Development of a SAFT-γ Mie group-contribution approach to modelling working fluids and its application in the computer-aided molecular and process design of ORCs

Los ciclos de Rankine orgánicos (Organic Rankine Cycles, ORCs) se presentan como una buena alternativa para recuperar el calor residual. De entre los fluidos de trabajo posibles, los hidrofluorocarbonos (HFCs) destacan por su potencial para aumentar el rendimiento de estos ciclos. Dentro del método SAFT-γ Mie, se han desarrollado los grupos funcionales pertenecientes a los HFCs, obteniendo una estimación de la presión de vapor y de la densidad de líquido saturado con una desviación media absoluta, respecto a los datos experimentales, de 3,17% y 1,77%, respectivamente. Por último, una vez desarrollado el modelo termodinámico con la ecuación SAFT-γ Mie y analizadas las propiedades de transporte, se propone un marco para el diseño molecular y de proceso asistido por ordenador (Computer-Aided Molecular and Process Design, CAMPD), con el objetivo de maximizar la potencia obtenida con el ciclo, modificando para ello tanto el fluido de trabajo como las condiciones de operación.Organic Rankine Cycles (ORCs) are known to be a good alternative for the recovery pf waste heat. Among the wide variety of working fluids, refrigerants, in particular fluorinated hydrocarbons, have a great potential to increase the thermodynamic performance of these cycles. Within the SAFT-γ Mie approach, functional groups present in HFCs have been developed, obtaining an estimation of the vapour pressure and the saturated-liquid density with an absolute average deviation (AAD), with respect to experimental data, of 3.17% and 1.77%, respectively. Finally, once the thermodynamic model with the SAFT-γ Mie approach is developed and the transport properties are analysed, a computer-aided molecular and process design (CAMPD) framework is proposed, with the objective of maxims the power output, modifying both the working fluid and the operating conditions.Departamento de Ingeniería Química y Tecnología del Medio AmbienteChemical Engineering Department, Molecular Systems Engineering (MSE). Imperial College of Science, Technology and Medicine of LondonMáster en Ingeniería Químic

Predicting the Surface Tension of Liquids: Comparison of Four Modeling Approaches and Application to Cosmetic Oils

The efficiency of four modeling approaches, namely group contributions, corresponding-states principle, σ-moment-based neural networks, and graph machines, are compared for the estimation of the surface tension (ST) of 269 pure liquid compounds at 25 °C from their molecular structure. This study focuses on liquids containing only carbon, oxygen, hydrogen or silicon atoms since our purpose is to predict the surface tension of cosmetic oils. Neural network estimations are performed from σ-moment descriptors as defined in the COSMO-RS model, while methods based on group contributions, corresponding-states principle and graph machines use 2D molecular information (SMILES codes). The graph machine approach provides the best results, estimating the surface tensions of 23 cosmetic oils, such as hemisqualane, isopropyl myristate or decamethylcyclopentasiloxane (D5), with accuracy better than 1 mN.m–1. A demonstration of the graph machine model using the recent Docker technology is available for download in the Supplementary Information

Design and evaluation of heat transfer fluids for direct immersion cooling of electronic systems

Comprehensive molecular design was used to identify new heat transfer fluids for direct immersion phase change cooling of electronic systems. Four group contribution methods for thermophysical properties relevant to heat transfer were critically evaluated and new group contributions were regressed for organosilicon compounds. 52 new heat transfer fluids were identified via computer-aided molecular design and figure of merit analysis. Among these 52 fluids, 9 fluids were selected for experimental evaluation and their thermophysical properties were experimentally measured to validate the group contribution estimates. Two of the 9 fluids (C6H11F3 and C5H6F6O) were synthesized in this work. Pool boiling experiments showed that the new fluids identified in this work have superior heat transfer properties than existing coolant HFE 7200. The radiative forcing and global warming potential of new fluids, calculated via a new group contribution method developed in this work and FT-IR analysis, were found to be significantly lower than those of current coolants. The approach of increasing the thermal conductivity of heat transfer fluids by dispersing nanoparticles was also investigated. A model for the thermal conductivity of nanoparticle dispersions (nanofluids) was developed that incorporates the effect of size on the intrinsic thermal conductivity of nanoparticles. The model was successfully applied to a variety of nanoparticle-fluid systems. Rheological properties of nanofluids were also investigated and it was concluded that the addition of nanoparticles to heat transfer fluids may not be beneficial for electronics cooling due to significantly larger increase in viscosity relative to increase in thermal conductivity.PhDCommittee Chair: Teja, Amyn; Committee Member: Hess, Dennis; Committee Member: Joshi, Yogendra; Committee Member: Liotta, Charles; Committee Member: Realff, Matthe

Develop a Hazard Index Using Machine Learning Approach for the Hazard Identification of Chemical Logistic Warehouses

PresentationWith the rapid development of chemical process plants, the safe storage of hazardous chemicals become an essential topic. Several chemical warehouse incidents related to fire and explosion have been reported recently. Therefore, an accurate hazard identification method for the logistic warehouse is needed not only for the facility to develop a proper emergency response plan but also for the residents who live near the facility to have an effective hazard communication. Furthermore, the government can better allocate the resources for first responders to make fire protection strategies, and the stakeholders can lead to improved risk management. Hazard index is a helpful tool to identify and quantify the hazard in a facility or a process unit. The challenge for this research is to improve the current method with the novel technique to implement our purpose. The first objective of this research is to develop a “Storage Hazard Factor” (SHF) to evaluate and rank the inherent hazards of chemicals stored in logistic warehouses. In the factor calculation, the inherent hazard of chemicals is determined by various parameters (e.g., the NFPA rating, the flammability limit, and the protective action criteria values, etc.) and validated by the comparison with other indices. The current criteria for flammable hazard ratings are based on flash point, which is proved to be insufficient. Two machine learning based methods will be used for the classification of liquid flammability considering aerosolization based on DIPPR 801 database. Subsequently, SHF and other warehouse safety penalty factors (e.g., the quantity of the chemicals, the distance to the nearest fire department, etc.) are utilized to identify the Logistic Warehouse Hazard Index (LWHI) of the facilities. In the last chapter, this method is applied to real-time data from Houston Chronicle, and several statistical analyses are used to prove the hazard index is helpful for hazard identification to emergency responders and hazard communication to the public

