An approach for interpreting thermogravimetric profiles using artificial intelligence

Voga, Geison P.; Belchior, Jadson C.

doi:10.1016/j.tca.2006.10.017

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…Beyond ANNs, some other artificial intelligence approaches have been sporadically applied to the TA problems and are worth mentioning. They include the expert analysis of thermogravimetric data [ 121 ], genetic approach [ 122 ] for determining kinetic parameters, modeling with adaptive neuro-fuzzy inference system [ 96 , 123 , 124 ] to predict the mass loss data, extreme gradient boosting algorithm [ 125 ] for product yield evaluation. A wider application of machine learning in these areas is expected as the computational tools become more readily available.…”

Section: Discussionmentioning

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: Discussionmentioning

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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“…This may be due to vagueness and complexity associated with many design parameters of berm breakwater. To minimize the cost, time and complexity in designing physical models, soft computing tools, such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Adaptive Neuro Fuzzy Inference System (ANFIS), etc., are successfully used in different fields (Kazperkiewiecz et al 1995, Voga and Belchior 2006, Dong et al 2005. Also in coastal fields some works have been carried out using soft computing tools.…”

Section: Introductionmentioning

confidence: 99%

Parameter Optimization Using GA in SVM to Predict Damage Level of Non-Reshaped Berm Breakwater

Narayana

Lokesha

Mandal

et al. 2014

The International Journal of Ocean and Climate Systems

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“…These soft computing tools have been used in different fields (Sarjakoski, 1988;Kazperkiewiecz, Raez and Dubrawski, 1995;Voga and Belchior, 2006;Dong, Cao and Lee, 2005;Kavaklioglu, 2011). Also in the coastal field some works have been carried out using soft computing tools.…”

Section: Introductionmentioning

confidence: 99%

Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models

Mandal¹,

Rao²,

Harish³

et al. 2012

International Journal of Naval Architecture and Ocean Engineeri

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An approach for interpreting thermogravimetric profiles using artificial intelligence

Cited by 4 publications

References 37 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

Parameter Optimization Using GA in SVM to Predict Damage Level of Non-Reshaped Berm Breakwater

Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models

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