Reactor Temperature Profile Research Articles

Experimental‐Assisted Approach to Develop a Numerical Model for Simulating the Reaction Propagation in Reactive Multilayers

One outstanding feature of self‐propagating reactions is their ability to release heat of reaction over both temporal and spatial scales, enabling the sustained progression of the reaction after a local ignition. They propagate in the form of a continuous reaction front through the mixture of the starting materials. Previous research on reactive materials has predominantly focused on unraveling the microstructure property relationships influencing released energy in reacting multilayers. This involved considering coupled differential equations, including the heat conduction equation and Fick's law. In this study, the introduction of a purely thermal numerical macroscale model is made, incorporating two states of material properties that differentiate between the thermal characteristics before and after phase formation. The homogenization of material properties before the phase formation is accomplished through the consideration of directional‐temperature‐dependent thermal conductivity and temperature‐dependent‐specific heat capacity. The energy‐release function is derived using experimental data for the reaction velocity depending on bilayer thickness. This model allows for the exploration of reaction motion and temperature profiles, achieving qualitative conformity with experimental measurements for freestanding foil, and necessitating reasonable computational effort.

Comparative dynamic optimization study of batch hydrothermal liquefaction of two microalgal strains for economic bio-oil production

This work presents dynamic optimization strategies of batch hydrothermal liquefaction of two microalgal species, Aurantiochytrium sp. KRS101 and Nannochloropsis sp. to optimize the reactor temperature profiles. Three dynamic optimization problems are solved to maximize the endpoint biocrude yield, minimize the final time, and minimize the reactor thermal energy. The biocrude maximization and time minimization problems demonstrated 11% and 6.18% increment in the optimal biocrude yields and reduction of 78.2% and 61.66% in batch times compared to the base cases for the microalgae with higher lipid and protein fractions, respectively. The energy minimization problem revealed a significant reduction in the reactor thermal energies to generate the targeted biocrude yields compared to the biocrude maximization. Therefore, the identified optimal temperature trajectories outperformed the conventional fixed temperature profiles and could improve the overall economics of the batch bio-oil production from the algal-based biorefineries by significantly enhancing the reactor performance.

Optimization of an Industrial Methanol Reactor Using Aspen Plus Simulator and Design Expert

The chemical conversion of carbon dioxide into methanol has the potential to address two major sustainability issues: the economically viable replacement of fossil energy resources and the avoidance of greenhouse gas emissions. However, the chemical stability of carbon dioxide poses a difficult barrier to its conversion, necessitating extreme reaction conditions, resulting in increased energy input and, subsequently, elevated equipment, operation, and environmental costs. This, in turn, could potentially undermine its promising sustainability as a raw material for the chemical and energy industries. This research aims to optimise methanol production from a methanol reactor by using design of experiment (DOE) in Design Expert and Aspen Plus as a reactor simulator. The optimisation process involved eight parameters: five inlet molar flowrates (CO, CO2, H2O, H2, CH3OH) and three reactor conditions (inlet temperature, pressure, and temperature profile). The methanol production in Design Expert was optimised using the Box-Behnken method and a quadratic model. This study used the maximum range for each parameter as the actual industrial data and the minimum range to be 50% below it. The validated Aspen-Plus model was used for methanol production simulation. Response surface methodology (RSM) was used to determine the optimal parameters. This simulation required 120 samples. The optimal parameter values from the RSM were 142.2 kmol/hr of inlet CH3OH, 2250.91 kmol/hr of CO, 1398.3 kmol/hr of CO2, 15.6973 kmol/hr of H20, 19053.7 kmol/hr of H2, 497.883K of reactor inlet temperature, 81.9999 bar of reactor pressure, and 30K for reactor temperature profile with the actual methanol production of 2814.23 kmol/hr. The optimal values were simulated to test the accuracy of the predictive model, and the error was less than 5.2 percent. Overall, the inlet H2 molar flowrate was reduced in optimised conditions, reducing raw material usage and increasing output.

