## 2 Materials and Methods
### Pre-Processing Step  
Data pre-processing is a critical step to ensuring the quality and reliability of machine learning models. The initial step in the pre-processing pipeline was outlier analysis. Outliers, which represent extreme deviations from the normal range of data, can adversely affect model performance and distort statistical properties. The Z-score method was applied to detect outliers, with a threshold of z > 3 used for identification. Z-score is a statistical technique that measures how many standard deviations a data point is from the mean. The absolute value of the Z-score indicates how \"normal\" or \"outlier\" a data point is within the dataset [72]. The Z-score was calculated for each data point using Equation (1). \[z=\ \frac{x-\mu}{\sigma} \tag{1}\]  
where \(x\) denotes a data point, \(\mu\) is the mean, and \(\sigma\) is the standard deviation of the corresponding feature. The proportion of outlier samples in the datasets used in this study was less than 10%; therefore, these samples were removed to ensure a more representative and consistent dataset for modeling. Following outlier detection, normalization was performed to enhance the performance of machine learning algorithms. All features needed to be on the same scale to ensure more accurate results. Normalization was applied to constrain the data within a specific range (typically \([0,1]\) or \([-1,1]\)). This is especially important for distance-based algorithms, as such algorithms may produce erroneous results when dealing with features on different scales. Moreover, ensuring features have similar scales can facilitate faster learning and help mitigate model biases. In particular, when the data follow a normal distribution, model performance tends to be more stable [73]. In this study, the distributions of the variables in the dataset are illustrated using Violin Plots in Figure 2A. The distributions of the variables after normalization using standard scaling are shown in Figure 2B. As shown in the Violin Plots (Figure 2A), significant scale differences and irregularities exist among the variables in the original dataset. In particular, the temperature variable exhibits a wide range of values (ranging from 600 to 1100). In contrast, all other variables are distributed within narrower ranges, with the highest value being 83. This results in the temperature variable suppressing the effect of the other variables, leading to pronounced heterogeneity in the dataset. Such disparities can complicate the learning process in machine learning models, as the models may overemphasize variables with larger values while disregarding the contributions of other variables. Therefore, applying preprocessing techniques such as normalization or standardization is critical to eliminating the biases caused by large-scale differences. These methods ensure all variables are brought to similar scales, contributing to more balanced and effective model performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **Abbr.** & **Unit** & **Min** & **Max** & **Avg** & **Direction** \\ \hline Nitrogen & N & wt\% & 0.0 & 6.6 & 0.7 & Input \\ Sulfur & S & wt\% & 0.0 & 4.2 & 0.4 & Input \\ Temperature & Temp & \({}^{\circ}\)C & 599.0 & 1108.0 & 801.7 & Input \\ Equivalence ratio & ER & Weight basis & 0.0 & 0.5 & 0.2 & Input \\ Steam to biomass ratio & SB & Weight basis & 0.0 & 4.7 & 0.6 & Input \\ Carbon monoxide composition & CO & Vol\% & 7.4 & 50.6 & 31.0 & Output \\ Carbon dioxide composition & CO\({}_{2}\) & Vol\% & 5.0 & 59.0 & 28.9 & Output \\ Hydrogen composition & H\({}_{2}\) & Vol\% & 6.4 & 65.7 & 32.2 & Output \\ Methane composition & CH\({}_{4}\) & Vol\% & 0.4 & 22.0 & 8.0 & Output \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._The normality assumption of the dataset was assessed using the Shapiro-Wilk test. The results of the test indicated that the normality assumption could be rejected for all variables (Table 3).