Comprehensive kinetic model for acetylene pretreated mesoporous silica supported bimetallic Co-Ni catalyst during Fischer-Trospch synthesis

Sun, Yi; Wang, Yixiao; He, Jun; Yusuf, Abubakar; Wang, Yunshan; Yang, Gang; Xiao, Xin

doi:10.1016/j.ces.2021.116828

Cited by 10 publications

(6 citation statements)

References 71 publications

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“…In this work, the shrinking core model (SCM) is used to estimate the conversion (on the KCl basis). 25 , 26 The reaction time ( t ) and the KCl conversion ( x ) can be expressed in the following form, which includes all the controlling steps where t is the reaction time (min), C S0 is the initial solid concentration (mol/L), C A0 is the initial concentration (mol/L), k g is the gaseous diffusion constant factor (m 2 /s), k s is the surface reaction constant factor (m 2 /s), and D e is the effective diffusivity (this refers to diffusion of vapor KCl in steam at a higher temperature, m/s). The modified correlation between the conversion and kinetic constant factor for the reaction is depicted as follows where k c is the modified reaction rate constant (m –0.6 /s), d is the average diameter of the particle (in this work, the averaged particle size is around 23 μm and the particle size distribution of PS + CaCl 2 is shown in Figure S3 ), E a is the activation energy (kJ/mol), R is the ideal gas constant (8.31 J/mol/K), T is the temperature of reaction (K), ρ c is the density of the PS particle (kg/m –3 ), M is the molecular mass of the reagent (kg/mol), and r is the average particle radius (m) with the following correlation where S refers to the surface area of the reagent (PS + CaCl 2 ); in this work, all particles in the ceramic boat reactor are assumed to be around 1.5 m 2 /g.…”

Section: Results and Discussionmentioning

confidence: 99%

Clean Process to Utilize the Potassium-Containing Phosphorous Rock with Simultaneous HCl and KCl Production via the Steam-Mediated Reactions

Wang

Shi

et al. 2022

ACS Omega

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In this paper, a clean process based on the steam-mediated reactions for simultaneous HCl and KCl production using the potassium (K)-containing phosphorous rock as a precursor is proposed. Through hydrochloric acid (HCl) leaching, not only the generation of H 3 PO 4 and CaCl 2 (via further precipitation) were realized but also the acid-insoluble residue [phosphorous-rock slag (PS)] rich in elements, that is, K, Al, Si, and so on, in the form of microcline (KAlSi 3 O 8 ) and quartz (SiO 2 ) was obtained and became readily available for further HCl and KCl generation. Over 95% of the elements, that is, K, Al, and Si, come into the final products, and the overall acid consumption (based on HCl) is significantly reduced (90%) due to recovery of acids. The impacts of the key operational parameters such as temperature, duration, and reagent impregnate ratio were rigorously analyzed via a supervised machine learning approach, and the optimal conditions were determined [reaction temperature, X 1 , 850 °C; reaction duration, X 2 , 40 min; and impregnate ratio (PS over CaCl 2 ), X 3 , 2.5] with approximately ±10% uncertainties. Thermodynamic analysis indicates that the introduction of steam to PS + CaCl 2 not only enhances the chemical potential for the formation of HCl and KCl but also provides the transport advantage in continuously removing the generated products, that is, HCl and KCl, out of the system. Molecular simulation indicates that the presence of both steam and SiO 2 in the PS matrix plays critical roles in decomposing PS + CaCl 2 at high temperature. The shrinking core model shows that both the intrinsic kinetics and transport are influential with the activation energy being around 14.63 kJ/mol. The potential reaction pathway is postulated.

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Section: Results and Discussionmentioning

confidence: 99%

Clean Process to Utilize the Potassium-Containing Phosphorous Rock with Simultaneous HCl and KCl Production via the Steam-Mediated Reactions

Wang

Shi

et al. 2022

ACS Omega

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“…To account for the olefin to paraffin ratio (OPR), the 1-olefin readsorption model assuming weak Van der Waal's interaction between olefin and the surface of the catalyst is deployed. Then, the activation energy for olefin readsorption can be written as 16,38 = + E E nE n re re 0 re (6) where E re 0 refers to the readsorption energy that is independent of the chain length (kJ•mol −1 ), E re refers to the part of activation energy that is dependent on the chain length (kJ• mol −1 ), and n refers to the carbon number (n ≥ 4). Then, the rate constant can be expressed as follows 39…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…To account for the olefin to paraffin ratio (OPR), the 1-olefin readsorption model assuming weak Van der Waal’s interaction between olefin and the surface of the catalyst is deployed. Then, the activation energy for olefin readsorption can be written as , where E re 0 refers to the readsorption energy that is independent of the chain length (kJ·mol –1 ), E re refers to the part of activation energy that is dependent on the chain length (kJ·mol –1 ), and n refers to the carbon number ( n ≥ 4). Then, the rate constant can be expressed as follows The rate constants of chain-length-dependent (CLD) readsorption and desorption can be expressed as follows where ε c * and ε c d* are the constants reflecting the part of activation energy of olefin readsorption and desorption with carbon number dependency and R refers to the gas constant (8.314 J·K –1 ·mol –1 ).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Many scholars have proposed different modeling approaches to model the kinetics. − These models include (i) data regression by formulating the minimization of the sum of the square of errors through a numerical Levenberg–Marquardt (LM) algorithm, (ii) a genetic algorithm (GA) to model the product distribution of FT synthesis such as using the Anderson–Schulz–Flory (ASF) method, (iii) an explicit model based on a second-order polynomial for correlating the critical operational parameters using a response surface methodology (RSM), and (iv) data-driven soft computation machine learning techniques such as using different artificial neuron networks (ANNs) in directly correlating the product distribution and critical operational parameters. − …”

