Developing an online predictor to predict product sulfur concentration for HDS unit

Hydrodesulfurization (HDS) is an important process in refining industries. Advanced control system (e.g. model predictive controller) requires on-line measurement of the product sulfur at the reactor outlet. However, most HDS processes do not have a sulfur analyzer at the reactor outlet. In order to predict product sulfur concentration usually a data based sulfur predictor is developed. Performance of data based predictor is usually poor since some of the input parameters (e.g. feed sulfur concentration) are unknown. The objective of this thesis is to overcome these limitations of data based predictors and develop an online product sulfur predictor for HDS unit. In this thesis, a hybrid model is proposed, developed and validated (using industrial data), which could predict product sulfur concentration for online HDS system. The proposed hybrid structure is a combination of a reaction kinetics based HDS reactor model and an empirical model based on support vector regression (SVR). The mechanistic model runs in off-line mode to estimate the feed sulfur concentration while the data based model uses the estimated feed sulfur concentration and other process variables to predict the product sulfur concentration. The predicted sulfur concentration can be compared with the lab measurements or sulfur analyzer located further downstream of the process at the tankage. In case there is a large discrepancy, the predictor goes to a calibration mode and uses the mechanistic model to re-estimate the feed sulfur concentration. The detailed logic for the online prediction is also developed. Finally a Matlab based Graphical User Interface (GUI) has been developed for the hybrid sulfur predictor for easy implementation to any HDS process

A microwave cavity resonator sensor for water-in-oil measurements

Online monitoring of Water-Liquid Ratio (WLR) in multiphase flow is key in petroleum production, processing and transportation. The usual practice in the field is to manually collect offline samples for laboratory analysis, which delays data availability and prevents real time intervention and optimization. A highly accurate and robust sensing method is needed for online measurements in the lower end of WLR range (0%–5%), especially for fiscal metering and custody transfer of crude oil, as well as to ensure adequate flow assurance prevention and remedial solutions. This requires a highly sensitive sensing principle along with a highly precise measurement instrument, packaged together in a sufficiently robust manner for use in the field. In this paper, a new sensing principle is proposed, based on the open-ended microwave cavity resonator and near wall surface perturbation, for non-intrusive measurement of WLR. In the proposed concept, the electromagnetic fringe field of a cylindrical cavity resonator is used to probe the liquid near the pipe wall. Two of the cylindrical cavity resonance modes, TM010 and TM011 are energized for measurements and the shift in the resonance frequency is used to estimate liquid permittivity and the WLR. Electromagnetic simulations in the microwave frequency range of 4 GHz to 7 GHz are used for proof-of-concept and sensitivity studies. A sensor prototype is fabricated and its functionality demonstrated with flowing oil-water mixtures in the WLR range of 0–5%. The frequency range of the proposed sensors is 4.4–4.6 GHz and 6.1–6.6 GHz for modes TM010 and TM011, respectively. The TM011 mode shows much higher sensitivity (41.6 MHz/WLR) than the TM010 mode (3.8 MHz/WLR). The proposed sensor consists of a 20 mm high cylinder, with a diameter of 30 mm and Poly-Ether-Ether-Ketone (PEEK) filler. The non-intrusiveness of the sensor, along with the high sensitivity in the resonance shift, makes it attractive for practical applications

Left atrial ball valve thrombus in restrictive cardiomyopathy and normal mitral valve: Loose cannon in heart

AbstractLeft atrial ball valve thrombus is an unusual condition, especially in patients with normal mitral valve. In the present case, we describe a 61-year-old female with restrictive cardiomyopathy who presented with a large left atrial ball valve thrombus, which subsequently embolized to right carotid artery and was treated with intravenous thrombolysis. This case provides useful insight into the genesis of such thrombi and highlights management dilemmas of a rare clinical problem

Netarsudil: a novel intra-ocular pressure lowering agent

Optic disc health is an important indicator of visual functions. The first line of management to prevent/halt the damage to optic disc is to control responsible pathological condition, if identified. In absence of identifiable cause, the most validated approach is lowering of intra-ocular pressure (IOP). Individually, as well as combinations of currently available drugs are not fully effective in all patients of glaucoma in achieving desired IOP control. Hence, there is a need of newer alternatives which address this unmet need. Recently, a newer IOP lowering agent with a novel mechanism of action, netarsudil, has been approved by United States Food and Drug Administration (US-FDA) in December 2017. Netarsudil acts by inhibiting Rho-associated protein kinase resulting in lowering of overall tone of the contractible cells in trabecular meshwork thereby improving drainage of aqueous humor outflow and lowering of IOP. Though in its early days, this drug gives an armamentarium to ophthalmologists and physicians to control IOP in patients of open-angle glaucoma and ocular hypertension

