Methanol steam reforming (MSR) is one the most interesting routes for production of fuel cell grade hydrogen. As this reaction is endothermic, its energy supply is one of the most important problems. In this line, recycling and burning the unconverted hydrogen exits from the exhaust of the fuel cells on the shell side of a shell-and-tube reactor (HR) has been suggested in the literature as one the energy supply solutions. In this work, the performance of a membrane-assisted shell-and-tube reactor (MR-HCO), in which part of the hydrogen produced by the MSR is continuously transferred to the shell side through the membrane layer and burned to supply energy, is investigated and compared with HR reactor. To this end, a set of heterogeneous 1D and 2D models are employed to model the shell and the tube sides of the reactors, respectively. The influence of air and steam-methanol feed hourly space velocities on maximum combustion temperature and methanol conversion are examined. It is observed that the MR-HCO has higher methanol conversion, hydrogen flow, and thermal efficiency, while lower CO concentration compared with the HR reactor. Furthermore, the MR-HCO showed a good potential to control the shell temperature rise and prevent hot spots. © 2023 Elsevier Ltd
Neural Computing and Applications (09410643)35(11)pp. 8517-8541
Using a reliable predictive model is important for modeling, controlling, and optimization of the isomerization process. This process has a significant impact on the gasoline quality, which can reduce greenhouse gases by improving the octane number. On the other hand, the accuracy of the predicted results of a data-driven model depends on the quality of input data; this is while the measured variables of industrial units are inevitably contaminated by various errors. Hence, the present work proposes an improved adaptive machine learning model and a new hybrid multiscale filter to predict the gasoline research octane number reliably from error-contaminated data of a light naphtha isomerization reactor. The proposed machine learning model is based on the integration of the feature selection algorithm of the double-level similarity with the support vector regression model (named DLS-SVR model) for adaptive prediction. The new hybrid filter is based on a combination of the wavelet transform and median absolute deviation, named multiscale median absolute deviation (MSMAD). MSMAD filter is proposed with the aim to establish an accurate method to identify and eliminate outliers and gross errors from the measured process variables. A pilot-scale reactor is employed to provide the required experimental validating dataset to evaluate the predictive performance of the proposed filter–model combination. Inputs of the DLS-SVR model are operating conditions (temperature: 115–150 °C, pressure: 28–42 bar, space velocity: 0.38–3 h−1) and feed composition (benzene: 0–3.5 wt%, cyclohexane: 0.8–23.2 wt%, methylcyclopentane: 1–29 wt%, H2/naphtha ratio: 0.03–0.3). The performance of the DLS-SVR model is compared with the response surface methodology, support vector regression, and double-level locally weighted extreme learning machine through the fivefold cross-validation technique. The particle swarm optimization–sequential quadratic programming algorithm is used to optimize the hyper-parameters of these models. The results prove that the generalized DLS-SVR model outperforms the other generalized models. Furthermore, the performance of the MSMAD filter is compared with the multiscale median, finite impulse response–median hybrid, median, and median absolute deviation filters by rectifying the error-contaminated temperature signal. Findings reveal that the DLS-SVR model utilizing the rectified signal by the MSMAD filter has a maximum coefficient of determination, R2 = 0.91, and minimum root mean square error, RMSE = 0.0562, among the other filter's rectified temperature signals. These values for error-free data are R2 = 0.945 and RMSE = 0.0439. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Industrial and Engineering Chemistry Research (08885885)62(24)pp. 9433-9452