Non-Equilibrium behavior of dissociated hydrogen in historic Nuclear thermal propulsion reactors

Hydrogen gas dissociation in Nuclear Thermal Propulsion (NTP) analyses and models is often assumed universally at chemical equilibrium or neglected entirely. This study examines the non-equilibrium chemical kinetics of high-speed dissociating hydrogen within a range of reactor temperature profiles using historic NTP engine inlet and outlet conditions. Below 1600 K, all reactors should be modeled at equilibrium unless somehow disrupted off the equilibrium concentration where it must be modeled in non-equilibrium. Above 1600 K, NTP systems should assume non-equilibrium behavior to accurately represent high-speed flow. However, analysis of historic NTP reactors all achieved equilibrium, modeled at the highest possible flow speeds, at the reactor outlet. Historic atomic hydrogen molar fractions at the outlet were less than 0.5%. These findings and analyses provide useful and validated assumptions for all NTP reactors for dissociation aiding future CFD analysis.

High temperature flow synthesis of iron oxide nanoparticles: Size tuning via reactor engineering

Batch thermal decomposition syntheses of iron oxide nanoparticles (IONPs) provide precise control of particle properties, but their scalability and reproducibility is challenging. This is addressed in this work via a versatile high temperature flow reactor with adjustable temperature profiles through three individual stages operated between 180 °C and 280 °C. The tuneable temperature profiles in combination with self-seeded growth methods made it possible to synthesise IONPs between 2 and 17 nm (a size increase that corresponds to a >600 fold particle volume increase) at production rates of several gIONP per day. The precursor solutions contained only iron(III) acetylacetonate in a polyol solvent and no nucleation or growth inhibitors, oxidation or reducing agents, ligands or any other additives . This broad size range covers most biomedical applications and is of special interest for T1 MRI contrast agents (2–5 nm), as well as for magnetic hyperthermia cancer therapy (>10 nm). The potential of the IONPs produced was demonstrated by their high longitudinal relaxivity >16 mM−1s−1 at a transversal/longitudinal relaxivity ratio <2.5 (small IONPs) and specific absorption rates increasing with the IONP size up to180 W/gFe. In addition, the polyol method employed allowed for simple ligand exchange with biocompatible sodium tripolyphosphate to make the IONPs stable in water, thus rendering them suitable for biomedical applications. The continuous high temperature process presented shows how to control the particle size not via the chemistry (e.g., chemical additives affecting the particle size through the surface chemistry), but engineering parameters, i.e., reactor temperature profiles, reagent addition sequences and seeded growth strategies.

An unconditionally stable third order scheme for mixed convection flow between parallel plates with oscillatory boundary conditions

AbstractThis work proposes an unconditionally stable third‐order multistep technique for time‐dependent partial differential equations. Its unconditional stability is proved by employing von Neumann stability analysis, and constructed Matlab code is another solid proof of the existence of the such scheme. The scheme is constructed on three consecutive time levels, and a compact fourth‐order scheme is considered for spatial discretization. The convergence conditions are found when applied to the system of parabolic equations. The scheme is tested on two examples of flow between parallel plates. The mathematical model of heat and mass transfer of flow between parallel plates under the effects of viscous dissipation, thermal radiations, and chemical reaction is given and solved by the proposed scheme. The impact of some parameters, including radiation and reaction rate parameters, on velocity, temperature, and concentration profiles is also illustrated by graphs. The proposed scheme is also compared with the existing scheme, providing faster convergence than an existing one. The fundamental benefit of the proposed scheme is that it can give a compact fourth‐order solution to parabolic equations.

Artificial neural network and semi-empirical modeling of industrial-scale Gasoil hydrodesulfurization reactor temperature profile

The sulphur content of the desulfurization reactor product is strongly related to the severity of the reaction, which is determined by the reactor bed temperatures. In this study, the potential of neural network and semiempirical regression models to estimate temperature variation in industrial-scale gasoil HDT reactor was examined to achieve improved prediction ability. The reactor consists of 4 beds with 10 temperature indicators to control and monitor the temperature. Model developing Inlet temperature, H2/HC ratio, LCO flowrate, cracked gasoline flowrate, and straight run gasoil flowrate as input layer parameters and bed section outlet temperature as output layer was used. A database with 283 different data was built using daily records of the HDS process from an Iranian refinery. Feedforward backpropagation algorithm with learngdm learning function was used to develop ANN models. The number of neurons in the hidden layer varied between 7–13 to find the most reliable network. The optimum ANN models were selected for reactor temperature profiles on the trial-and-error method. The performance of designed models was tested by computing root mean square error (RMSE), and average absolute deviation (AAD). Comparing the neural network model and regression models, the artificial neural network model is the best model for the reactor bed temperature prediction. The value of AAD for temperatures sensors 1 to 10 in the artificial neural network model obtained 0.128, 0.094, 0.091, 0.1, 0.032, 0.067, 0.052, 0.031, 0.086, and 0.062 respectively, which show good agreement between the actual data and the artificial neural network predicted data for temperature profile in the length of the reactor.

