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Machine learning based on neural networks facilitates data-driven techniques for handling large amounts of data, either obtained through experiments or simulations at multiple spatio-temporal scales, thereby finding the hidden patterns underlying these data and promoting efficient research methods. The main purpose of this paper is to extend the capabilities of a new solver called realFluidReactingNNFoam, under development at the University of Perugia, in OpenFOAM with a neural network algorithm for replacing complex real-fluid thermophysical property evaluations, using the approach of coupling OpenFOAM and Python-trained neural network models. Currently, neural network models are trained against data generated using the Peng–Robinson equation of state assuming a mixture's frozen temperature. The OpenFOAM solver, where needed, calls the neural network models in each grid cell with appropriate inputs, and the returned results are used and stored in suitable OpenFOAM data structures. Such inference for thermophysical properties is achieved via the "Neural Network Inference in C made Easy (NNICE)" library, which proved to be very efficient and robust. The overall model is validated considering a liquid-rocket benchmark comprised of liquid-oxygen (LOX) and gaseous-hydrogen (GH2) streams. The model accounts for real-fluid thermodynamics and transport properties, making use of the Peng–Robinson equation of state and the Chung transport model. First, the development of a real-fluid model with an artificial neural network is described in detail. Then, the numerical results of the transcritical mixing layer (LOX/GH2) benchmark are presented and analyzed in terms of accuracy and computational efficiency. The results of the overall implementation indicate that the combined OpenFOAM and machine learning approach provides a speed-up factor higher than seven, while preserving the original solver accuracy. [ABSTRACT FROM AUTHOR]

Studies of phase transition kinetics are important for such supercritical processes as supercritical drying, adsorption, micronization, etc. In supercritical technologies, "organic solvent—CO2" systems are often formed, the properties of which strongly depend on the system parameters. In this article, the kinetic curves of phase transitions in the "2-propanol—CO2" system were investigated experimentally and theoretically. Experimental studies were carried out in a 250 mL high-pressure apparatus at temperatures of 313 and 333 K and pressures of 6.3 and 7.8 MPa with and without the addition of alginate porous gel. Theoretical studies were carried out using the mass transfer equation, the Peng-Robinson equation of state, and the Van der Waals mixing rules, with Python being used for the calculations. The mass transfer coefficients and equilibrium concentrations of CO2 in the liquid phase were determined using the BFGS optimization method. [ABSTRACT FROM AUTHOR]

Process monitoring and forecasting are essential to ensure the efficiency of industrial processes. Although it is possible to model processes using phenomenological approaches, these are not always easy to apply and generalize due to the complexity of the processes and the high number of unknown parameters. This work aims to present a hybrid modeling architecture that combines a phenomenological model with machine learning models. The proposal is to enable the use of simplified phenomenological models to explain the basic principles behind a phenomenon. Next, the data-oriented model corrects deviations from the simplified model predictions. The research hypothesis consists of showing the benefits of integrating prior knowledge of chemical engineering in simplifying data-based models, enhancing their generalization and improving their interpretability. The gasification process of lignin biomass with supercritical water was used as a case study for this methodology and the variable to be observed was the production of hydrogen. The real experimental data of this process were augmented using Gibbs energy minimization with the Peng–Robinson equation of state, thus generating a more voluminous database that was considered as real process data. The ideal gas model was used as a simplified model, producing significant deviations in predictions (relative deviations greater than 20%). Deviations (∆H2 = H 2 r e a l − H 2 p r e d i c t ) were used as the target variable for the machine learning model. Linear regression models (LASSO and simple linear regression) were used to predict ∆H2 and this variable was added to the simplified forecast model. This consisted of the hybrid prediction of the resulting hydrogen formation ( H 2 p r e d i c t ). Among the verified models, the simple linear regression adjusted better to the values of ∆H2 (R2 = 0.985) and MAE smaller than 0.1. Thus, the proposed hybrid architecture allowed for the prediction of the formation of hydrogen during the gasification process of lignin biomass, despite the thermodynamic limitations of the ideal gas model. Hybridization proved to be robust as a process monitoring tool, providing the abstraction of non-idealities of industrial processes through simple, data-oriented models, without losing predictive power. The objective of the work was fulfilled, presenting a new possibility for the monitoring of real industrial processes. [ABSTRACT FROM AUTHOR]

