## 1 Introduction
Stoichiometric models are often modified to increase their accuracy. The modified parameters are usually the concentration of unburned tar and the reaction equilibrium constant. [PERSON] et al. [13] modified the stoichiometric model by estimating the mole number of tar as a function of ER. [PERSON] et al. [14] modified the reaction equilibrium constant in a stoichiometric model, whereas [PERSON] et al. [15] modified both the mole number of unburned tar and the reaction equilibrium constant in the model. The equilibrium constant is usually corrected by methanation reactions, methane reforming, and water gas shift reactions. Correction factors for the equilibrium constants are usually calculated from secondary data (published experimental data). However, the available modified stoichiometric models still cannot accurately predict the concentrations of methane and hydrogen produced from gasification. There are several factors that may lead to the inaccuracy of stoichiometric models up to date, i.e., the models (i) do not include the prediction of tar content, (ii) do not consider factors that contribute to the accurate prediction of methane and hydrogen levels, and (iii) do not use steam reforming reaction constants. Tar prediction is important because it is troublesome for producer gas performance, therefore, its inclusion in the models is necessary. A few models include a catalyst; however, it is used to simulate the reduction of tar in the producer gas [16; 17], e.g., dolomite, which consists of 30% \(w/w\) CaO, 45% \(w/w\) CO, and 15% \(w/w\) MgO, and nickel [17; 18]. Therefore, a catalyst is not included in this study because this study aimed to predict the amount of tar in the producer gas by utilizing a steam reforming reaction. Experimental studies on biomass gasification are complex, costly, and time-consuming. The complexity of reactions within gasification also leads to challenging gasification modeling and simulation. Model precision and reliability are important to explain gasification under real conditions. The stoichiometric models developed in this study improve some deficiencies of other models that have been modified so far. This research developed stoichiometric models by introducing correction factors for the reaction equilibrium constants. The novelties of this research are the inclusion of the steam reaction and valid and accurate models that predict the concentration of H\({}_{2}\) and tar content. This research is highly relevant to the forecasting of syngas as a fuel for internal engines in the future, which potentially reduces conventional fuel expenses by 44% [4]. However, it is crucial to ensure that the tar concentration of the syngas does not exceed 100 g/Nm\({}^{3}\)[19]. Therefore, the models developed in this study will be applicable for simulating tar concentration obtained from gasification. The modeling in this study was performed in two stages, using the ANN model-thinking approach as the calculation algorithm. The first stage involved calculating the correction factors and the new function for the reaction equilibrium constants using 47 secondary datasets. The second stage evaluated the validation and accuracy of the models against 24 secondary datasets. There are two novelties introduced by this study, i.e., (i) new stoichiometric models with variables N/C, O/C, H/C, ER, and T to simulate the composition of CO, CO\({}_{2}\), CH\({}_{4}\), H\({}_{2}\), N\({}_{2}\), and LHV-gas as well as tar concentration; (ii) the correction factors and the new empirical correlation for reaction equilibrium constants in the stoichiometric models using steam reforming.