Temperature Profile Estimation in the Core of a Sodium-Cooled Fast Reactor using CFD Modeling

The latest proposal for nuclear systems for application in space are 4th generation reactors that use liquid metals as coolant. The operating conditions, as well as its safety in these matters, is linked to its thermal and fluid dynamic behavior. This paper presents the estimation of the temperature profile of a sodium-cooled fast reactor from the conduction and convection heat transfer mechanisms. The temperature distribution is analyzed in the area of the fuel and the temperature profile of the coolant in the area of the high conductivity pipes. Is established the analysis of nuclear fuel in a one-dimensional, stationary and with power generation. In the GAP area, the analysis is carried out by natural convection, and finally, the cladding in contact with sodium by forced conduction-convection. As results, it is presented the simulation in Computational Fluid Dynamics to determine the temperature profile due to the behavior of the coolant in the pipe when subjected to a constant flow of heat supplied by the nuclear fuel pellets. The temperature profile will allow us to determine the parameters to establish the operating conditions of secondary power conversion systems.

Hydrodynamics-reaction-coupled simulations in a low-scale batch FCC regenerator: Comparison between an annular and a free-bubbling fluidized beds

Although annular fluidized beds (AFB) has been widely studied, a direct comparative evaluation of its reactive performance with bubbling fluidized beds (BFB) under similar operating conditions is still lacking. In this study, a comparative simulation using Euler–Lagrange MP-PIC method is performed under varying superficial gas velocities to assess their chemical reaction performance in relation to coke combustion rate, flue gas and temperature profiles in both reactors. The macroscale hydrodynamics of transport phenomenon, pressure fluctuation, solids holdup, slip velocity, and radial nonuniformity index (RNI) are also evaluated in detail. The results show, compared to BFB, the AFB shows big hydrodynamic differences, as indicated by the stronger cross-flow of solids, higher pressure fluctuation magnitudes, nonuniform lateral solids distribution, higher slip velocity and RNIs. However, reaction performance in relation to coke burning pattern and rates, flue gas distribution and temperature profile in the two beds are close. Under the constant bed inventory and air flowrate criteria, bigger changes in hydrodynamics are needed to affect the reaction performance significantly in the two small reactors due to the batch operation mode and the slow coke burning rate.

Wiener-Neural-Network-Based Modeling and Validation of Generalized Predictive Control on a Laboratory-Scale Batch Reactor.

Batch reactors are large vessels in which chemical reactions take place. They are mostly found to be used in process control industries for processes such as reactant mixing, waste treatment of leather byproducts, and liquid extraction. Modeling and controlling of these systems are complex due to their highly nonlinear nature. The Wiener neural network (WNN) is employed in this work to predict and track the temperature profile of a batch reactor successfully. WNN is different from artificial neural networks in various aspects, mainly its structure. The brief methodology that was deployed to complete this work consisted of two parts. The first part is modeling the WNN-based batch reactor using the provided input–output data set. The input is feed given to the reactor, and the reactor temperature needs to be maintained in line with the optimal profile. The objective in this part is to train the neural network to efficiently track the nonlinear temperature profile that is provided from the data set. The second part is designing a generalized predictive controller (GPC) using the data obtained from modeling the reactor to successfully track any arbitrary temperature profile. Therefore, this work presents the experimental modeling of a batch reactor and validation of a WNN-based GPC for temperature profile tracking.

