SAM

Development of molten salt–based processes through thermodynamic evaluation assisted by machine learning

ROACH, Lucien — Fri, 01 Nov 2024 00:00:00 GMT

Development of molten salt–based processes through thermodynamic evaluation assisted by machine learning ROACH, Lucien; ERRIGUIBLE, Arnaud; AYMONIER, Cyril Molten salt–based processes and hydrofluxes are highly sensitive to mixture composition and require knowledge of the combined melting point for successful materials syntheses. In particular processes using hydroxide–based fluxes (pure salt melts) and hydrofluxes (salt melts containing 15–50% HO) have been shown to be interesting environments to synthesize inorganic materials in high oxidation states. The development of tools to predict these properties is desirable to inform the implementation of processes using these mixtures. In this work, we use an artificial neural network model to estimate the melting points of fluxes and hydrofluxes comprising of quaternary mixtures of NaOH, KOH, LiOH, and H2O. A database of 1644 data points collected from 47 different sources was used in the training of the model. Melting points were predicted from the molar fractions of each component (4 independent variables)...

Supercritical water oxidation using hydrothermal flames at microscale as a potential solution for organic waste treatment in space applications – A practical demonstration and numerical study

SHARMA, Deewakar — Wed, 01 May 2024 00:00:00 GMT

Supercritical water oxidation using hydrothermal flames at microscale as a potential solution for organic waste treatment in space applications – A practical demonstration and numerical study SHARMA, Deewakar; NGUYEN, Olivier; PALENCIA, Fabien; LECOUTRE, Carole; GARRABOS, Yves; GLOCKNER, Stéphane; MARRE, Samuel; ERRIGUIBLE, Arnaud Supercritical water oxidation (SCWO) with hydrothermal flames is well established for the treatment of aqueous organic waste as it not only overcomes the limitations of simple SCWO, such as precipitation of salts, but also exhibits many advantages over other waste treatment processes. Seeking these advantages, we propose to perform SCWO using hydrothermal flames in microfluidic reactors ) for aerospace applications to be used in deep space/ISS missions. The novelty and highlight of this work are successful demonstration of realizing microreactors (channel width 200 ), which can withstand pressure of 250 bar with temperature °C, thereby presenting the feasibility to realize this technology. We present the first evidence of SCWO/hydrothermal in a flow microreactor of sapphire, which is captured through optical visualization. This is followed by a numerical investigation to understand the underlying physics leading to the formation of hydrothermal flame and thus differentiate it from a simple SCWO reaction. The simulations are performed in a 2D domain in a co-flow configuration with equal inlet velocity of fuel and oxidizer at two different inlet temperatures (350 °C and 365 °C), just below the critical temperature of water using ethanol and oxygen, the former acting not only as a model organic matter but also fuel for the formation of hydrothermal flames. It is observed that due to microscale size of the system, hydrothermal flames are formed at low inlet velocities (< 30 mm/s), while reaction at higher ones are characterized as simple SCWO reaction. This upper limit of inlet velocity was found to increase with inlet temperature. Finally, some key characteristics of hydrothermal flames - ignition delay time, flame structure, shape, and local propagation speed are analyzed.

Thermodynamic assessment of two-step nucleation occurrence in supercritical fluid

GUILLOU, Pierre — Sun, 01 Sep 2024 00:00:00 GMT

Thermodynamic assessment of two-step nucleation occurrence in supercritical fluid GUILLOU, Pierre; MARRE, Samuel; ERRIGUIBLE, Arnaud For the crystallization of an API in supercritical CO2, a two – step nucleation mechanism involves the apparition of metastable liquid droplets in the vapour phase composed of the API dissolved in the CO2, before crystallization. To find out the pressure and temperature conditions such a two – step mechanism could be observed, we studied the stability / metastability / instability for {(S)-Naproxen + CO2} and {(RS)-Ibuprofen + CO2} vapour binary mixtures. Thermodynamic computations proposed in the paper, have shown that a mixture of API and CO2 at elevated pressures can be unstable and/or metastable with respect to a liquid-vapour equilibrium and at the same time with respect to a solid-vapour equilibrium. Depending on the degree of supersaturation, such a mixture can potentially first decompose via spinodal decomposition into coexisting liquid and vapour phases, which turn due to nucleation and growth theory to a solid-fluid equilibrium.

Assessment of machine learning algorithms for predicting autoignition and ignition delay time in microscale supercritical water oxidation process

SHARMA, Deewakar — Wed, 01 Nov 2023 00:00:00 GMT

Assessment of machine learning algorithms for predicting autoignition and ignition delay time in microscale supercritical water oxidation process SHARMA, Deewakar; LECOUTRE, Carole; PALENCIA, Fabien; NGUYEN, Olivier; ERRIGUIBLE, Arnaud; MARRE, Samuel With recent advancements in space technology, there is a need to develop technologies to ensure a sustainable environment for human survival. Among these, treatment of human and organic waste aboard manned space missions is a challenging task for which supercritical water oxidation using hydrothermal flames has been proposed as a possible solution. The critical step in readily adopting this technology from established ground-based setups is scaling the process to microscale. In addition to the challenge of physical realization of the microreactors at these high pressure and temperature (P > 22 MPa, T>350°C) conditions, the need to explicitly analyze the process dynamics at microscale is inevitable owed to the size of the reactors under consideration, the physics being significantly different from meso/mini scale systems. One of the primary objectives is to identify the operating physical parameters for which formation of hydrothermal flames can be obtained. Before proceeding with an expensive computational or experimental approach to determine the exact ignition map, an initial estimate based on physical arguments can help in providing insights into the process. We address this problem using homogeneous ignition calculations to develop machine learning models to predict autoignition as well as ignition delay time. The ingenuity of the work lies in defining autoignition criteria in relation to flow time scales expected at microscale. Various classification models were trained and tested for predicting autoignition and regression models were demonstrated to predict the ignition delay time. While predicting autoignition is a straightforward process, a two-step approach is proposed for ignition delay time. Finally, how machine learning can be used more explicitly, particularly for understanding and designing efficient microreactors, is presented which highlights that machine learning approach is not merely restricted to prediction but can also have real implications on improving the process as a whole.

Applications of machine learning in supercritical fluids research

ROACH, Lucien — Wed, 01 Nov 2023 00:00:00 GMT

Applications of machine learning in supercritical fluids research ROACH, Lucien; RIGNANESE, Gian-Marco; ERRIGUIBLE, Arnaud; AYMONIER, Cyril Machine learning has seen increasing implementation as a predictive tool in the chemical and physical sciences in recent years. It offers a route to accelerate the process of scientific discovery through a computational data-driven approach. Whilst machine learning is well established in other fields, such as pharmaceutical research, it is still in its infancy in supercritical fluids research, but will likely accelerate dramatically in coming years. In this review, we present a basic introduction to machine learning and discuss its current uses by supercritical fluids researchers. In particular, we focus on the most common machine learning applications; including: (1) The estimation of the thermodynamic properties of supercritical fluids. (2) The estimation of solubilities, miscibilities, and extraction yields. (3) Chemical reaction optimization. (4) Materials synthesis optimization. (5) Supercritical power systems. (6) Fluid dynamics simulations of supercritical fluids. (7) Molecular simulation of supercritical fluids and (8) Geosequestration of CO2 using supercritical fluids.