## 2 Materials and Method  
### Initial Hypothesis  
Green hydrogen is produced by electrolysis of water with no carbon emission. This process uses electricity to split water into hydrogen and oxygen. The electricity comes from renewable sources such as wind turbines, solar panels or hydropower [19]. PEM water electrolysis offers an efficient and flexible way to produce \"green hydrogen\" from renewable (intermittent) energy sources [19; 20]. The schematics of a PEM electrolytic cell are shown in Figure 2.  
Cell reactions:  
\[\text{Anode}:\ 2\text{H}_{2}\text{O}\to 4\text{H}^{+}+4\text{e}^{-}+\text{O}_{2} \tag{1}\]  
\[\text{Cathode}:\ 2\text{H}^{+}+2\text{e}^{-}\to\text{H}_{2} \tag{2}\]  
\[\text{Overall}:\ 2\text{H}_{2}\text{O}\to\text{O}_{2}+2\text{H}_{2} \tag{3}\]  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Scientific Studies** & **Key Contributions** & **Data Selection and Evaluation Method** & **Artificial Intelligence Concept Used for Predictive Maintenance of Electrolyser** \\ \hline [PERSON] et al. [16] & Application of artificial neural networks (ANN) in terms of modelling and simulating the aging process. & n/a & ANN with Stochastic gradient descent (SGD) is used to find the optimal weight parameters for the desired relationship between input and outputs. Inputs to the neural network are stack current, temperature, and time while the output is the voltage. \\ [PERSON] et al. [17] & Data-driven digital-twin model to develop a dynamic model for predicting power consumption, hydrogen production, and temperature. & n/a & Fuzzy logic and neural network are implemented to improve the durability of the electrolyser. \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: Schematics of a PEM electrolytic cell.  
The equations governing the production of hydrogen are shown in (1) to (5).  
\[\Delta G=\Delta H-T\Delta S \tag{4}\]  
\[\dot{n}_{H2}=\frac{\eta_{f}n_{cel}\dot{i}_{ely}}{zF} \tag{5}\]  
where:  
\(\Delta G\) is the Gibbs free energy exchange (J).  
\(\Delta H\) is the enthalpy change (J).  
\(T\) is the reaction temperature (K).  
\(\Delta S\) is the entropy change (JK\({}^{-1}\)).  
\(\dot{n}_{H2}\) is the molar flow rate of hydrogen (mol/h).  
\(\eta_{f}\) is the Faraday efficiency which is affected by temperature.  
\(\eta_{cel}\) is the number of cells.  
\(i_{ely}\) is the current through the electrolytic cell (A).  
\(z\) is the number of electrons in reduction reaction (2).  
\(F\) is the Faraday constant (26.81 Ah/mol).  
One of the problems that affect reliability of a PEM electrolyser is membrane drying [9]. This is a function that is expressed in (6):  
\[Drying\ \ effect\ of\ membrane=f(T,\ R,\ F_{w},\ V,\ j) \tag{6}\]  
where:  
\(T\) is the operating temperature (\({}^{\circ}\)C).  
\(R\) is the membrane resistance (\(\Omega\)).  
\(V\) is the voltage depending on the amount of current density (V).  
\(F_{w}\) is the condition of the feed water.  
\(j\) is the current density (A/cm\({}^{2}\)).  
In the study performed by [PERSON] et al. [8], experimental results show that most of the membrane degradation occurs at the cathode side and also show the strong influence of the temperature on the degradation rate. In the study, the cell temperature is assumed uniform; hence, the temperature of hydrogen at the outlet of the cathode can be taken as the electrolyser temperature.  
Hence, considering the importance of temperature in the degradation of electrolysers, in this paper, the authors use hydrogen temperature as the variable to be predicted for predictive maintenance in an electrolyser.