About this Research Topic

Manuscript Submission Deadline 16 November 2022

Machine learning (ML) is being widely employed in physics, chemistry, and material science. Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being previously programmed. The field of machine learning is dedicated to comprehending and constructing learning methods to improve performance on specific sets of tasks. Methods within ML have already demonstrated their worth in solving various chemical engineering problems, but applications in pyrolysis, thermal analysis, and, especially, thermokinetic studies they are still in an initiatory stage. An exponential growth, increase of general demand and a promising future is expected in this field.

Machine learning has found several applications in chemistry and material science. It has already applied widely in daily life elsewhere such as in speech recognition, natural language processing, text to speech conversion. One of the most popular machine learning methods is the artificial neural networks (ANN). Artificial neural networks are models inspired by biological neural systems such as the human brain. ANN is composed of connected units called artificial neurons. The key features of the neural network are its topology (number of layers and neurons in it, and the strength of connections between the neurons (defined by mathematical weights). Among the benefits of ANN, are that it includes the storage of information on the entire network, ability to work with insufficient knowledge, good fault tolerance, distributed memory, gradual corruption, among others. Although a wide use of machine learning in the science field is observed in literature, its usage in thermal analysis commenced later, and most of the studies emerged only recently. The present Research Topic aims to summarize such studies and to assess some prospects.

This Research Topic seeks submissions including, but not limited to, the following themes:

• Predicting the output and efficiency of thermal systems by using different machine learning methods;
• Investigation of different systems based on interpolation or extrapolation of the data considering the temperature dependency of desired property;
• Applications of artificial intelligence methods in thermal engineering;
• Applications of machine learning approaches in prediction of thermal conductivity, vapor pressure, thermal stability, among others;
• Control temperature of different systems based to standard controllers;
• Applications of machine learning methods in the control systems applied in thermal systems;
• Prediction of the pyrolysis products in thermal decomposition processes;
• Estimation of the kinetic parameters and/or models in thermal processes;
• Thermal analysis of heat exchangers.

Keywords: machine learning, pyrolysis, thermal analysis, prediction, artificial intelligence


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Machine learning (ML) is being widely employed in physics, chemistry, and material science. Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being previously programmed. The field of machine learning is dedicated to comprehending and constructing learning methods to improve performance on specific sets of tasks. Methods within ML have already demonstrated their worth in solving various chemical engineering problems, but applications in pyrolysis, thermal analysis, and, especially, thermokinetic studies they are still in an initiatory stage. An exponential growth, increase of general demand and a promising future is expected in this field.

Machine learning has found several applications in chemistry and material science. It has already applied widely in daily life elsewhere such as in speech recognition, natural language processing, text to speech conversion. One of the most popular machine learning methods is the artificial neural networks (ANN). Artificial neural networks are models inspired by biological neural systems such as the human brain. ANN is composed of connected units called artificial neurons. The key features of the neural network are its topology (number of layers and neurons in it, and the strength of connections between the neurons (defined by mathematical weights). Among the benefits of ANN, are that it includes the storage of information on the entire network, ability to work with insufficient knowledge, good fault tolerance, distributed memory, gradual corruption, among others. Although a wide use of machine learning in the science field is observed in literature, its usage in thermal analysis commenced later, and most of the studies emerged only recently. The present Research Topic aims to summarize such studies and to assess some prospects.

This Research Topic seeks submissions including, but not limited to, the following themes:

• Predicting the output and efficiency of thermal systems by using different machine learning methods;
• Investigation of different systems based on interpolation or extrapolation of the data considering the temperature dependency of desired property;
• Applications of artificial intelligence methods in thermal engineering;
• Applications of machine learning approaches in prediction of thermal conductivity, vapor pressure, thermal stability, among others;
• Control temperature of different systems based to standard controllers;
• Applications of machine learning methods in the control systems applied in thermal systems;
• Prediction of the pyrolysis products in thermal decomposition processes;
• Estimation of the kinetic parameters and/or models in thermal processes;
• Thermal analysis of heat exchangers.

Keywords: machine learning, pyrolysis, thermal analysis, prediction, artificial intelligence


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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