Multi–scale Modelling of Refinery Pre–heat Trains Undergoing Fouling for Improved Energy Efficiency

Fouling in pre–heat trains of refinery crude distillation units causes major energy inefficiencies, resulting in increased costs, greenhouse gas emissions, maintenance efforts and health and safety hazards. Although chemical and physical phenomena underlying fouling deposition are extremely complex and several details remain unknown, the understanding of the fouling process has progressed significantly in the past 40 years. However, this knowledge has so far not been exploited to effectively improve heat exchanger and heat exchanger network design and operation. As a result, old methodologies that neglect the local effects and dynamics of fouling, in favour of lumped, steady–state, heuristic models (e.g. using TEMA fouling factors) are still used. In this thesis a novel mathematical model for pre–heat trains undergoing crude oil fouling was developed, validated with plant data and used to propose mitigation strategies. The model is dynamic, distributed and considers simultaneously several scales of investigation. Key phenomena are captured at the tube level as a function of local conditions. These include the dependence of fouling rate on temperature and velocity, the variation of physical properties, the structural changes of the deposits over time (ageing) and the dynamics of surface roughness. The single tube model was then extended to describe a unit–scale heat exchanger geometry. This has been validated against plant data from four units in two refineries operated by major oil companies. The predicted outlet temperatures over extended periods (i.e. 4-16 months) are accurate within ±1% for the tube–side and ± 2% for the shell–side. Model simulations were then used to assist the retrofit of one particular unit for which it was possible to save ca. 22% of the energy losses (not including pumping power) produced by fouling over ca. a year of operation. Finally, the interconnection of single heat exchangers in a network allowed the simulation of the fouling behaviour of two existing pre–heat trains. To systematically assess the impact of fouling on refinery economics, a set of key performance indicators (KPIs) was proposed. Network–level simulations were used in conjunction with the KPIs to unveil complex interactions and propose network retrofit arrangements that improve energy recovery over time whilst reducing fouling. It is concluded that the model can be used with confidence to predict fouling and assist monitoring, design and retrofit of refinery heat exchangers and heat exchanger networks. The results shown indicate that the approach proposed can lead to substantial benefits

Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes

Owing to their simple construction, cost effectiveness, and high thermal efficiency, pulsating heat pipes (PHPs) are growing in popularity as cooling devices for electronic equipment. While PHPs can be very resilient as passive cooling systems, their operation relies on the establishment and persistence of slug/plug flow as the dominant flow regime. It is, therefore, paramount to predict the flow regime accurately as a function of various operating parameters and design geometry. Flow pattern maps that capture flow regimes as a function of nondimensional numbers (e.g., Froude, Weber, and Bond numbers) have been proposed in the literature. However, the prediction of flow patterns based on deterministic models is a challenging task that relies on the ability of explaining the very complex underlying phenomena or the ability to measure parameters, such as the bubble acceleration, which are very difficult to know beforehand. In contrast, machine learning algorithms require limited a priori knowledge of the system and offer an alternative approach for classifying flow regimes. In this work, experimental data collected for two working fluids (ethanol and FC-72) in a PHP at different gravity and power input levels, were used to train three different classification algorithms (namely K-nearest neighbors, random forest, and multilayer perceptron). The data were previously labeled via visual classification using the experimental results. A comparison of the resulting classification accuracy was carried out via confusion matrices and calculation of accuracy scores. The algorithm presenting the highest classification performance was selected for the development of a flow pattern map, which accurately indicated the flow pattern transition boundaries between slug/plug and annular flows. Results indicate that, once experimental data are available, the proposed machine learning approach could help in reducing the uncertainty in the classification of flow patterns and improve the predictions of the flow regimes

