Revisiting the importance of appropriate parameter estimation based on sensitivity analysis for developing kinetic models

Sámano, Vicente; Tirado, Alexis; Félix, Guillermo; Ancheyta, Jorge

doi:10.1016/j.fuel.2020.117113

Cited by 18 publications

(11 citation statements)

References 6 publications

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“…Meanwhile, the SSE of the composition of different products is extremely low. Sensitivity analysis is applied to assess that in the nonlinear parameter estimation by means of perturbations for the aimed parameter value ranging from −20 to 20%. − Analysis of parameter sensitivity is practiced for the important k 1 , k 5 , k 8 , and k 10 values at 350 °C for the NiW/AY-15 catalyst. The plots of the perturbation range and SSE values of the objective function are shown in Figure S2.…”

Section: Resultsmentioning

confidence: 99%

Hydrocracking Straight-Run Diesel into High-Value Chemical Materials: The Effect of Acidity and Kinetic Study

Cao

Zhang

Mei

et al. 2022

Ind. Eng. Chem. Res.

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A novel process of hydrocracking straight-run diesel was proposed for producing more chemical materials. The influences of acidities and hydrogenation abilities for serial bifunctional catalysts on hydrocracking behaviors for diesel were also investigated. The results showed that the acidities of catalysts increased with additional amounts of Y zeolite, and the hydrogenation abilities enhanced with the amounts of Al 2 O 3 due to its large pore size and pore volume, which could provide a preferable support colony for active metals to disperse. The X-ray photoelectron spectra (XPS) and high-resolution transmission electron microscopy (HRTEM) results demonstrated that the NiW/AY-15 catalyst had the highest proportion and dispersion degree of MoS 2 active phases. Meanwhile, the evaluation results of straight-run diesel hydrocracking exhibited that the light naphtha, jet fuel, and upgraded diesel with large amounts of paraffins could be directly applied as feedstock of the steam-cracking unit for producing olefins. The heavy naphtha with high amounts of aromatics and cyclanes could also serve as high-quality feedstock for the catalytic reforming unit. Therefore, the quantities of chemical materials were maximized through hydrocracking straight-run diesel. Moreover, the amounts of paraffins in the hydrocracking product of diesel over the NiW/AY-15 catalyst were the highest among all of the catalysts due to its best hydrogenation performance. Finally, the kinetic model and reaction characteristics of straight-run diesel hydrocracking were initially proposed on the basis of the experimental results. The kinetic analysis proved that hydrocracking of straight-run diesel followed the pseudo-first-order reaction.

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Section: Resultsmentioning

confidence: 99%

Hydrocracking Straight-Run Diesel into High-Value Chemical Materials: The Effect of Acidity and Kinetic Study

Cao

Zhang

Mei

et al. 2022

Ind. Eng. Chem. Res.

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“…Nevertheless, this latter method does not always allow for converging to the optimal minimum, and hence, other techniques are required, such as random algorithms, among which the most used one is the Monte Carlo algorithm. [2][3][4][5]7,9,10 The nonlinear optimization is generally carried out by minimizing an OF based on the difference between the experimental and calculated data, the most used being the sum of the squared errors (SSE, eq 1). Nonetheless, this is not the only OF that can be used.…”

Section: Introductionmentioning

confidence: 99%

“…An additional advantage of the sensitivity analysis is to identify if one or more parameters are sensible to perturbations and significantly change the results of the model or if changing the value completely do not change the results. [2][3][4]11,18 Nowadays, the development of most of the kinetic models reported in the literature does not consider detailed statistical analyses during the kinetic parameter estimation, thus incorrectly selecting the initial values for the optimization algorithm. Moreover, the calculated parameter values are not always verified for local minima or global minimum of the selected objective function, which is a common drawback.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo and Sensitivity Analysis Methods for Kinetic Parameters Optimization: Application to Heavy Oil Slurry-Phase Hydrocracking

et al. 2022

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A methodology to estimate kinetic parameters using the Monte Carlo algorithm and sensitivity analysis is described. The approach is applied to the experimental data reported in the literature for slurry-phase hydrocracking of heavy oil with ionic liquids. All experiments were carried out in a batch reactor at a reaction temperature of 430 °C, H 2 pressure of 12.3 MPa, and reaction times of 0.5−6 h. It is demonstrated that the reported values of kinetic parameters can be optimized so that the average absolute error is substantially reduced from 21.73 to 7.93%. Simulations with the Monte Carlo algorithm with various initial values of parameters help find the best initial guess for further optimization of parameters. The prediction of product yields was improved using the average absolute error as objective function since it was distributed equally for all products, being coke the lump with greater error reduction (from 57.39 to 7.60%). The sensitivity and statistical analyses performed on the optimized reaction rate coefficients confirmed that the obtained results are the optimal values that minimize the error between calculated and experimental data. The lowest value of average absolute error (7.93%) determined by sensitivity analysis was found for the optimized values; moreover, the slope and intercept of the parity plot are almost 1 and 0, respectively.

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“…The modeling of characteristics of petroleum and its derivatives has been a subject of numerous studies [1,2]. Different regression techniques [3][4][5][6][7][8][9][10][11][12][13][14] and artificial intelligence [15,16] (machine learning, neural network) approaches have been applied to model petroleum characteristics. Nonlinear regression has been the most used approach for model parameter estimation [17].…”

Section: Introductionmentioning

confidence: 99%

Different Nonlinear Regression Techniques and Sensitivity Analysis as Tools to Optimize Oil Viscosity Modeling

et al. 2021

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Four nonlinear regression techniques were explored to model gas oil viscosity on the base of Walther’s empirical equation. With the initial database of 41 primary and secondary vacuum gas oils, four models were developed with a comparable accuracy of viscosity calculation. The Akaike information criterion and Bayesian information criterion selected the least square relative errors (LSRE) model as the best one. The sensitivity analysis with respect to the given data also revealed that the LSRE model is the most stable one with the lowest values of standard deviations of derivatives. Verification of the gas oil viscosity prediction ability was carried out with another set of 43 gas oils showing remarkably better accuracy with the LSRE model. The LSRE was also found to predict better viscosity for the 43 test gas oils relative to the Aboul Seoud and Moharam model and the Kotzakoulakis and George.

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Revisiting the importance of appropriate parameter estimation based on sensitivity analysis for developing kinetic models

Cited by 18 publications

References 6 publications

Hydrocracking Straight-Run Diesel into High-Value Chemical Materials: The Effect of Acidity and Kinetic Study

Hydrocracking Straight-Run Diesel into High-Value Chemical Materials: The Effect of Acidity and Kinetic Study

Monte Carlo and Sensitivity Analysis Methods for Kinetic Parameters Optimization: Application to Heavy Oil Slurry-Phase Hydrocracking

Different Nonlinear Regression Techniques and Sensitivity Analysis as Tools to Optimize Oil Viscosity Modeling

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