the experimental data. Reaction path analyses reveal that the competition between oxidation and recombination pathways are responsible for the NTC region in methane oxidation.

Towards quantitative small-animal imaging on hybrid PET/CT and PET/MRI systems

Over the past two decades, innovations in small-animal positron emission tomography (PET) have reached an impressive level, which has brought countless opportunities to explore the major puzzles in biomedical research. It is a given that pairing information coming from different imaging modalities renders unprecedented knowledge and provides a great insight into various facets of biological systems, such as anatomy, function, physiology, and metabolism in animal models of human diseases, which are difficult to be beaten by standalone PET scanners. The development of bimodal and tri-modal imaging platforms with advanced software solutions dedicated for quantitative studies in small-animals has spurred academic and industrial interest. However, it is undisputed that the potential success of these scanners in filling the translational gap between human and animal findings, hinges to a great extent upon optimization and standardization of relevant parameters and acquisition protocols, which is often overlooked. This article reviews the trends till 2020 in the field of preclinical PET imaging with emphasize on image reconstruction and quantitative corrections implemented on state-of-the-heart hybrid systems. First, the challenges, limitations, and benefits offered by multi-modality imaging systems are described and then, the most commonly used strategies, as well as novel techniques for image reconstruction and image corrections (attenuation, scattering, normalization, motion, and partial volume effect) are presented. The advantages and disadvantages of different methods are also discussed. We also briefly touch upon the factors that should be considered for reliable kinetic modeling and absolute quantitation in preclinical small animal research. Multi-modality imaging has attracted a lot of research, particularly in the preclinical portfolio. Nevertheless, more research is still needed to optimize the conceptual design, reach the limits of quantitative imaging and implement standardized protocols for small-animal studies. Without any doubt, exploring the potential advantages of combined imaging units providing optimal image quality and reliable tools for quantification of biological parameters through standardized imaging protocols is one of the goals of translational research.

1793-P: C-Peptide Kinetics: The Van Cauter Population Model May Not Be Applicable to Post–Gastric Bypass Adults with Postprandial Hyperinsulinemic Hypoglycemia

Insulin secretion is key to understand postprandial hyperinsulinemic hypoglycemia (PHH), an increasingly recognized and potentially debilitating complication of gastric bypass (GB) surgery. To assess it noninvasively, the Oral C-peptide Minimal Model (OCMM) can be used. This requires to know C-peptide (CP) metabolic clearance rate (MCR) usually fixed to the values predicted by the Van Cauter (VC) population model, but this has never been assessed in PHH individuals. Here, the aim is to fill this gap. Two groups of post-GB adults with PHH underwent either a 75g oral glucose (11F/5M; age=43±8 y; BMI=28.2±7.1kg/m2) or a 60g mixed-meal (9F/3M; age=44±10y; BMI=27.4±3.9kg/m2) tolerance test with sampling for plasma glucose and CP over 3.5 and 3h, respectively. The OCMM was used to describe CP concentration with CP kinetics fixed to VC or estimated based on data (DB). Model performance was assessed by its ability to predict CP data. In both groups, the VC approach underestimated CP-peak and overestimated CP-tail, while the DB one well predicted CP data in all subjects (Figure 1A). As a consequence, MCR resulted significantly higher with DB than VC approach (Figure 1B). The present findings question the validity of the VC population model to predict CP kinetics in PHH patients and call for CP bolus experiments to develop a reliable kinetic model in this population. Disclosure M. Schiavon: None. D. Herzig: None. M. Hepprich: None. M.Y. Donath: Advisory Panel; Self; AstraZeneca, Bayer Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Lilly Diabetes, Merck Sharp & Dohme Corp., Novo Nordisk Inc., Roche Pharma, Sanofi-Aventis. C. Stettler: None. L. Bally: None. C. Dalla Man: Research Support; Self; Sanofi-Aventis Deutschland GmbH. Funding Italian Ministry of Education (232/2016); Swiss National Science Foundation (PCEGP3_186978); University of Padova

Application of kinetics and computational fluid dynamics in pinene isomerization

A reliable kinetic model to describe the effects of various factors on the reaction rate and selectivity of pinene isomerization is developed. Furthermore, computational fluid dynamics (CFD) is applied to simulate the solid–liquid dispersion in reactor. The catalyst TiM is obtained by improving the composition and structure of hydrated titanium dioxide. The kinetic equation of pinene isomerization is deduced based on reaction mechanism and catalyst deactivation model. The kinetic equation of pinene isomerization reaction is fitted, and the results show that the fitted equation is correlated with the experimental data. The rate and selectivity of pinene isomerization reaction are affected by the amount of catalyst, deactivation of catalyst, structure of catalyst, reaction temperature and water content of catalyst. The solid–liquid distribution of the reactor is calculated by computational fluid dynamics numerical simulation, and the solid–liquid dispersion in commercial scale reactor is more uniform than that in lab-scale reactor. • Establish a more complete kinetic model by introducing deactivation model • The kinetic model correlates well with experimental data. • The lab-scale reactor and commercial scale reactor are investigated by CFD numerical simulation. • The factors affecting the rate and selectivity of the pinene isomerization reaction are systematically studied.

High-pressure air injection laboratory-scale numerical models of oxidation experiments for Kirsanovskoye oil field

High-Pressure Air Injection (HPAI) as an enhanced oil recovery (EOR) method has a high potential and has already been effectively applied for carbonate reservoirs development. The target object of the current research is a Kirsanovskoe oil field confined to the North Kinelsky oil and gas region. Kirsanovskoye field is a carbonate reservoir with an average depth in the range of 1300–1350 m, average porosity of 11.5%, and average permeability of 70.6 mD.The HPAI process, which includes intensive oxidation and combustion reactions, phase transitions of components and change in the composition and properties of coexisting phases, is not fully explored yet. Its complexity and lack of reliable chemical and kinetic models both for light and heavy oils cause several limiting factors for the construction of a field numerical model with the high predicting level in the respect to the HPAI as the EOR method.The presented work is devoted to the construction and validation of laboratory-scale numerical models of oxidation experiments to provide proper HPAI kinetic model for oxidation and combustion reactions for the Kirsanovskoye oil field. For these purposes, high-pressure ramped temperature oxidation (HPRTO) and subsequent Medium pressure combustion tube (MPCT) experiments were conducted and analyzed. HPRTO test is used for the determination of the reaction kinetics together with such characteristics as a function of temperature, O2 uptake, CO2 generation, oxidation front velocity, peak temperatures, residual coke, and amount of burned and recovered oil. MPCT provides information on stoichiometry for the high-temperature process and optimal airflow rate. A significant part of the research is dedicated to the construction of 3D numerical models (multilayer design, proper heater regime, etc.) of HPRTO and MPCT experiments to avoid constructional uncertainties and their further validation against the experimental data. To describe the chemical behavior of hydrocarbons during the HPAI process we use the reaction scheme which includes polymerization of Malthenes and Asphaltenes in low-temperature oxidation region, thermal cracking and coke combustion in the high-temperature oxidation region.