Antonio Carlos Daltro de Freitas

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.

This work aims to present tes-thermo, a Python library developed to solve thermodynamic equilibrium problems using the Gibbs energy minimization approach. The library is a variant of TeS v.3, a standalone executable developed for the same purpose. The tool formulates the chemical equilibrium problem of combined phases as a nonlinear programming problem, implemented using Pyomo (Python Optimization Modeling Objects) and solved with IPOPT (Interior Point OPTimizer). To validate the tool and demonstrate its robustness, the supercritical water gasification (SCWG) of methanol and ethanol was investigated. The Peng–Robinson equation of state was employed to account for non-idealities in the gas phase. Experimental and simulated data from the literature were used for validation, and, in both cases, the results were satisfactory, with root mean square errors consistently below 0.23. The SCWG processes studied revealed that hydrogen production is favored by increasing temperature and decreasing pressure. For both methanol and ethanol, increasing the carbonaceous substrate fraction in the feed promotes hydrogen formation; however, it also leads to reduced hydrogen relative yield due to the enhanced formation of methane and carbon monoxide under these conditions. Consequently, although hydrogen production increases, the hydrogen molar fraction in the dry gas stream tends to decrease with the higher substrate content. As expected, the SCWG of methanol produces more hydrogen and less carbon monoxide compared to ethanol under similar conditions. This behavior is consistent with the higher carbon content in ethanol, which favors reactions leading to carbon oxides. In summary, tes-thermo proves to be a robust and reliable tool for conducting research and studies on topics related to thermodynamic equilibrium.

This study presents TeS v.3, a thermodynamic equilibrium simulator integrated with an artificial intelligence agent (AI), ThermoAgent, to enhance the analysis of complex chemical systems. Developed in Python, the simulator employs Gibbs energy minimization for isothermal reactors and entropy maximization for adiabatic reactors. ThermoAgent leverages the LangChain framework to interpret natural language commands, autonomously execute simulations, and query a scientific knowledge base through a Retrieval-Augmented Generation (RAG) approach. The validation of TeS v.3 demonstrated high accuracy, with coefficients of determination (R² > 0.95) compared to reference simulation data and strong correlation (R² > 0.88) with experimental data from the steam methane reforming (SMR) process. The SMR analysis correctly distinguished the high conversions in isothermal reactors from the limited conversions in adiabatic reactors, due to the reaction temperature drop. ThermoAgent successfully executed simulations and provided justified analyses, combining generated data with information from reference publications. The successful integration of the simulator with the AI agent represents a significant advancement, offering a powerful tool that accurately calculates equilibrium and accelerates knowledge extraction through intuitive interaction.

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.

O presente trabalho objetiva determinar as condições ótimas para produção de hidrogênio através da reação de reforma a vapor do metano com o uso do software TeS (Thermodynamic Equilibrium Simulation) na forma de um problema de minimização da energia de Gibbs. Para tanto, os resultados calculados neste trabalho foram comparados com resultados simulados e dados experimentais reportados da literatura em condições operacionais semelhantes. Concluiu-se que os resultados simulados no TeS são consistentes, confiáveis e válidos, podendo ser empregados na análise termodinâmica de sistemas reacionais complexos do tipo gás-vapor, como o da reforma a vapor de metano.

Pharmaceutical pollutants are considered emerging contaminants, representing a significant concern to the ecosystem. Thus, this study reports on the degradation of antihypertensive and cardiovascular drugs (atenolol, captopril, propranolol hydrochloride, diosmin, hesperidin, losartan potassium, hydrochlorothiazide, and trimetazidine) present in simulated wastewater through applying the technology of oxidation using supercritical water (SCW). The operational parameters of the treatment process, particularly the feed flow rate, temperature, and concentration of H₂O₂, were assessed. A central composite design of experiments associated with differential evolution was employed in the optimization. Both liquid and gaseous phase products were submitted to physical–chemical characterization. As a result, the optimized conditions for the treatment were discovered to be a feed flow rate of 13.3 mL/min, a temperature of 600 °C, and a H₂O₂ oxidation coefficient of 0.65, corresponding to the oxygen stoichiometric coefficient in the carbon oxidation chemical reaction. Under optimal conditions, the total organic carbon (TOC) decreased from 332 to 25 mg/L (92.1%), and the pharmaceutical molecules underwent near-complete degradation. The physical–chemical parameters also met with the main environmental regulations for wastewater disposal. The compounds determined in the gaseous phase were CO₂ (97.9%), H₂ (1.3%), CH₄ (0.3%), and CO (0.5%.). Additionally, a modeling thermodynamic equilibrium of the system was performed, based on the experimental data. The results revealed that SCW technology has a great potential to oxidize/degrade organic matter and can be applied to treat pharmaceutical pollutants.