The growing attention regarding a more sustainable future, and thus into energy recovery and waste reduction technologies, has intensified the interest towards processes which allow to exploit waste and biomasses to generate energy, such as the anaerobic digestion. Improving the efficiency of this industrial application is crucial to increase methane production, and is essential from the economic, environmental and safety point of view. This study focuses on the thermodynamic modelling of a steady-state reactor as a flash unit, in order to determine the best operating conditions to produce the maximum amount of pure bio-methane. To this purpose, a new hybrid approach based on the Peng–Robinson cubic equation of state and on the Multi-Parameter Helmholtz-Energy EoS has been proposed. The simulations, performed using the developed algorithm at temperatures between 20 and 55 °C and at pressure values between 0.3 atm and 1.5 atm, point out that the fugacity of the mixture evaluated with the proposed technique is much more accurate and reliable than the one calculated with the PR EoS. In addition, this research has shown not only that the purity and the production of the biogas can be optimised by working at mesophilic conditions and at pressure between 1 atm and 1.5 atm, but also that it is not convenient to operate in a temperature range of 42 °C–45 °C, since about 20 % more H 2 S goes into the exiting biogas, reducing the C H 4 amount and raising the post-treatment costs. Lastly, it has been seen that there is a significant water content in the vapour phase (∼5 %wt.), and this is a factor to be taken into account in order to improve the process. [ABSTRACT FROM AUTHOR]

In this work volume-translated Peng-Robinson group contribution equation of state was used to calculate excess enthalpies. Four model systems were selected with the purpose to compare experimental and predicted enthalpy values at different temperatures. After the calculations were performed in Matlab software, results were verified with free software tool of Dortmunder Datenbank (DDB). In a next step, the mixing process and interaction forces were described on the basis of the sign and course of enthalpy values. The endothermic behavior of three systems could be well predicted, while for the most polar system, predictions were less precise. Furthermore, the discrepancy between experimental data from the literature and predicted values was discussed to evaluate the accuracy of the selected model. Lowest mean deviations (<75 J/mol and <15% at all temperatures) could be stated for alkane/benzene mixtures, while highest deviations could be again observed for the most polar mixture. Although the magnitude of deviations was in agreement with the literature, it could be shown that the selected temperature is of major importance for the quality of predictions. Furthermore, a review of different literature values for the n-hexane/benzene system could reveal that the reliability of experimental data has to be carefully checked. [ABSTRACT FROM AUTHOR]

The leakage process simulation of a Natural Gas Liquid (NGL) storage tank requires the simultaneous solution of the NGL's pressure, temperature and phase state in the tank and across the leak hole. The methods available in the literature rarely consider the liquid/vapor phase transition of the NGL during such a process. This paper provides a comprehensive pressure-temperature-phase state method to solve this problem. With this method, the phase state of the NGL is predicted by a thermodynamic model based on the volume translated Peng-Robinson equation of state (VTPR EOS). The tank's pressure and temperature are simulated according to the pressure-volume-temperature and isenthalpic expansion principles of the NGL. The pressure, temperature, leakage mass flow rate across the leak hole are calculated from an improved Homogeneous Non-Equilibrium Diener-Schmidt (HNE-DS) model and the isentropic expansion principle. In particular, the improved HNE-DS model removes the ideal gas assumption used in the original HNE-DS model by using a new compressibility factor developed from the VTPR EOS to replace the original one derived from the Clausius-Clayperon equation. Finally, a robust procedure of simultaneously solving the tank model and the leak hole model is proposed and the method is validated by experimental data. A variety of leakage cases demonstrates that this method is effective in simulating the dynamic leakage process of NGL tanks under critical and subcritical releasing conditions associated with vapor/liquid phase change. [ABSTRACT FROM AUTHOR]

The reinjection of the fire-flooding exhaust is a novel disposal process for handling the exhaust produced by the in-situ combustion technology. For reasonable process design and safe operation, it is of great significance to select an optimum property calculation method for the fire-flooding exhaust. However, due to the compositional particularity and the wide range of operating parameters during reinjection, the state equations in predicting the exhaust properties over the wide range of operating parameters have not been studied clearly yet. Hence, this paper investigates the applicability of several commonly-used equations of state, including the Soave–Redlich–Kwong equation, Peng–Robinson equation, Lee–Kesler–Plocker equation, Benedict–Webb–Rubin–Starling equation, and GERG-2008 equations. Employing Aspen Plus software, the gas densities, compressibility factors, volumetric coefficients, and dew points for five exhaust compositions are calculated. In comparison with the experimental data comprehensively, the result indicates that the Soave–Redlich–Kwong equation shows the highest precision over a wide range of temperature and pressure. The mean absolute percentage error for the above four parameters is 3.84%, 5.17%, 5.53%, and 4.33%, respectively. This study provides a reference for the accurate calculation of the physical properties of fire-flooding exhausts when designing and managing a reinjection system of fire-flooding exhaust. [ABSTRACT FROM AUTHOR]

