Reginaldo Guirardello

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.

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.

The search for alternative sources of, and substitutes for, chemicals derived from fossil-based feedstocks encourages studies of heterogeneous catalysts to increase the feasibility of sustainable production of biomass derivatives, such as γ-valerolactone, among others. In this context, first, the performance of a titania-supported nickel catalyst (a non-noble catalyst) was evaluated in the reaction of hydrogenation of levulinic acid to γ-valerolactone in water using molecular hydrogen. The methods used included the synthesis of titania via the solgel method and nickel deposition by deposition–precipitation via removal of the complexing agent. The nickel was activated in a flow of hydrogen; the temperature of reduction and the calcination step were investigated with experiments at reaction conditions to study the catalyst’s stability. Then, after a statistical evaluation of several proposed kinetic models, the kinetics of the reaction was found to be best represented by a model obtained considering that the reaction over the surface was the determinant step, followed by the non-dissociative adsorption of hydrogen and the competitive adsorption among hydrogen, levulinic acid, and γ-valerolactone. With that model, the activation energy of the levulinic acid to 4-hydroxypentanoic acid step was (47.0 ± 1.2) kJ mol⁻¹, since the determinant step was the hydrogenation reaction of the levulinic acid to 4-hydroxypentanoic acid. It was also concluded that the catalyst prepared was stable, active, and selective to γ-valerolactone.

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.

This study reports the continuous production of H₂ from the wastewater effluent of the biodiesel industry in a medium containing water under supercritical conditions. The effects of temperature and feed flow rate on the generation of hydrogen were evaluated and optimized. At a temperature of 700 °C and a 17.5 mL/min feed flow, a total gas flow of 5541 NmL/min was achieved. Among all identified gases, hydrogen represented the highest molar fraction of 73%. Under optimized conditions, a H₂ yield of 357 NmL/g_{effluent feed} was observed. The experimental results indicate a significant increase in the H₂ production at the highest experimented temperatures. On the other hand, the feed flow only slightly influenced the process within the assessed range but showed a tendency to increase the H₂ production at the highest values. Finally, information on energy efficiency optimization and scale-up are presented, and at the same time, different designs for industrial implementation of the hydrothermal gasification process are proposed.

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.