Stretching graphene using polymeric micro-muscles

The control of strain in two-dimensional materials opens exciting perspectives for the engineering of their electronic properties. While this expectation has been validated by artificial-lattice studies, it remains elusive in the case of atomic lattices. Remarkable results were obtained on nanobubbles and nano-wrinkles, or using scanning probes; microscale strain devices were implemented exploiting deformable substrates or external loads. These devices lack, however, the flexibility required to fully control and investigate arbitrary strain profiles. Here, we demonstrate a novel approach making it possible to induce strain in graphene using polymeric micrometric artificial muscles (MAMs) that contract in a controllable and reversible way under an electronic stimulus. Our method exploits the mechanical response of poly-methyl-methacrylate (PMMA) to electron-beam irradiation. Inhomogeneous anisotropic strain and out-of-plane deformation are demonstrated and studied by Raman, scanning-electron and atomic-force microscopy. These can all be easily combined with the present device architecture. The flexibility of the present method opens new opportunities for the investigation of strain and nanomechanics in two-dimensional materials

Tumori dell’intestino tenue: nostra esperienza in urgenza

I tumori dell’intestino tenue sono neoplasie relativamente rare. Sintomi di natura aspecifica ed esami diagnostici di basse sensibilità e validità sono complessivamente responsabili di una diagnosi ritardata e, in caso di malignità, di malattia spesso avanzata e per lo più incurabile con l’intervento. Uno studio retrospettivo è stato effettuato in 42 casi con presentazione clinica di acuzie, dal 1972 al 2001; l’età media dei pazienti è stata di 52 anni (range 14-79 anni); c’è stata una lieve prevalenza del sesso femminile (57.1% vs 42.9%). La presentazione acuta più comune è stata l’occlusione (57.1%), seguita da sanguinamento gastrointestinale (23.8%), perforazione (14.3%) e occlusione/perforazione (4.8%). I tumori benigni si sono presentati nel 38.1% (16 casi), l’adenoma rappresenta il tipo più comune; le forme maligne sono state il 61.9% (26 casi), l’adenocarcinoma e i linfomi rappresentano l’istotipo più comune. La chirurgia radicale è stata possibile solo nel 57% delle forme maligne (24 pazienti): la morbidità è stata del 4.8% (2 casi: 1 deiscenza anastomotica e 1 ascesso subfrenico); la mortalità è stata del 14.3%. Dal nostro studio retrospettivo possiamo affermare che la sopravvivenza per le lesioni maligne è strettamente dipendente dalla precocità della diagnosi TNM e dalla possibilità di una procedura chirurgica radicale, prima che la lesione diventi non resecabile, come è accaduto nel 42% dei nostri casi. Un indice di sospetto estremamente elevato nella valutazione di sintomi, spesso aspecifici, integrato con studi diagnostici specifici, potrebbe rappresentare l’approccio più appropriato. La prognosi per le forme benigne è invece eccellente in tutti i casi

Anisotropic straining of graphene using micropatterned SiN membranes

We use micro-Raman spectroscopy to study strain profiles in graphene monolayers suspended over SiN membranes micropatterned with holes of non-circular geometry. We show that a uniform differential pressure load $\Delta P$ over elliptical regions of free-standing graphene yields measurable deviations from hydrostatic strain conventionally observed in radially-symmetric microbubbles. The top hydrostatic strain $\bar{\varepsilon}$ we observe is estimated to be $\approx0.7\%$ for $\Delta P = 1\,{\rm bar}$ in graphene clamped to elliptical SiN holes with axis $40$ and $20\,{\rm \mu m}$. In the same configuration, we report a $G_\pm$ splitting of $10\,{\rm cm^{-1}}$ which is in good agreement with the calculated anisotropy $\Delta\varepsilon \approx 0.6\%$ for our device geometry. Our results are consistent with the most recent reports on the Gr\"uneisen parameters. Perspectives for the achievement of arbitrary strain configurations by designing suitable SiN holes and boundary clamping conditions are discussed.Comment: 8 pages, 6 figure (including SI

A machine learning approach to the prediction of heat-transfer coefficients in micro-channels

