**5. Conclusions and perspectives**

*Deep Learning Applications*

[67] Fault

[68] Soft

[69] Surrogate

[70] Surrogate

[14] Hybrid

[71] Cyber

Detection

Sensors

model in MPC and RTO

> model from CFD in MPC

model in a MPC

Security

*\*\*RNN stands for Recurrent Neural Network. \*\*\*LSTM stands for Long Short-Term Memory.*

*FN-ANN stands for Feed-Forward Artificial Neural Network.*

*Current applications of ANNs to process safety and control.*

*respectively. Among them, the number of neurons in the hidden layer(s).*

**References Field Case Study Class of** 

Penicillin fermentation process

pH control in a chemical process

Reaction process in a CSTR

Phthalic anhydride synthesis in a fixed-bed catalytic reactor

Twoconsecutive CSTRs

MPC integrated with cyber-secure feedback controller

**Neural Network** **Activation Function**

LSTM\*\*\* Sigmoid 10–20–15-2 Matlab

RNN\*\* Tanh 5–14–1 Not

FF-ANN\* Tanh 3–10-1 Matlab

RNN ReLu 3–64–64–1 Keras

RNN Tanh 2–30–30-4 IPOPT-

FF-ANN Tanh 4–12–10-9 Matlab

**Topology\*\*\*\* Software**

described

Python

**178**

**4. Future works**

*\**

**Table 4.**

reliable protection system against cyber-attacks.

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

*\*\*\*\*The first and last elements in topology represent the number of neurons in the input and in the output layer,* 

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

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, and process safety and control.

The prospective availability of large volumes of data with good quality will make 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 a challenge for the next studies.
