Abstract
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.
Highlights
Machine learning has found a number of applications in chemistry [1,2,3] and material science [4,5,6]
Regarding the applications considered in the present review, they appear to be in an initiatory stage and use primarily the feedforward neural networks as a computational tool
Beyond Artificial neural networks (ANNs), some other artificial intelligence approaches have been sporadically applied to the Thermal analysis (TA) problems and are worth mentioning
Summary
Machine learning has found a number of applications in chemistry [1,2,3] and material science [4,5,6]. DSC produces a differential signal, i.e., the This data, assumption ignores the thermalto inertia term in the to heat that is a rate potential flow that is generally assumed be proportional theflow conversion dα/dt.source. To give a simple example of a black box model, let us imagine a neural network that is trained to output example of a black box model, let us imagine a neural network that is trained to output the conversion rate data once fed with temperature and time values. This ANN is readily the conversion rate data once fed with temperature and time values This ANN is readily applicable and does not require knowledge of the process kinetics. Analysis (TA) studies are indicated to be discussed in main text
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