**4. Future works**

Today, ANNs are one of the most found subjects in the scientific literature of Chemical and Process Engineering; and their use tends to continue growing. This can be explained by the launch of Industry 4.0, in which these data-driven models play an essential role in the implementation of some type of intelligent systems in processes [72]. Thus, to remain relevant in this current scenario, companies need specialized professionals on their team. For this reason, this topic has been introduced into the curriculum of most Chemical Engineering degree programs [73].

Indeed, the continuous availability of large volumes of stored data in industrial processes will lead to the development of new ANN approaches for process modeling and data interpretation. These models will deliver more direct relationships between cause and effect variables for process optimization and control through MPC strategies. Therefore, the automation of entire plant units will conduct to intelligent processes, capable of making decisions for safer operation, and with a reliable protection system against cyber-attacks.

Another subarea worth mentioning for future developments is the design of new materials. The use of ANNs has led to a decrease in the number of lengthy and

**179**

**Author details**

and Rita Maria de Brito Alves\*

*Application of Artificial Neural Networks to Chemical and Process Engineering*

costly laboratory experiments for analyzing the performance of polymers, ceramics, glasses, and mainly, catalysts. Therefore, it is possible to convert data from past publications and from high-throughput (HT) experiments into information, leading to a surprising acceleration in developing new materials with better perfor-

This chapter presented the ANNs and their Chemical and Process Engineering applications, showing how they have become a powerful tool for modeling chemical processes. This analysis also showed their increasing application, helping to understand and analyze process data features for future research in thermodynamics, transport phenomena, kinetics and catalysis, process analysis and optimization,

The prospective availability of large volumes of data with good quality will make

We gratefully acknowledge the support of the RCGI – Research Centre for Gas Innovation, hosted by the Universidade de São Paulo (USP) and sponsored by FAPESP – The São Paulo Research Foundation (2014/50279-4) and Shell Brasil. In addition, the authors acknowledge the financial support provided by FAPESP for

ANNs one of the most used methods to represent a process, estimate thermodynamic properties, develop new catalysts, replace complex phenomenological models, and improve control and safety strategies. Moreover, in real chemical processes, a particular part of the inputs affect only a section of the outputs. Therefore, the knowledge of first principles embedded in a data driven machine learning model is

Fabio Machado Cavalcanti, Camila Emilia Kozonoe, Kelvin André Pacheco

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

Escola Politécnica - Universidade de São Paulo, São Paulo, Brazil

doctoral scholarships (Grant 2017/11940-5 and 2017/26683-8).

\*Address all correspondence to: rmbalves@usp.br

provided the original work is properly cited.

The authors declare no conflict of interest.

*DOI: http://dx.doi.org/10.5772/intechopen.96641*

mances for a given process.

**5. Conclusions and perspectives**

and process safety and control.

a challenge for the next studies.

**Acknowledgements**

**Conflict of interest**

*Application of Artificial Neural Networks to Chemical and Process Engineering DOI: http://dx.doi.org/10.5772/intechopen.96641*

costly laboratory experiments for analyzing the performance of polymers, ceramics, glasses, and mainly, catalysts. Therefore, it is possible to convert data from past publications and from high-throughput (HT) experiments into information, leading to a surprising acceleration in developing new materials with better performances for a given process.
