## 1 Introduction  
Currently, energy consumption is still based on fossil fuels (coal, natural gas, and oil) [1]. Increased use of fossil fuels leads to increased pollution, greenhouse gas emissions, and other particulate pollutants. Fossil fuels are \"non-renewable energy sources\". New renewable energy sources are key to replace fossil fuel sources. Hydropower (74.1%), biofuels (12.6%), geothermal energy (11.5%), solar power (1.2%), and wind power (0.6%) are considered as renewable energy sources. Another renewable energy source that is currently massively available is biomass [2]. Thermochemical processes are used to generate energy from the biomass. Various thermochemical methods convert post-consumer waste into clean energy [3]. Gasification is one of the thermochemical processes that produce renewable energy. Gasification of biomass synthesis gas is suitable for (i) temperature internal combustion engines; (ii) biomass synthesis gasification for fuel cells; (iii) methanol and biohydrogen production; and (iv) Fischer-Tropsch process technology to produce synthetic fuels for gasoline and diesel engines. However, power decrease (de-rating) and tar concentration have become challenges in syngas production [4]. Therefore, more studies on gasification are required to produce CO, CH\({}_{4}\), and H\({}_{2}\) while minimizing its side-effects (de-rating and tar production). The high cost and difficulty of experimental gasification research have motivated numerical simulation studies in gasification research [5]. The numerical simulation models that have been developed were thermodynamic equilibrium models, kinetic models, Aspen Plus, artificial neural networks, and computational fluid dynamics (CFD) [6]. Approximately 66% of the numerical simulation studies use thermodynamic equilibrium techniques [7]. There are several factors to consider when developing thermodynamic equilibrium models of gasification, i.e., (1) calculating the optimal gas production, (2) adaptability to different types of biomass, (3) the ability to change the reaction equilibrium constants, and (4) the influence of the reactor types [8]. There are two types of thermodynamic equilibrium models, i.e., stoichiometric and non-stoichiometric models. Methods of mass balance equilibrium constant and reaction equilibrium constant are used to calculate composition of the syngas in stoichiometric models [9]. The Gibbs free energy minimization approach is used to predict the composition of the syngas in non-stoichiometric models, with the mass balance of carbon, oxygen, and hydrogen as constraints [7]. The thermodynamic equilibrium models mostly result in good simulation results. However, the equilibrium conditions usually do not correspond to the conditions in the gasifier, therefore, the model reliability is questionable [10]. The stoichiometric models can be modified to improve their reliability and accuracy, but the modification might be suitable in stoichiometric models. The inclusion of correction factors for the reaction equilibrium constants makes the modification of the stoichiometric models possible. On the other hand, non-stoichiometric modes usually apply Lagrange equations that use carbon, hydrogen, oxygen, and nitrogen mass balances as constraints. Therefore, the modification of non-stoichiometric models is limited. [PERSON] et al. [11] used a stoichiometric model to study the effect of biomass operational parameters equivalent ratio (ER), temperature, and moisture content (MC) on gasification in a downdraft gasifier. The model simulated syngas composition, tar, char, temperature, cold gasification efficiency (CGE), and low heating value (LHV). [PERSON] et al. [5] developed a semi-empirical model based on a stoichiometric model for woody biomass gasification. The study also used empirical models to predict tar and methane gas. [PERSON] and [PERSON] [12] simulated the co-gasification of biomass and plastic to produce hydrogen gas. However, the model cannot accurately estimate the yields of methane and hydrogen gases.