High temperature creep of tungsten free cobalt based Superalloys

The current study reports the high temperature creep behaviour of the recently discovered1,2,3 Co-Al-Mo-Nb/Ta superalloys with additions of Ni, Cr and Ti. These alloys have a classical microstructure, where the L12 ordered is present uniformly throughout the matrix of cobalt in the form of coherent precipitates. The in these alloys, has been stabilized without the addition of tungsten. This results in lower density as well as easier homogenization treatment, since slowly diffusing tungsten is absent. The solvus of these alloys is beyond 1000o C. The alloys exhibit excellent mechanical properties at higher temperatures, with specific strengths that are attractive compared to commercially available current cast polycrystalline cobalt based superalloys. Please click Additional Files below to see the full abstract

Alchemist: Parametric Control of Material Properties with Diffusion Models

We propose a method to control material attributes of objects like roughness, metallic, albedo, and transparency in real images. Our method capitalizes on the generative prior of text-to-image models known for photorealism, employing a scalar value and instructions to alter low-level material properties. Addressing the lack of datasets with controlled material attributes, we generated an object-centric synthetic dataset with physically-based materials. Fine-tuning a modified pre-trained text-to-image model on this synthetic dataset enables us to edit material properties in real-world images while preserving all other attributes. We show the potential application of our model to material edited NeRFs

Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization

We recover a video of the motion taking place in a hidden scene by observing changes in indirect illumination in a nearby uncalibrated visible region. We solve this problem by factoring the observed video into a matrix product between the unknown hidden scene video and an unknown light transport matrix. This task is extremely ill-posed, as any non-negative factorization will satisfy the data. Inspired by recent work on the Deep Image Prior, we parameterize the factor matrices using randomly initialized convolutional neural networks trained in a one-off manner, and show that this results in decompositions that reflect the true motion in the hidden scene.Comment: 14 pages, 5 figures, Advances in Neural Information Processing Systems 201

Development of gas-liquid slug flow measurement using continuous-wave Doppler ultrasound and bandpass power spectral density

This paper addresses the issues of slug detection and characterization in air-water two-phase flow in a vertical pipeline. A novel non-invasive measurement technique using continuous-wave Doppler ultrasound (CWDU) and bandpass power spectral density (BPSD) is proposed for multiphase flow applications and compared with the more established gamma-ray densitometry measurement. In this work, analysis using time-frequency analysis of the CWDU is performed to infer the applicability of the BPSD method for observing the slug front and trailing bubbles in a multiphase flow. The CWDU used a piezo transmitter/receiver pair with an ultrasonic frequency of 500 kHz. Signal processing on the demodulated signal of Doppler frequency was done using the Butterworth bandpass filter on the power spectral density which reveals slugs from background bubbles. The experiments were carried out in the 2” vertical pipeline-riser at the process system engineering laboratory at Cranfield University. The 2-inch test facility used in this experiment is made up of a 54.8 mm internal diameter and 10.5 m high vertical riser connected to a 40 m long horizontal pipeline. Taylor bubbles were generated using a quick-closing air valve placed at the bottom of the riser underwater flow, with rates of 0.5 litres/s, 2 litres/s, and 4 litres/s. The CWDU spectrum of the measured signal along with the BPSD method is shown to describe the distinctive nature of the slug

Seeing 3D Objects in a Single Image via Self-Supervised Static-Dynamic Disentanglement

Human perception reliably identifies movable and immovable parts of 3D scenes, and completes the 3D structure of objects and background from incomplete observations. We learn this skill not via labeled examples, but simply by observing objects move. In this work, we propose an approach that observes unlabeled multi-view videos at training time and learns to map a single image observation of a complex scene, such as a street with cars, to a 3D neural scene representation that is disentangled into movable and immovable parts while plausibly completing its 3D structure. We separately parameterize movable and immovable scene parts via 2D neural ground plans. These ground plans are 2D grids of features aligned with the ground plane that can be locally decoded into 3D neural radiance fields. Our model is trained self-supervised via neural rendering. We demonstrate that the structure inherent to our disentangled 3D representation enables a variety of downstream tasks in street-scale 3D scenes using simple heuristics, such as extraction of object-centric 3D representations, novel view synthesis, instance segmentation, and 3D bounding box prediction, highlighting its value as a backbone for data-efficient 3D scene understanding models. This disentanglement further enables scene editing via object manipulation such as deletion, insertion, and rigid-body motion.Comment: Project page: https://prafullsharma.net/see3d