Application of response surface methodology and artificial neural network on pyrolysis of safflower seed press cake

Angın, Dilek; Tiryaki, Aysun Egrisogut

doi:10.1080/15567036.2013.862585

Cited by 18 publications

(12 citation statements)

References 13 publications

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“…There is a number of papers [ 63 , 64 , 65 , 66 ] on the application of ANNs for obtaining the total conversion (e.g., mass loss by TGA) using experimental conditions as an input. One of the first papers on the topic has been by Carsky and Kuwornoo [ 67 ] who used ANN to predict the main quantities for coal pyrolysis: tar, volatiles, and char yields.…”

Section: Prediction Of Conversion Data (Single Value Whole Curve)mentioning

confidence: 99%

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Muravyev

Luciano²,

Ornaghi

et al. 2021

Molecules

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Artificial neural networks (ANNs) are a method of machine learning (ML) that is now widely used in physics, chemistry, and material science. ANN can learn from data to identify nonlinear trends and give accurate predictions. ML methods, and ANNs in particular, have already demonstrated their worth in solving various chemical engineering problems, but applications in pyrolysis, thermal analysis, and, especially, thermokinetic studies are still in an initiatory stage. The present article gives a critical overview and summary of the available literature on applying ANNs in the field of pyrolysis, thermal analysis, and thermokinetic studies. More than 100 papers from these research areas are surveyed. Some approaches from the broad field of chemical engineering are discussed as the venues for possible transfer to the field of pyrolysis and thermal analysis studies in general. It is stressed that the current thermokinetic applications of ANNs are yet to evolve significantly to reach the capabilities of the existing isoconversional and model-fitting methods.

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Section: Prediction Of Conversion Data (Single Value Whole Curve)mentioning

confidence: 99%

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Muravyev

Luciano²,

Ornaghi

et al. 2021

Molecules

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“…and aromatic hydrocarbons [ 85 , 86 ]. Several research works were reported on the thermal treatment of different vegetable oils like sunflower, safflower, palm, castor, tung oils [ [87] , [88] , [89] ]. The comparison between the characteristics of pure soyabean and pyrolyzed soyabean oil is given…”

Section: Unprocessed Algal Oils As Biofuelsmentioning

confidence: 99%

A review on prospective production of biofuel from microalgae

Ganesan

Manigandan

Samuel

et al. 2020

Biotechnology Reports

178

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“…As one of the surrogate models, the response surface can more accurately represent the functional relationship between input and output. Compared to other surrogate models, the response surface belongs to the display function that can be more easily used for reliability calculations [29]. Due to the poor approximation ability of the linear response surface, the quadratic response surface with cross terms is adopted.…”

Section: Establishment Of Response Surface Functionmentioning

confidence: 99%

Fatigue Reliability Analysis of Motor Hanger for High‐Speed Train Based on Bayesian Updating and Subset Simulation

Zhi

Zhang

et al. 2020

Advances in Materials Science and Engineering

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In order to more accurately analyze the fatigue reliability of motor hanger for high-speed train and reduce the influence of uncertain factors, a Bayesian statistical method is introduced to propose a novel fatigue reliability analysis method based on Bayesian updating and subset simulation. First, considering the influence of various uncertain parameters on the first principal stress (FPS) of motor hanger, the ANSYS parametric design language (APDL) is used to establish the parametric model. e D-optimal design of experiment is carried out to calculate the FPS of the motor hanger. Second, the experimental data is fitted by the least square method to establish a polynomial response surface function which characterizes the FPS of the motor hanger, and analysis of variance (ANOVA) is carried out. On this basis, the variation trend of the FPS under parameter fluctuation is calculated, and its probability distribution characteristics are obtained. Based on the MATLAB platform, the Bayesian updating method is adopted to correct the probability and statistical characteristics of the FPS to improve the accuracy of prediction. Finally, the subset simulation (SS) method is used to calculate the fatigue failure probability of the motor hanger. e research results show that the proposed method helps to improve the accuracy and efficiency of fatigue reliability analysis.

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Application of response surface methodology and artificial neural network on pyrolysis of safflower seed press cake

Cited by 18 publications

References 13 publications

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

A review on prospective production of biofuel from microalgae

Fatigue Reliability Analysis of Motor Hanger for High‐Speed Train Based on Bayesian Updating and Subset Simulation

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