Fischer–Tropsch synthesis (FTS) facilitates the conversion of syngas, derived from feedstocks such as biomass, coal, and natural gas, into valuable hydrocarbons (HCs). This investigation employed optimization methods, specifically Gibbs energy minimization, to perform a thermodynamic characterization of the low-temperature Fischer–Tropsch (LTFT) reaction for HC generation. The CONOPT3 solver within GAMS 23.2.1 software was utilized for solving the developed model. To represent the complex FTS product spectrum, twenty-three compounds, encompassing C₂–C₂₀ aliphatic hydrocarbons, were considered using a stoichiometric framework. The study explored the impact of operational parameters, including temperature (350–550 K), pressure (5–30 bar), and H₂/CO molar feed ratio (1.0–2.0/0.5–1.0), on hydrocarbon synthesis. Evaluation of the outcomes focused on HC yield and product characteristics. A significant sensitivity of the reaction to operating parameters was observed. Notably, lower temperatures, elevated pressures, and a H₂/CO ratio of 2.0/1.0 were identified as optimal for fostering the formation of longer-chain HCs. The developed model demonstrated robustness and efficiency, with rapid computation times across all simulations.

Thermodynamic equilibrium modeling is a useful approach for predicting gas production during supercritical water gasification (SCWG) of wet biomass; however, Gibbs energy minimization can be computationally demanding for repeated process simulations. This study developed an artificial neural network (ANN) surrogate model to predict the equilibrium yields of H₂, CO, CO₂, and CH₄ during SCWG of açaí residual biomass from Maranhão, Brazil. Four biomass samples collected under different geographical and seasonal conditions were characterized by ultimate analysis and used to generate a thermodynamic database with 1000 simulations per sample using the TeS-Thermo PyPI Python library (0.1.11). The ANN comprised 10 input variables, two hidden layers with 12 and 6 neurons, and 4 output neurons, and was trained using the Levenberg–Marquardt algorithm. The selected ANN achieved R² values of 0.99995, 0.99563, 0.99960, and 0.99971 for H₂, CO, CO₂, and CH₄, respectively. External validation using an independent biomass composition provided evidence of the ANN’s generalization capability within the investigated compositional domain, with R² values above 0.99244 for all four species. Sensitivity analyses further demonstrated that the ANN reproduced the effects of temperature (700–1100 K), water-to-biomass molar ratio (3–6 mol mol⁻¹), and pressure (230–260 bar), with low deviations from TeS-Thermo predictions. These results demonstrate that the ANN provides an accurate and computationally efficient surrogate model for rapid thermodynamic screening, sensitivity analysis and process optimization of SCWG biomass processes.

Thermodynamic equilibrium modeling is a useful approach for predicting gas production during supercritical water gasification (SCWG) of wet biomass; however, Gibbs energy minimization can be computationally demanding for repeated process simulations. This study developed an artificial neural network (ANN) surrogate model to predict the equilibrium yields of H₂, CO, CO₂, and CH₄ during SCWG of açaí residual biomass from Maranhão, Brazil. Four biomass samples collected under different geographical and seasonal conditions were characterized by ultimate analysis and used to generate a thermodynamic database with 1,000 simulations per sample using the TeS-Thermo PyPI Python library. The ANN comprised 10 input variables, two hidden layers with 12 and 6 neurons, and 4 output neurons, and was trained using the Levenberg–Marquardt algo-rithm. The selected ANN achieved R² values of 0.99989, 0.99553, 0.99957, and 0.99963 for H₂, CO, CO₂, and CH₄, respectively. External validation using an independent biomass composition provided evidence of the ANN's generalization capability within the in-vestigated compositional domain, with R² values above 0.98 for all four species. Sensi-tivity analyses further demonstrated that the ANN reproduced the effects of temperature (700–1100 K), water-to-biomass molar ratio (3–6 mol mol⁻¹), and pressure (230–260 bar), with low deviations from TeS-Thermo predictions. These results demonstrate that the ANN provides an accurate and computationally efficient surrogate model for rapid thermo-dynamic screening, sensitivity analysis and process optimization of SCWG biomass processes.