An improved kinetic modelling of woody biomass gasification in a downdraft reactor based on the pyrolysis gas evolution

Biomass gasification technology is evolving and more research through modelling alongside the experimental work needs to be performed. In the past, all the attention has been concentrated on the combustion and reduction stages to be the controlling reactions while the pyrolysis is modelled as an instantaneous process. In this study, a new enhanced model for the gasification process in the downdraft reactor is proposed with a more realistic representation of the pyrolysis stage as a temperature-dependent sequential release of gases. The evolution of the pyrolysis gas, followed by the combustion and reduction reactions, are kinetically controlled in the proposed model which is developed within the Aspen Plus software package. The simulation of the reactor temperature profile and the evolution of the pyrolysis gas is carried out in an integrated MATLAB and Aspen Plus model. The proposed model has been validated against experimental data obtained from the gasification of different woody biomass types and considering a range of scale reactor and power loads. The predicted results are in very good agreement with the experimental data, and therefore the model can be used with confidence to perform a sensitivity analysis to predict the performance of a gasifier at different load levels corresponding to the air flow rate range of 3–10 L/s. As the supplied air flow rate increases, the LHV decreases but the gas yield behaves conversely, and in turn the cold gas efficiency is maintained at a good level of energy conversion at ≥ 70%. Furthermore, the variation in the biomass moisture content, which is commonly in the range of 5–25 % has a significant effect on the gasification efficiency. Such that biomass that has a high moisture content substantially reduces the CO content and consequently the LHV of the produced gas. Hence, it is important to maintain the moisture content at the lowest level.

Scale-up studies on electrically driven steam methane reforming

This work is devoted to scale-up parametric studies of a new reactor for the steam reforming of methane driven by electrical current. The main distinguishing feature of this type of reactor is the use of direct electrical current flowing through metal particles to heat the whole reactor volume using Joule heating. The heat and mass transfer model uses six gaseous chemical species in the gas phase and inside the catalyst. Reaction rate expressions are taken from the literature (Langmuir–Hinshelwood–Hougen–Watson (LHHW) kinetics). In this work we consider a cylindrical electrically insulated tube with a diameter of 0.5 m and a height of 0.88 m, which is filled with metal particles and catalyst particles with diameters of 10 mm and 4 mm, respectively. Parametric runs have been carried out for different flow rates from 0.04 kg/s to 0.1 kg/s and electrical power from 185 kW to 462 kW. The results of simulations revealed that the cross-sectional temperature profile of a scaled-up reactor is mostly uniform, with small hot spots but a temperature difference that is usually within 100K. It was shown that the gas inflow located on the top gives smoother gradients of the particle temperature and gaseous species in comparison to the case when the gas is supplied from the bottom of the fixed bed.

Modeling and simulation of a multi-bed industrial reactor for renewable diesel hydroprocessing

The complexity of designing and optimizing vegetable oil hydrotreating reactors lies in describing multiple phenomena, including heat and mass transfer, hydrogen consumption, pressure drop, and a complicated network of highly exothermic reactions. This study intends to analyze the behavior of a vegetable oil hydroprocessing unit in a commercial environment via modeling and simulation techniques. In order to describe the three-phase (gas-liquid-solid) system in a detailed manner, a commercial-scale reactor model having multiple catalyst beds and inter-bed quench gas injections was constructed accounting for the heat and mass transfer between phases, the dynamic response of the system, the variation in gas phase velocity, and intraparticle effects. Based on dynamic reactor simulations, quench gas injection strategies were proposed to control the reactor temperature profile and yield of the desired products. Simulation results showed that the selection of a feed inlet temperature plays a major role in reactor overheating and quench injections must start as soon as the reactant stream reaches the inter-bed quench zone to stabilize reactor temperature more rapidly during start-up. In addition, the lengths of the catalyst beds need to be adjusted such that the heat released by chemical reactions is properly distributed along the reactor. The results overall provide useful information for the design and optimization of commercial-scale catalyst bed configurations for hydrotreating renewable feedstock. In particular, it is highlighted that by means of an appropriate gas quenching configuration, reactor temperature can be adequately controlled, allowing higher yields of green diesel.

