## 1 Introduction
To perform an accurate simulation, fluid dynamics (CFD) models require a high computational load [29]. Therefore, a mathematical model is required to design and optimize the thermal conversion process reliably and accurately, given its complexity, the involvement of nonlinear parameters, and the high-dimensional conversion processes [30]. To address these disadvantages, Machine Learning (ML) is used, which is a subclass of artificial intelligence (AI), a technology that allows a machine to learn and is used in the optimization of thermochemical conversion processes and the prediction of their efficiency [31]. Advantages of ML-based models include that they do not require extensive knowledge and information about the process, but they only require a dataset of experimental measurements [32]. In solid yield prediction, real-time monitoring, and control, ML has recently been shown to be a solution for thermochemical conversion processes. Among the most common ML algorithms in the literature on optimization and prediction for thermochemical analysis, elemental analysis, and high calorific value problems, the following stand out: least squares regression (LSR) [33], Sum of Squared Error (SSE) [34], Nelder-Mead [35; 36; 37], CFD simulation [27], and particle swarm optimization (PSO) [13; 38; 39]. A powerful advantage of these algorithms is the ability to make predictions about global and individual Arrhenius parameters. The first algorithm consists of obtaining a set of Arrhenius parameters for each study temperature, while the second one obtains a set of Arrhenius parameters from a set of temperatures. In both cases, the experimental data are adjusted using a two-step kinetic model during a non-isothermal torrefaction process [40]. In this research, the main goal is to determine the impact of studying Arrhenius constants under the individual and global parameters scheme for elemental analysis, HHVs, atomic O/C and H/C ratios, and energy density in the wheat straw torrefaction process. In [33], the LSR method is used to predict a set of global Arrhenius parameters over a temperature range of 200-300 \({}^{\circ}\)C for different types of biomass, adjusting 31 experimental data points. In [17], the SSE algorithm performs individual parameter adjustment with 25 experimental data points in a temperature range of 220-300 \({}^{\circ}\)C for spruce and birch biomass. The Nelder-Mead algorithm is the most common kinetic parameter adjustment algorithm. It is used in [10] to predict a set of global Arrhenius parameters in a temperature range of 210-380 \({}^{\circ}\)C for juniper, adjusting nine experimental data points with a two-step kinetic model with an error of less than 2%. In [19], the Nelder-Mead algorithm is used to perform global parameter adjustment with 15 experimental data points in a temperature range of 210-290 \({}^{\circ}\)C in _Eucalyptus grandi_; in [14; 21], the Nelder-Mead algorithm is used to perform a global parameter adjustment for poplar and fir with 30 experimental data points in a temperature range of 200-230 \({}^{\circ}\)C; and in [41], the Nelder-Mead algorithm is used with the Matlab function _lsqcurvefit_ to perform individual parameter adjustment with a set of 48 experimental data points. In [42], a study of global and individual parameter adjustment for poplar and xylan is carried out in a temperature range of 200-240 \({}^{\circ}\)C using the Nelder-Mead algorithm with a set of 24 experimental points. In [8], individual parameter adjustment is performed for wood with 48 experimental data points in a temperature range of 200-300 \({}^{\circ}\)C using CFD software solved with the fourth-order Runge-Kutta method implemented in FORTRAN. The PSO algorithm is implemented in [13; 40] to perform an individual parameter adjustment of 28 and 80 experimental data points for each study of xylan and sorghum distillation residues in a temperature range of 200-300 \({}^{\circ}\)C in both cases.