In this work, the use of a reverse-flow microchannel monolithic reactor is theoretically studied for improving the performance of syngas production by catalytic partial oxidation of methane over an Rh/Al2O3 catalyst. This helps to make a better judgment about the possibility of syngas production using this type of small-scale reactor. Imposing reverse-flow operation on catalytic microchannels creates eminent features that enable overcoming some drawbacks of technologies based on partial oxidation and increasing the reaction performance. A one-dimensional heterogeneous unsteady-state model is employed to model the reactor behavior. The full GRI 3.0 mechanism is employed to model the gas-phase reactions, and a detailed Langmuir-Hinshelwood surface mechanism is considered for catalytic reactions. The effects of feed preheat temperature, feed CH4/O2 ratio, reaction pressure, and flow switching time on methane conversion and syngas quality and yield are studied. The performance of the reverse-flow operation was also compared with the unidirectional one. The simulation results agree with the literature-reported experimental data, and the model can predict the reactor behavior well. The results show that the reverse-flow operation can significantly improve the syngas production yield and reduce the minimum preheat temperature required to light off the reactor. The maximum syngas production yield of ∼75% and H2/CO ratio of ∼2.7 are achieved at a feed CH4/O2 ratio of ∼1.6 after the cyclic steady state is established. The reverse-flow operation increases the methane conversion and syngas yield by at least 8% and 76%, respectively, compared with the unidirectional one. © 2023 American Chemical Society
In this research, a data-driven adaptive model is developed to predict the variables indicating gasoline quality in the light naphtha isomerization process and determine the optimal conditions leading to improved gasoline quality. To this end, an integrated method based on double-level similarity criterion and support vector regression (DLS-SVR) is proposed. The variables that indicate gasoline quality are research octane number (RON), benzene volume percentage (BVP), and Reid vapor pressure (RVP). In addition to the influential operating variables of pressure, temperature, feed weight hourly space velocity (WHSV), and hydrogen to naphtha feed molar ratio, the model considers benzene's feed concentration and cycloparaffin content. Experiments are conducted using commercial Pt/Al2O3-CCl4 catalyst in a pilot-scale packed-bed reactor. The developed model's predictive performance and generalization ability are compared with the response surface methodology, support vector regression, and double-level locally weighted extreme learning machine through the fivefold cross-validation technique. The generalized DLS-SVR predicts gasoline's RON, BVP, and RVP with R2 = 0.901, 0.959, and 0.931 and RMSE = 0.055, 0.061, and 0.053, respectively, indicating that its performance is superior to alternative generalized models. The optimal conditions are computed using the DLS-SVR model and co-evolutionary particle swarm optimization algorithm (CPSO). The optimal operation of the reactor yielded a 6.78-unit increase in gasoline RON and a minimum BVP of 0.394 %. The results demonstrate that the proposed DLS-SVR model can accurately predict the variables indicating the quality of isomerate gasoline. © 2022 Elsevier Ltd
Journal of Natural Gas Science and Engineering (18755100)102
Gas leaks from natural gas pipelines can lead to catastrophic incidents, especially in the case of sour natural gas owing to the combination of its toxicity and flammability. As a safety consideration, these pipelines are underground to protect humans and installations. However, no comprehensive model has yet been proposed that can predict the leakage rate from the damaged buried pipelines in a wide range of influential factors. In this work, to compensate for this shortcoming, a set of soil classified models are presented considering the emission of sour natural gas in silty, sandy, and gravelly soils using the results of optimal design-based CFD simulations. In this way, a wide range is selected for effective factors of pipe pressure (2–100 bara), leakage hole diameter (2–40 mm), pipe diameter (4–56 in), soil porosity (0.3–0.45), and soil particle diameter (0.002–40 mm). These ranges cover the specifications of both urban distribution pipeline systems and transmission ones. A two-step solution strategy is implemented to consider the effect of pressure drop on the leakage rate. The CFD simulations are in good agreement with experimental data reported in literature. The leakage models are capable to predict the results of random simulations with a mean absolute percentage error of 13%, 9%, and 7.7% for silty, sandy, and gravelly soils, respectively, over a wide range of pressure and leakage hole diameter. Furthermore, the effects of soil mass properties and pipe wall thickness on the leakage process are investigated. To clarify the effect of soil mass on leakage rate, the CFD analysis of an aboveground leaking pipe is also performed comparatively. © 2022 Elsevier B.V.