Ammonia converter Simulation and Optimization Based on an Innovative Correlation for (KP) Prediction

In this paper, the simulation and optimization of an industrial ammonia synthesis reactor is illustrated. The converter under study is of a vertical design, equipped with three radial-flow catalyst beds with inter-stage cooling and two quenching points. For building the model, a modified kinetic equation of ammonia synthesis reaction, based on Temkin- Pyzhev equation and an innovative correlation for (KP) prediction, was developed in suitable form for the implementation in Aspen HYSYS plug flow reactor using the spreadsheet embedded in the software with the introduction of some invented simulation techniques. A new parameter, which is a function of (T, P and α), was introduced into the reaction rate equation to account for the variation of KP with pressure. The simulation model is able to describe the converter behavior with acceptable accuracy. A case study was done, using Aspen HYSYS Optimizer, illustrated the optimum reactor temperature profile, after 12 years of operation, to achieve maximum production. The result predicts an increase of 8 tons ammonia per day accompanied with an increase of steam production of 12 tons per day.

Solar thermal energy application to dry reforming of methane on the open-cell foam to enhance the energy storage efficiency of a thermochemical fluidized bed membrane reformer: modelling and simulation

The hydrodynamic characterization of the solar-driven CO2 reforming of methane through b-SiC open-cell foam in a fluidized bed configuration is performed by reacting Methane (CH4) with carbon dioxide (CO2). The mathematical modelling is important to design and optimize the reforming methods. Usually, the reforming methods's application through b-SiC foam bed improves the heat transfer and mass transfer due to high porosity and surface area of the b-SiC foam. Fluidized Bed Membrane (FBM) Reformers can be substantially studied as a promising equipment to investigate the thermochemical conversion of CH4 using CO2 to produce solar hydrogen. This work has as main objective a theoretical modelling to describe the process variables of the solar-driven CO2 reforming of methane in the FBM reformer. The FBM reformer is filled with b-SiC open-cell foam where the thermochemical conversion is carried out. The model variables describe the specific aims of work and these objectives can be identified from each equation of the developed mathematical model. The present work has been proposed to study two specific aims as (i) The effective thermal conductivity's effect of the solid phase and (ii) molar flows of chemical components. The endothermic reaction temperature's profiles are notably increased as the numeral value of the effective thermal conductivity's effect of the solid phase. is rised. The solar-driven CO2 reforming method is suggested to improve the Production Rate (PR) of H2 regarding the PR of CO.

Improved bio-oil upgrading due to optimized reactor temperature profile

Production of second-generation biofuels by upgrading of pyrolysis bio-oil from biomass attracted much attention in recent years. Here, we present a study dedicated to the stabilization of pyrolysis bio-oil over NiMo/Al2O3 catalyst in single-stage fixed bed reactor with different temperature profiles (ramp to the 340 °C). Detailed characterisation of the textural properties of the fresh and the spent catalyst and the determination of the changes in physicochemical properties and chemical composition of the liquid and gaseous products have allowed us to discuss the influence of the temperature profile on the catalyst stability during 80 h time on stream of the bio-oil hydrotreatment. We have observed that the lower average temperature in the reactor can yield stabilized bio-oil with physicochemical properties comparable with bio-oil that was hydrogenated at higher temperatures. Moreover, the catalyst long-term stability was higher when using a slower temperature ramp. Thus, the physicochemical properties of the stabilized bio-oil obtained with the lower average temperature in the reactor was maintained uniform with the increasing time on stream, while deterioration of the properties of the hydrotreated liquid products produced under the faster temperature ramp occurred. Additionally, we have shown that no sulfur loss occurred from the NiMo/Al2O3 catalyst during bio-oil hydrotreatment.

Sulfite removal from flue-gas desulfurization residues of coal-fired power plants: Oxidation experiments and kinetic parameters estimation

Semi-dry flue-gas desulfurization (FGD) processes abate 99% of atmospheric emissions of sulfur dioxide from coal-fired power plants at the expense of producing daily tones of solid FGD residues containing sulfites, sulfates, carbonates and hydroxides of calcium and magnesium, besides fly-ashes. In this work, a fluidized-bed reactor pilot plant was used for experiments of dry-oxidation of FGD residues aiming at converting sulfites into sulfates in order to upgrade such residues for utilization as raw material to the cement industry. A two-dimensional design of experiments on the plane of feed air temperature and reactor time-on-stream was conducted in the pilot plant generating sulfite conversion data and transient reactor temperature profiles. These data were used for estimating the first-order kinetic parameters of sulfite conversion via non-linear regression following the Maximum Likelihood Principle. The optimized Arrhenius factor and Arrhenius activation energy obtained via the Nelder–Mead Flexible Simplex method were, respectively, 0.001 mol/kg.s.bar and 14146.5 J/mol. This kinetic model allows designing large-scale plants for treatment of semi-dry FGD residues in order to beneficiate it for utilization in the cement industry, avoiding the disposal and environmental costs of landfilling such residues.

