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.

CNR Istituto di Scienze e Tecnologie Chimiche Giulio Natta Via De Marini 6 16149 Genova Italy

Department of Chemistry University of Alabama at Birmingham 901 S 14th Street Birmingham AL 35294 USA

Department of Materials Federal University for Latin American Integration Silvio Américo Sasdelli Avenue 1842 Foz do Iguaçu Paraná 85866 000 Brazil

Department of Physical Chemistry Faculty of Chemical Technology University of Pardubice Studentská 95 CZ 53210 Pardubice Czech Republic

N N Semenov Federal Research Center for Chemical Physics Russian Academy of Sciences 4 Kosygina Str 119991 Moscow Russia

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