The accurate prediction of the two-phase heat transfer coefficient (HTC) as a function of working fluids, channel geometries and process conditions is key to the optimal design and operation of compact heat exchangers. Advances in artificial intelligence research have recently boosted the application of machine learning (ML) algorithms to obtain data-driven surrogate models for the HTC. For most supervised learning algorithms, the task is that of a nonlinear regression problem. Despite the fact that these models have been proven capable of outperforming traditional empirical correlations, they have key limitations such as overfitting the data, the lack of uncertainty estimation, and interpretability of the results. To address these limitations, in this paper, we use a multi-output Gaussian process regression (GPR) to estimate the HTC in microchannels as a function of the mass flow rate, heat flux, system pressure and channel diameter and length. The model is trained using the Brunel Two-Phase Flow database of high-fidelity experimental data. The advantages of GPR are data efficiency, the small number of hyperparameters to be trained (typically of the same order of the number of input dimensions), and the automatic trade-off between data fit and model complexity guaranteed by the maximization of the marginal likelihood (Bayesian approach). Our paper proposes research directions to improve the performance of the GPR-based model in extrapolation.Comment: 7 pages, 2 figures, to be published in the proceedings of the 17th International Heat Transfer Conference 2023 (IHTC-17

Revealing the atomic structure of the buffer layer between SiC(0001) and epitaxial graphene

On the SiC(0001) surface (the silicon face of SiC), epitaxial graphene is obtained by sublimation of Si from the substrate. The graphene film is separated from the bulk by a carbon-rich interface layer (hereafter called the buffer layer) which in part covalently binds to the substrate. Its structural and electronic properties are currently under debate. In the present work we report scanning tunneling microscopy (STM) studies of the buffer layer and of quasi-free-standing monolayer graphene (QFMLG) that is obtained by decoupling the buffer layer from the SiC(0001) substrate by means of hydrogen intercalation. Atomic resolution STM images of the buffer layer reveal that, within the periodic structural corrugation of this interfacial layer, the arrangement of atoms is topologically identical to that of graphene. After hydrogen intercalation, we show that the resulting QFMLG is relieved from the periodic corrugation and presents no detectable defect sites

Morphological modulation of graphene-mediated hybridization in plasmonic systems

Graphene laid on plasmonic Au-nanoparticle arrays becomes uniaxially wrinkled and induces optical anisotropy in the plasmonic response of the system

Modeling Photodetection at the Graphene/Ag2S Interface

Mixed-dimensional systems host interesting phenomena that involve electron and ion transport along or across the interface, with promising applications in optoelectronic and electrochemical devices. Herein, a heterosystem consisting of a graphene monolayer with a colloidal Ag2S nanocrystal film atop, in which both ions and electrons are involved in photoelectrical effects, is studied. An investigation of the transport at the interface in different configurations by using a phototransistor configuration with graphene as a charge-transport layer and semiconductor nanocrystals as a light-sensitive layer is performed. The key feature of charge transfer is investigated as a function of gate voltage, frequency, and incident light power. A simple analytical model of the photoresponse is developed, to gain information on the device operation, revealing that the nanocrystals transfer electrons to graphene in the dark, but the opposite process occurs upon illumination. A frequency-dependence analysis suggests a fractal interface between the two materials. This interface can be modified using solid-state electrochemical reactions, leading to the formation of metallic Ag particles, which affect the graphene properties by additional doping, while keeping the photoresponse. Overall, these results provide analytical tools and guidelines for the evaluation of coupled electron/ion transport in hybrid systems

Microscale thermophoresis and docking studies suggest lapachol and auraptene are ligands of IDO1.

Indoleamine 2,3-dioxygenase 1 (IDO1) is a key target for the development of small molecule immunotherapies in oncology. In this framework, the screening of chemotherapeutic agents to identify compounds binding to IDO1 represents a valuable strategy for the development of multitarget drug candidates that combine synergic immunoregulatory properties to cytotoxic activity. In this study, we report that two natural compounds endowed with anticancer activity, namely lapachol and auraptene, act as IDO1 ligands with dissociation constant (Kd) in the micromolar range of potency. Docking studies provide plausible binding modes of these compounds to the catalytic cleft of IDO1. Our results support the notion that lapachol and auraptene may be considered interesting lead compounds in the immuno-oncology setting