NUMERICAL MODELLING OF THE ENDOTHERMIC PROCESS OF THE STEAM REFORMING OF METHANE IN A FIXED BED REFORMER

Thermochemical Packed-Bed (TPB) reformer has been substantially studiedin the past years as a promising equipment to investigate thethermochemical conversion of methane (CH4). This work has as mainobjective a theoretical modelling to describe the process variables of SteamReforming of Methane (SRM) method in the TPB reformer. The TPBreformer is filled with β-SiC open-cell foam where the thermochemicalconversion of CH4 is carried out. The model variables describe the specificaims of work and these objectives can be identified from each equation ofthe developed mathematical model. This work has been proposed to studytwo specific aims as (i) the effective thermal conductivity's effect of thesolid phase (λs,eff.) and (ii) molar flows of chemical components. Theendothermic reaction temperature's profiles are notably increased as thenumeral value of λs,eff. is raised. The Steam Reforming of Methane (SRM)method is suggested to improve the Production Rate (PR) of H2 regardingthe PR of CO. As results, the PR of H2 is of 29.48% while the PR of CO isof 2.76%.

Synthesis of gasoline range fuels by the catalytic cracking of waste plastics using titanium dioxide and zeolite

The current study examined the carbon recycling application of waste materials. Thermal catalytic cracking reactions were carried out in a fixed bed to synthesize gasoline-range hydrocarbon fuels from used plastics. Titanium (IV) oxide (TiO2) and zeolite were tested as catalysts for pyrolysis using low-density polyethylene (LDPE), polyvinylchloride (PVC), and polystyrene (PS) reactants. In addition to the catalyzed pyrolysis reactions, we also investigated non-catalyzed thermal degradation of the plastic substrates for negative control. The liquid yield, reaction temperature profile, and physical appearance of the synthesized liquid products were determined. The pyrolysis reactions demonstrated that the optimum catalyst–polymer ratio is 40%. The distillate collection temperatures ranged between 82 and 198 °C (LDPE), 68–172 °C (PVC), and 40–168 °C (PS). Our experiments showed that LDPE, PVC, and PS can readily be pyrolyzed to produce 44% (LDPE), 13% (PVC), and 89% (PS) hydrocarbon liquid products using zeolite catalyst. Gas chromatography–mass spectrometry (GC–MS) was used to analyze the structure and chemical composition of the products. The main products were C5 (1,2-dimethylcyclopropane), C6 (2-methylpentane), C7 (1,3-dimethylcyclopentene, 1-heptene), and C8 (2-octene, 4-octene, octane, 3-ethylhexane), indicating gasoline-range hydrocarbon molecules. The highest liquid yield of 89.3% was obtained from zeolite catalyst over polystyrene in comparison to all plastics cracked while the lowest liquid yield of 3.9% was obtained from the cracking of PVC under no catalyst condition.

Directly Microwave-Accelerated Cleavage of C-C and C-O Bonds of Lignin by Copper Oxide and H2 O2.

Model erythro, phenolic, and nonphenolic lignin β-O-4 dimer compounds are treated with copper oxide and H2 O2 at the electronic field maximum position of a single-mode 2.45 GHz microwave system equipped with a cavity resonator. The products obtained through microwave heating and oil-bath heating with the same reaction vessel and temperature profile are quantitatively compared. Dimer degradation is found to proceed through consecutive elementary reactions. The phenolic dimer is dehydroxylated and this is followed by the spontaneous cleavage of Cα -Cβ and C-O-C bonds to produce guaiacol, vanillin, and vanillic acid. The reaction of the nonphenolic dimer produces veratric acid, veratraldehyde, and guaiacol. Microwave irradiation accelerates cleavage of the side chain and the oxidation of vanillin to vanillic acid. However, no acceleration of veratraldehyde oxidation to veratric acid or aromatic ring cleavage to produce dicarboxylic acids is observed. The selective acceleration of elementary reactions during the degradation of model lignin compounds indicates that microwaves interact with reaction intermediates that are sensitive to electromagnetic waves.