(708f) Prediction of Tar Reaction Production Distribution from Gibbs Free Energy Minimization

Producing syngas and chemicals from biomass, plastic waste, and coal gasification consist of two reaction steps, that is, pyrolysis and gasification. The production distribution obtained from the secondary tar cracking reaction in the pyrolysis step is less studied; however, this product distribution is important to know in order to enhance the computational simulation accuracy for the gasification process. In this work, we predicted the equilibrium product gas distribution by using the classical Gibbs free energy minimization (minG) method. The minG method is especially useful to predict biomass gasification process^[1,2]. partly due to the two facts that complex multiple reactions occur in the tar carking process and there is no need to know these complex reactions when applying the minG method. Considering the second-step rate limiting gasification kinetics compared with the first-step pyrolysis reactions ^[3], it is expected that the equilibrium product distribution obtained from the minG calculations will provide useful information to optimize the gasification.

In this talk, we will present the thermodynamical properties for the products obtained from the secondary tar reaction, such as critical temperature (T_c), critical pressure (P_c), acentric factor (w), and the standard formation free energy (D_fG_m⁰). In addition, the product distributions obtained from the minG calculations will be shown. Many T_c, P_c, w, and D_fG_m⁰ values are not available in the literature and they are computed when necessary. The T_c, P_c, and w values are calculated from molecular dynamics simulations. The D_fG_m⁰ value is obtained from density-function theory (DFT) calculation. Two different methods are developed to predict D_fG_m⁰ by using the CBS-QB3 model calculations implemented in Gaussian software package. In one method, machine learning was used to predict the difference in standard reaction free energy between theoretical calculations and the experimental data. It was found that the simple multi-linear regression algorithm performs better than other algorithms partly due to the group contributions of CO₂, H₂O, and O₂ used in that method. The two DFT calculation methods give consistent results for standard reaction free energy values, and they also agree very well with the experimental data with a difference of 1 kcal/mol for all the test cases studied so far. The T_c, P_c, w, and D_fG_m⁰ values are used as inputs for the minG method, which is formalized by using the classical Peng-Robinson equation of state and classical thermodynamics under the constraint of mass balance. In the minG calculations, basin-hopping method for global optimization is integrated with various different local minimization methods in Scipy to avoid the minimization to stuck in local minima.

[1] dos Santos Junior, J.M.; Zelioli, Í.A.M.; Mariano, A.P. Eng 2023, 4, 1495–1515. https://doi.org/10.3390/eng4020086

[2] Ajorloo, M.; Ghodrat, M.; Scott, J.; Strezov, V. Journal of the Energy Institute 102 (2022) 395–419

[3] Dahou, T.; Defoort, F.; Khiari, B.; Labaki, M.; Dupont, C.; Jeguirim. M.; 2021, Role of inorganics on the biomass char gasification reactivity: A review involving reaction mechanisms and kinetics models - ScienceDirect