## 1 Introduction
An interesting proposal is that of [PERSON] [34], he applied a three-layer neural network to analyze the relationship between the input and output of a PSA plant. In ref. [35], an NN was used to predict and optimize the performance of the PSA system in hydrogen purification. First, a data set was generated to train the NN. Then, the effects of the hidden layers of neurons and training samples were analyzed. Finally, an NN with six neurons in the hidden layer was applied to predict the performance of the PSA system. In [PERSON] and [PERSON] (2016), they studied the PSA plant to optimize the capture of CO\({}_{2}\) using a deep neural network to predict the main process indicators. As mentioned in the examples above, machine learning has been a key tool for many chemical processes. Every day, machine learning changes and modifies; we have neural networks, recurrent networks, and deep neural networks. In this work, a feedforward neural network is utilized to design the controller. This type of network is used to model, control, or optimize processes such as the PSA plant for gas separation ([PERSON] et al., 2017; [PERSON] et al., 2017). \begin{tabular}{l l l} \hline \hline
**Nomenclature** & **Description** & **Units** \\ \hline \multicolumn{3}{c}{**Geometrical and Physical Properties**} \\ \hline \(a_{p}\) & Specific surface area of particles & m\({}^{2}\) m\({}^{-3}\) \\ \(r_{p}\) & Radius of an adsorbent particle & m \\ \(\Psi\) & Shape factor for the adsorbent particles & – \\ \(\epsilon_{i}\) & Interparticle void fraction (porosity) & – \\ \(\epsilon_{p}\) & Intraparticle porosity & – \\ \(\rho\) & Molar density of the gas phase & kmol m\({}^{-3}\) \\ \hline \multicolumn{3}{c}{**Transport and Transfer Properties**} \\ \hline \(D_{mi}\) & Molecular diffusion coefficient & m\({}^{2}\) s\({}^{-1}\) \\ \(D_{el}\) & Effective diffusivity within adsorbed phase & m\({}^{2}\) s\({}^{-1}\) \\ \(E_{zi}\) & Axial dispersion coefficient & m\({}^{2}\) s\({}^{-1}\) \\ \(I_{i}\) & Mass transfer flux & kmol m\({}^{-3}\) s\({}^{-1}\) \\ MTC\({}_{s}\) & Solid-phase mass transfer coefficient & s\({}^{-1}\) \\ \(H_{s}\) & Heat transfer coefficient & J s\({}^{-1}\) m\({}^{-2}\) K\({}^{-1}\) \\ \(k_{sa}\) & Axial thermal conductivity & W m\({}^{-1}\) K\({}^{-1}\) \\ \hline \multicolumn{3}{c}{**Thermodynamic and Adsorption Parameters**} \\ \hline \(K\) & Langmuir adsorption equilibrium constant & Pa\({}^{-1}\) \\ \(Q\) & Isosteric heat of adsorption & J mol\({}^{-1}\) \\ \(W_{i}\) & Adsorbed amount per unit mass of adsorbent & kmol kg\({}^{-1}\) \\ \(W_{i}^{\prime}\) & Equilibrium adsorbed amount per unit mass & kmol kg\({}^{-1}\) \\ \(IP_{1i},IP_{2i},IP_{3i},IP_{4i}\) & Isothermal adsorption parameters & – \\ \(\Omega\) & Glueckauf adsorption parameter & – \\ \hline \multicolumn{3}{c}{**Heat and Energy Properties**} \\ \hline \(C_{ps}\) & Heat capacity of adsorbent & MJ kmol\({}^{-1}\) K\({}^{-1}\) \\ \(C_{pai}\) & Heat capacity of the gas phase & MJ kg\({}^{-1}\) K\({}^{-1}\) \\ \(R\) & Universal gas constant & J mol\({}^{-1}\) K\({}^{-1}\) \\ \hline \multicolumn{3}{c}{**Process and Operating Conditions**} \\ \hline \(F\) & Flow rate of the gas phase & kmol h\({}^{-1}\) \\ \(P\) & System pressure & Pa \\ \(T\) & System temperature & K \\ \(T_{g}\) & Gas-phase temperature & K \\ \(T_{s}\) & Solid-phase temperature & K \\ \(t\) & Time variable & s \\ \(v_{g}\) & Superficial gas velocity & m s\({}^{-1}\) \\ \(z\) & Axial coordinate & m \\ \(y_{i}\) & Molar fraction of component \(i\) in the gas phase & – \\ \hline \multicolumn{3}{c}{**Additional Parameters**} \\ \hline \(i\) & Component index (e.g., water or ethanol) & – \\ M & Molecular weight of a species & kg mol\({}^{-1}\) \\ \hline \hline \end{tabular}   
The key contribution of this article is the development of a neural network (NN) capable of effectively controlling a complex system, such as the PSA process, for biohydrogen production.