Chemical Engineering and Processing - Process Intensification (02552701)167
The performance of a honeycomb monolith and a fixed-bed of cylindrical extruded HZSM-5 catalyst for the methanol to olefins process (MTO) are evaluated and compared using two-scale two-dimensional heterogeneous models in adiabatic and diabatic operations considering two approaches of “restricted comparison” and “free comparison”. The 53-step elementary reaction mechanism of Mihail and coworkers is used to model the MTO reactions. The effects of space-time (0.1–5 s), feed water content (up to 60 mol%), and monolith cell density (200–600 cpsi) on the catalyst temperature, feed conversion, and selectivity and yield of olefins are investigated. “Restricted comparison” indicates that the feed conversion and selectivity and yield of the total olefins in the monolithic reactor are greater than the fixed-bed with a maximum discrepancy of 5%. There is an optimum space-time of ~ 0.3 s at which the yield of total light olefins in the monolith and fixed-bed reactors achieves the maximum values of 0.45 and 0.43, respectively. The results of “free comparison” reveal that a 600 cpsi monolith can produce ~ 43% more light olefins using ~ 75% less catalyst mass at the optimal space-time of 0.3 s compare with a fixed-bed reactor at an identical specific surface area. © 2021 Elsevier B.V.
Chemical Engineering Research and Design (02638762)121pp. 134-148
Rhodium coated monoliths offer a considerable potential for efficient industrial scale production of syngas through catalytic partial oxidation of methane. Much of the previous experimental and theoretical researches in this area have been conducted at a near atmospheric pressure, whereas for industrial applications, pressures up to 30 bar may be required. The boundary-layer flow model is used to make a thorough investigation of partial oxidation of methane in monolithic reactors at high pressure. The theoretical findings of the present study show that provided the operating conditions are chosen carefully, increasing the pressure does not significantly affect the selectivity, yield and quality of the syngas compared to low pressure operation. Experimental evidence supporting the theoretical findings reported in this article are emerging through the high pressure measurements at the University of Minnesota (Bitsch-Larsen et al., 2008). © 2017 Institution of Chemical Engineers
Journal of Power Sources (03787753)352pp. 64-76
Modeling and CFD simulation of a three-dimensional microreactor includes thirteen structured parallel channels is performed to study the hydrogen production via methanol steam reforming reaction over a Cu/ZnO/Al2O3 catalyst. The well-known Langmuir-Hinshelwood macro kinetic rate expressions reported by Peppley and coworkers [49] are considered to model the methanol steam reforming reactions. The effects of inlet steam to methanol ratio, pre-heat temperature, channels geometry and size, and the level of external heat flux on the hydrogen quality and quantity (i.e., hydrogen flow rate and CO concentration) are investigated. Moreover, the possibility of reducing the CO concentration by passing the reactor effluent through a water gas shift channel placed in series with the methanol reformer is studied. Afterwards, the simulation results are compared with the experimental data reported in the literature considering two different approaches of mixture-averaged and Maxwell-Stefan formulations to evaluate the diffusive flux of mass. The results indicate that the predictions of the Maxwell-Stefan model is in better agreement with experimental data than mixture-averaged one, especially at the lower feed flow rates. © 2017 Elsevier B.V.
Chemical Engineering Journal (13858947)244pp. 317-326
The effects of supercritical fluid (SCF) on Fischer-Tropsch synthesis over an industrial well-characterized Co-Ru/γ-Al2O3 was studied in a laboratory fixed bed reactor. The influence of reaction conditions (such as temperature (240-260°C), pressure (50-80bar), syngas feed ratio (H2/CO ratio of 0.5-2.5), and contact time and syngas flow rate (3000-6000Nccgcat-1h-1)) on the FTS activity, selectivity, and hydrocarbon product distributions in the supercritical fluid media was studied. The opportunity to selectively control and maximize the production of the desired fuel fractions from the FTS hydrocarbons spectrum, by tuning either reaction temperature and reaction pressure, was investigated. This study also covers an attempt to understand and model the enhanced chain growth probability in the supercritical phase FTS that resulted in significant deviations from the standard Anderson-Shultz-Flory (ASF) model (specifically within the middle distillate hydrocarbons). The influence of solvent (pure n-hexane and n-pentane) at constant temperature (240°C) and density (0.3g/cm3) on the selectivity of FTS in the product hydrocarbon as a function of carbon number was investigated under supercritical conditions. Similar hydrocarbon distribution was obtained, however CO conversion in supercritical hexane was obviously lower than that in supercritical pentane medium. The reaction performances of the Fischer-Tropsch synthesis in the supercritical phase was also compared with those in the conventional gas phase FTS. The results indicate that in most cases, supercritical FTS showed higher activity and better selectivity towards the most desired products compared to conventional gas phase FTS. © 2014 Elsevier B.V.