In Silico Prediction of Physicochemical Properties

This report provides a critical review of computational models, and in particular(quantitative) structure-property relationship (QSPR) models, that are available for the prediction of physicochemical properties. The emphasis of the review is on the usefulness of the models for the regulatory assessment of chemicals, particularly for the purposes of the new European legislation for the Registration, Evaluation, Authorisation and Restriction of CHemicals (REACH), which entered into force in the European Union (EU) on 1 June 2007. It is estimated that some 30,000 chemicals will need to be further assessed under REACH. Clearly, the cost of determining the toxicological and ecotoxicological effects, the distribution and fate of 30,000 chemicals would be enormous. However, the legislation makes it clear that testing need not be carried out if adequate data can be obtained through information exchange between manufacturers, from in vitro testing, and from in silico predictions. The effects of a chemical on a living organism or on its distribution in the environment is controlled by the physicochemical properties of the chemical. Important physicochemical properties in this respect are, for example, partition coefficient, aqueous solubility, vapour pressure and dissociation constant. Whilst all of these properties can be measured, it is much quicker and cheaper, and in many cases just as accurate, to calculate them by using dedicated software packages or by using (QSPRs). These in silico approaches are critically reviewed in this report.JRC.I.3-Toxicology and chemical substance

Modeling the gas-particle partitioning of secondary organic aerosol: the importance of liquid-liquid phase separation

The partitioning of semivolatile organic compounds between the gas phase and aerosol particles is an important source of secondary organic aerosol (SOA). Gas-particle partitioning of organic and inorganic species is influenced by the physical state and water content of aerosols, and therefore ambient relative humidity (RH), as well as temperature and organic loading levels. We introduce a novel combination of the thermodynamic models AIOMFAC (for liquid mixture non-ideality) and EVAPORATION (for pure compound vapor pressures) with oxidation product information from the Master Chemical Mechanism (MCM) for the computation of gas-particle partitioning of organic compounds and water. The presence and impact of a liquid-liquid phase separation in the condensed phase is calculated as a function of variations in relative humidity, organic loading levels, and associated changes in aerosol composition. We show that a complex system of water, ammonium sulfate, and SOA from the ozonolysis of α-pinene exhibits liquid-liquid phase separation over a wide range of relative humidities (simulated from 30% to 99% RH). Since fully coupled phase separation and gas-particle partitioning calculations are computationally expensive, several simplified model approaches are tested with regard to computational costs and accuracy of predictions compared to the benchmark calculation. It is shown that forcing a liquid one-phase aerosol with or without consideration of non-ideal mixing bears the potential for vastly incorrect partitioning predictions. Assuming an ideal mixture leads to substantial overestimation of the particulate organic mass, by more than 100% at RH values of 80% and by more than 200% at RH values of 95%. Moreover, the simplified one-phase cases stress two key points for accurate gas-particle partitioning calculations: (1) non-ideality in the condensed phase needs to be considered and (2) liquid-liquid phase separation is a consequence of considerable deviations from ideal mixing in solutions containing inorganic ions and organics that cannot be ignored. Computationally much more efficient calculations relying on the assumption of a complete organic/electrolyte phase separation below a certain RH successfully reproduce gas-particle partitioning in systems in which the average oxygen-to-carbon (O:C) ratio is lower than ~0.6, as in the case of α-pinene SOA, and bear the potential for implementation in atmospheric chemical transport models and chemistry-climate models. A full equilibrium calculation is the method of choice for accurate offline (box model) computations, where high computational costs are acceptable. Such a calculation enables the most detailed predictions of phase compositions and provides necessary information on whether assuming a complete organic/electrolyte phase separation is a good approximation for a given aerosol system. Based on the group-contribution concept of AIOMFAC and O:C ratios as a proxy for polarity and hygroscopicity of organic mixtures, the results from the α-pinene system are also discussed from a more general point of view

Organic solvent nanofiltration (OSN) modelling - from pure solvents to highly rejected solutes

Imperial Users onl

Predicting physical properties of alkanes with neural networks

We train artificial neural networks to predict the physical properties of linear, single branched, and double branched alkanes. These neural networks can be trained from fragmented data, which enables us to use physical property information as inputs and exploit property-property correlations to improve the quality of our predictions. We characterize every alkane uniquely using a set of five chemical descriptors. We establish correlations between branching and the boiling point, heat capacity, and vapor pressure as a function of temperature. We establish how the symmetry affects the melting point and identify erroneous data entries in the flash point of linear alkanes. Finally, we exploit the temperature and pressure dependence of shear viscosity and density in order to model the kinematic viscosity of linear alkanes. The accuracy of the neural network models compares favorably to the accuracy of several physico-chemical/thermodynamic methods

Fate and transport of TCE-Petroleum Hydrocarbon mixture at UTTR

In 1988 a preliminary assessment (PA) study was conducted at Chemical Disposal Pit No. 4 to address several concerns of Hill Air Force Base (HAFB), the facility operators. The objectives of the PA were; i) to assess the potential for organic chemical releases to the subsurface environment, and ii) to determine the need for any immediate control measures, if the subsurface environment were threatened