Section: Introductionmentioning

confidence: 99%

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Kinetic Study of Product Distribution Using Various Data-Driven and Statistical Models for Fischer–Tropsch Synthesis

Wang

Zhang

et al. 2021

ACS Omega

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Three modeling techniques, namely, a radial basis function neural network (RBFNN), a comprehensive kinetic with genetic algorithm (CKGA), and a response surface methodology (RSM), were used to study the kinetics of Fischer−Tropsch (FT) synthesis. Using a 29 × 37 (4 independent process parameters as inputs and corresponding 36 responses as outputs) matrix with total 1073 data sets for data training through RBFNN, the established model is capable of predicting hydrocarbon product distribution i.e., the paraffin formation rate (C 2 −C 15 ) and the olefin to paraffin ratio (OPR) within acceptable uncertainties. With additional validation data sets (15 × 36 matrix with total 540 data sets), the uncertainties of using three different models were compared and the outcomes were: RBFNN (±5% uncertainties), RSM (±10% uncertainties), and CKGA (±30% uncertainties), respectively. A new effective strategy for kinetic study of the complex FT synthesis is proposed: RBFNN is used for data matrix generation with a limited number of experimental data sets (due to its fast converge and less computation time), CKGA is used for mechanism selections by the Langmuir−Hinshelwood−Hougen−Watson (LHHW) approach using a genetic algorithm to find out potential reaction pathways, and RSM is used for statistical analysis of the investigated data matrix (generated from RBFNN through central composite design) upon responses and subsequent singular/multiple optimizations. The proposed strategy is a very useful and practical tool in process engineering design and practice for the product distribution during FT synthesis.

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“…However, the current predominant H 2 generation still comes from fossil-based materials via existing mature industrial chemical processes such as natural gas steam reforming (NGSR), nature gas thermal cracking (NGTC), auto-thermal reforming (ATR), coal gasification, and partial oxidation of heavier-than-naphtha hydrocarbons [8]. Consequently, the paradox of sustainability of H 2 utilization and the non-renewability of H 2 generation will be encountered, although the development of carbon capture storage and utilization (CCSU) such as via a mature catalytic process like Fischer-Tropsch synthesis might alleviate environmental impacts from H 2 generation [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Review of Enhancement of Biohydrogen Productions by Chemical Addition Using a Supervised Machine Learning Method

Liu

et al. 2021

Energies

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In this work, the impact of chemical additions, especially nano-particles (NPs), was quantitatively analyzed using our constructed artificial neural networks (ANNs)-response surface methodology (RSM) algorithm. Fe-based and Ni-based NPs and ions, including Mg2+, Cu2+, Na+, NH4+, and K+, behave differently towards the response of hydrogen yield (HY) and hydrogen evolution rate (HER). Manipulating the size and concentration of NPs was found to be effective in enhancing the HY for Fe-based NPs and ions, but not for Ni-based NPs and ions. An optimal range of particle size (86–120 nm) and Ni-ion/NP concentration (81–120 mg L−1) existed for HER. Meanwhile, the manipulation of the size and concentration of NPs was found to be ineffective for both iron and nickel for the improvement of HER. In fact, the variation in size of NPs for the enhancement of HY and HER demonstrated an appreciable difference. The smaller (less than 42 nm) NPs were found to definitely improve the HY, whereas for the HER, the relatively bigger size of NPs (40–50 nm) seemed to significantly increase the H2 evolution rate. It was also found that the variations in the concentration of the investigated ions only statistically influenced the HER, not the HY. The level of response (the enhanced HER) towards inputs was underpinned and the order of significance towards HER was identified as the following: Na+ > Mg2+ > Cu2+ > NH4+ > K+.

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Comprehensive kinetic model for acetylene pretreated mesoporous silica supported bimetallic Co-Ni catalyst during Fischer-Trospch synthesis

Cited by 10 publications

References 71 publications

Clean Process to Utilize the Potassium-Containing Phosphorous Rock with Simultaneous HCl and KCl Production via the Steam-Mediated Reactions

Clean Process to Utilize the Potassium-Containing Phosphorous Rock with Simultaneous HCl and KCl Production via the Steam-Mediated Reactions

Kinetic Study of Product Distribution Using Various Data-Driven and Statistical Models for Fischer–Tropsch Synthesis

A Review of Enhancement of Biohydrogen Productions by Chemical Addition Using a Supervised Machine Learning Method

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