International Journal of Hydrogen Energy (03603199)39(7)pp. 3269-3285
In this work, a set of cylindrical rhodium coated micro channels with diameter range of 0.2-0.8 mm and a length of 1 cm is considered to simulate the behavior of the autothermal reforming reactions of methane to synthesis gas. A one-dimensional heterogeneous model is used to describe the flow field. Gas phase reactions are modeled by GRI 3.0 mechanism, and a 38-step detailed surface mechanism over a rhodium catalyst is considered for catalytic reactions. The effect of feed moisture content on product selectivity and yield, minimum required preheat temperature, length of the reforming and oxidation zones, and reactor thermal efficiency is studied at two different operating pressures of 1 and 20 atm, methane to oxygen feed ratios of 1.2-2.5, and pore diameters of 0.2-0.8 mm. Moreover, the simulation results are compared with the experimental data of Michael et al. [13]. © 2013, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserver.
Fuel Processing Technology (03783820)90(10)pp. 1305-1313
The rate of Fischer-Tropsch synthesis over an industrial well-characterized Co-Ru/γ-Al2O3 catalyst was studied in a laboratory well mixed, continuous flow, slurry reactor under the conditions relevant to industrial operations as follows: temperature of 200-240 °C, pressure of 20-35 bar, H2/CO feed ratio of 1.0-2.5, gas hourly space velocity of 500-1500 N cm3 gcat- 1 h- 1 and conversions of 10-84% of carbon monoxide and 13-89% of hydrogen. The ranges of partial pressures of CO and H2 have been chosen as 5-15 and 10-25 bar respectively. Five kinetic models are considered: one empirical power law model and four variations of the Langmuir-Hinshelwood-Hougen-Watson representation. All models considered incorporate a strong inhibition due to CO adsorption. The data of this study are fitted fairly well by a simple LHHW form - RH2 + CO = apH20.988pCO0.508 / (1 + bpCO0.508)2 in comparison to fits of the same data by several other representative LHHW rate forms proposed in other works. The apparent activation energy was 94-103 kJ/mol. Kinetic parameters are determined using the genetic algorithm approach (GA), followed by the Levenberg-Marquardt (LM) method to make refined optimization, and are validated by means of statistical analysis. Also, the performance of the catalyst for Fischer-Tropsch synthesis and the hydrocarbon product distributions were investigated under different reaction conditions. © 2009 Elsevier B.V. All rights reserved.
Sari, A.,
Safekordi, A.,
Farhadpour, F.A. International Journal of Chemical Reactor Engineering (21945748)6
Catalytic partial oxidation of methane in short residence time rhodium coated monolithic reactors offers an attractive route for syngas production. The plug flow and boundary layer flow approximations are considered as computationally efficient substitutes for the full Navier-Stokes model of the reactor while including detailed heterogeneous and homogeneous chemistry. The one dimensional plug flow model has trivial computational demands but only a limited range of application. The boundary layer model provides an excellent, computationally manageable substitute for the full Navier-Stokes model over a wide range of operating conditions. Using the 38-step elementary surface reaction mechanism of Deutschmann et al. (2001a) and the full GRI 3.0 mechanism for gas phase oxidation of methane, the boundary layer model can predict the experimental data of Hickman et al. (1993a) and Horn et al. (2007) with high accuracy. Sensitivity analyses with the boundary layer model delineate the complex surface phenomena responsible for the sharp transitions in reactor performance observed experimentally on increasing the feed methane to oxygen ratio. The detrimental influence of gas phase reactions on increasing the pressure to industrial levels (>20 bars) is also clearly demonstrated. Copyright © 2008 The Berkeley Electronic Press. All rights reserved.