Teaching and research laboratories generate wastes of various compositions and volumes, ranging from diluted aqueous solutions to concentrated ones, which, due to milder self-regulation waste-management policies, are carelessly discarded, with little attention given to the consequences for the environment and human health. In this sense, the current study proposes the application of the supercritical water oxidation (SCWO) process for the treatment of complex refractory wastewater generated in research and teaching laboratories of universities. The SCWO, which uses water in conditions above its critical point (T > 647.1 K, p > 22.1 MPa), is regarded as an environmentally neutral process, uniquely adequate for the degradation of highly toxic and bio-refractory organic compounds. Initially, the wastewater samples were characterized via headspace gas chromatography coupled with mass spectrometry. Then, using a continuous tubular reactor, the selected operational parameters were optimized by a Taguchi L9 experimental design, aiming to maximize the total organic carbon reduction. Under optimized conditions—that is, temperature of 823.15 K, feed flow rate of 10 mL min⁻¹, oxidizing ratio of 1.5 (50% excess over the oxygen stoichiometric ratio), and sample concentration of 30%—TOC, COD, and BOD reductions of 99.9%. 91.5% and 99.2% were achieved, respectively. During the treatment process, only CO₂, methane, and hydrogen were identified in the gaseous phase. Furthermore, the developed methodology was applied for the treatment of wastewater samples generated in another research laboratory and a TOC reduction of 99.5% was achieved, reinforcing the process’s robustness. A thermodynamic analysis of SCWO treatment of laboratory wastewater under isothermal conditions was performed, using the Gibbs energy minimization methodology with the aid of the GAMS^® 23.9.5. (General Algebraic Modeling System) software and the CONOPT 4 solver. Therefore, the results showed that SCWO could be efficiently applied for the treatment of wastewater generated by different teaching and research laboratories without the production of harmful gases and the addition of hazardous chemicals.

In this paper, we analyze the autothermal reforming (ATR) of methane through Gibbs energy minimization and entropy maximization methods to analyze isothermic and adiabatic systems, respectively. The software GAMS^® 23.9 and the CONOPT3 solver were used to conduct the simulations and thermodynamic analyses in order to determine the equilibrium compositions and equilibrium temperatures of this system. Simulations were performed covering different pressures in the range of 1 to 10 atm, temperatures between 873 and 1073 K, steam/methane ratio was varied in the range of 1.0/1.0 and 2.0/1.0 and oxygen/methane ratios in the feed stream, in the range of 0.5/1.0 to 2.0/1.0. The effect of using pure oxygen or air as oxidizer agent to perform the reaction was also studied. The simulations were carried out in order to maintain the same molar proportions of oxygen as in the simulated cases considering pure oxygen in the reactor feed. The results showed that the formation of hydrogen and synthesis gas increased with temperature, average composition of 71.9% and 56.0% using air and O₂, respectively. These results are observed at low molar oxygen ratios (O₂/CH₄ = 0.5) in the feed. Higher pressures reduced the production of hydrogen and synthesis gas produced during ATR of methane. In general, reductions on the order of 19.7% using O₂ and 14.0% using air were observed. It was also verified that the process has autothermicity in all conditions tested and the use of air in relation to pure oxygen favored the compounds of interest, mainly in conditions of higher pressure (10 atm). The mean reductions with increasing temperature in the percentage increase of H₂ and syngas using air under 1.5 and 10 atm, at the different O₂/CH₄ ratios, were 5.3%, 13.8% and 16.5%, respectively. In the same order, these values with the increase of oxygen were 3.6%, 6.4% and 9.1%. The better conditions for the reaction include high temperatures, low pressures and low O₂/CH₄ ratios, a region in which there is no swelling in terms of the oxygen source used. In addition, with the introduction of air, the final temperature of the system was reduced by 5%, which can help to reduce the negative impacts of high temperatures in reactors during ATR reactions.

The thermal cracking process of methane does not present the emissions of polluting gases, forming only hydrogen with a high degree of purity and solid carbon that can be commercialized for other industrial purposes globally. Thermodynamic methodologies based on Gibbs energy minimization and entropy maximization are used in the present study to simulate operating conditions of isothermal and adiabatic reactors, respectively. The chemical equilibrium and combined phases problem were written in a non-linear programming form and optimized with the GAMS software using the CONOPT 3 solver. The results obtained by the methodology described in this study present a good agreement with the data reported in the literature, with mean relative deviations lower than 1.08%. High temperatures and low pressures favor the decomposition of methane and the formation of products. When conditioned in an isothermal reactor, total methane conversions are obtained at temperatures above 1200 K at 1 bar. When conditioned to an adiabatic reactor, due to the lack of energy support provided by the isothermal reactor and taking into account that it is an endothermic process, high methane-conversion rates are obtained for temperatures above 1600 K at 1 bar. As an alternative, the combined effects of the addition of hydrogen to the feed combined with a system of extreme pressure variation indicate a possibility of conducting the thermal cracking process of methane in adiabatic systems. Setting the CH₄/H₂ ratio in the system feed at 1:10 at 1600 K and 50 bar, following severe depressurization through an isentropic valve, varying the pressure from 50 to 1 bar, the methane conversion varies from 0 to 94.712%, thus indicating a possible operational conformation for the process so that the amount of carbon generated is not so harmful to the process, taking into account that the formation of the same occurs only after the reaction and heating processes. Under the same operating conditions, it is possible to use about 40.57% of the generated hydrogen to provide energy for the process to occur.