**1. Introduction**

Many chemical and process engineers are excited about the applications of Artificial Intelligence (AI) to their fields of expertise. AI can be defined as the ability of digital-computers to perform tasks at which people are better, at the moment [1]. In this context, Machine Learning (ML) is seen as one of the most relevant subareas, providing computers with the ability to learn without being programmed explicitly. It is essentially a form of applying statistics to estimate complex functions with less emphasis on obtaining the confidence intervals around them [2].

This current excitement was also stimulated by the Big Data boom related to the analysis and interpretation of large data volumes (of the order of several terabytes), which are generated at high rates and present various formats (numbers, text, multimedia, among others). Industry 4.0 requires this piece of knowledge from chemical and process engineers since process plants have large volumes of stored

historical data, obtained through sensors that measure thousands of variables in the order of seconds [3]. The analysis and exploitation of these data is a critical component for the operation of an industrial process.

In this framework, the so-called Artificial Neural Networks (ANNs) have numerous advantages and applications. They are universal nonlinear approximators based on the human brain-behavior through interconnected neurons that learn tasks from experience; in that case, from data [4]. Similarly to the nervous system, artificial neural networks are organized in the form of several simple individual elements – nodes or neurons – which interconnect with each other, forming networks capable of storing and transmitting information from/to the outside. Another relevant capacity of ANNs is their plasticity, which, through a learning process, allows changing in the interconnection pattern of its elements [5]. ANNs have been widely used in modeling or regression (linear and nonlinear) for one or several independent variables. It is worth noting that their use is not new in Chemical and Process Engineering, dating from the 1980s with some progress along the way, decisively contributing to the resurgence of the interest of the scientific community in this subject [3].

Modeling, simulation, and optimization are essential activities and competitive differentials among researchers to meet the challenges produced by environmental and commercial restrictions. In this context, ANNs are mostly used in process prediction and classification, as they are a robust nonlinear regression. In particular, this technique should be used when the solution of a problem is hampered by some of the following points: lack of physical or statistical understanding of the problem, statistical variations of the observable data, and the nonlinear mechanism responsible for the generation of the data [6].

In general terms, the use of neural networks consists of the following steps: 1- establishing the network architecture; 2- providing experimental data; 3- adjusting the network parameters – also known as their weights – until they learn the phenomenon (step called training); and 4- using the trained network with new input data for predicting the corresponding output data.

ANNs have been successfully applied to chemistry to correlate spectra of analytical methods and product properties [7]; in catalysis, to determine the relationships between the catalyst structure and its activity [8]; in process modeling, to predict product performance and operating conditions [9], and particularly in process control and fault diagnosis [10]. The main reasons for the growing popularity of the neural network approach are its lower computational cost compared to other methods and its ability to solve complex nonlinear problems [5]. Therefore, this review study demonstrates the increasing use of ANNs in Chemical and Process Engineering, helping to understand and to explore process data aspects for future research.