The original Shan-Chen's pseudopotential Lattice Boltzmann Model (LBM) has continuously evolved during the past two decades. However, despite its capability to simulate multiphase flows, the model still faces challenges when applied to multicomponent-multiphase flows in complex geometries with a moderately high-density ratio. Furthermore, classical cubic equations of state usually incorporated into the model cannot accurately predict fluid thermodynamics in the near-critical region. This paper addresses these issues by incorporating a crossover Peng–Robinson equation of state into LBM and further improving the model to consider the density and the critical temperature differences between the CO2 and water during the injection of the CO2 in a water-saturated 2D homogeneous porous medium. The numerical model is first validated by analyzing the supercritical CO2 penetration into a single narrow channel initially filled with H2O, depicting the fundamental role of the driving pressure gradient to overcome the capillary resistance in near one and higher density ratios. Significant differences are observed by extending the model to the injection of CO2 into a 2D homogeneous porous medium when using a flat versus a curved inlet velocity profile. [ABSTRACT FROM AUTHOR]

Oxygen production from air belongs to energy-intense processes and, as a result, possibilities for its decrease are a frequent topic of optimization studies, often performed with simulation software such as Aspen Plus or Aspen HYSYS. To obtain veritable results and sound solutions, a suitable calculation method hand in hand with justified assumptions and simplifications should form the base of any such studies. Thus, an analysis of the study by Hamayun et al., Energies2020, 13, 6361, has been performed, and several weak spots of the study, including oversimplified assumptions, improper selection of a thermodynamic package for simulation and omission of certain technological aspects relevant for energy consumption optimization studies, were identified. For each of the weak spots, a recommendation based on good praxis and relevant scientific literature is provided, and general recommendations are formulated with the hope that this comment will aid all researchers utilizing Aspen Plus and Aspen HYSYS software in their work. [ABSTRACT FROM AUTHOR]

Due to ecological and economic advantages, natural gas is used as an alternative fuel in the transportation sector in the form of compressed natural gas (CNG) and liquefied natural gas (LNG). Development of infrastructure is necessary to popularize vehicles that use alternative fuels. Selected positive factors from EU countries supporting the development of the CNG market were discussed. The process of natural gas vehicle (NGV) fast filling is related to thermodynamic phenomena occurring in a tank. In this study, the first law of thermodynamics and continuity equations were applied to develop a theoretical model to investigate the effects of natural gas composition on the filling process and the final in-cylinder conditions of NGV on-board composite cylinder (type IV). Peng–Robinson equation of state (P-R EOS) was applied, and a lightweight composite tank (type IV) was considered as an adiabatic system. The authors have devised a model to determine the influence of natural gas composition on the selected thermodynamic parameters during fast filling: Joule–Thomson (J-T) coefficient, in-cylinder gas temperature, mass flow rate profiles, in-cylinder mass increase, natural gas density change, ambient temperature on the final natural gas temperature, influence of an ambient temperature on the amount of refueled natural gas mass. Results emphasize the importance of natural gas composition as an important parameter for the filling process of the NGV on-board composite tank (type IV). [ABSTRACT FROM AUTHOR]

A high-pressure pneumatic catapult works under extreme boundaries such as high-pressure and rapid change of pressure and temperature, with the features of nonlinearity and gas-solid convection. In the thermodynamics processes, the pressure is much larger than the critical pressure, and the compressibility factor can deviate from the Zeno line significantly. Therefore, the pneumatic performance and thermo-physical properties need to be described with the real gas hypothesis instead of the ideal gas one. It is found that the analytical results based on the ideal gas model overestimate the performance of the catapult, in comparison to the test data. To obtain a theoretical model with dynamic leakage compensation, leakage tests are carried out, and the relationship among the leakage rate, pressure and stroke is fitted. The compressibility factor library of the equation of state for compressed air is established and evaluated by referring it to the Nelson-Obert generalized compressibility charts. Based on the Peng–Robinson equation, a theoretical model of the high-pressure pneumatic catapult is developed, in which the effects of dynamic leakage and the forced convective heat transfer between the gas and the metal wall are taken into account. The results from the theoretical model are consistent with the data from ejection tests. This research presents an approach to study the performance of a high-pressure pneumatic catapult with high precision. [ABSTRACT FROM AUTHOR]