Development of Ni catalysts with low metal content and improved stability in the dry reforming of methane to syngas

Catalysts with 2.5 wt% Ni supported on MgO-Al2O3 were developed for CH4 dry reform-ing (DRM) that are stable at either mild (stoichiometric) or severe (CH4-rich) conditions. Solid materials were characterized with suitable methods. Catalysts with NiO-MgO-Al2O3 solid solution were active even at 500 °C. In stoichiometric DRM, Ni catalyst modified with La and citric acid reduced both Ni re-oxidation and agglomeration, preserving high activity over 160 h. Besides, proper support pre-treatment or adding Gd3+ both offered outstandingly coking-resistant Ni-based catalysts for CH4-rich DRM over 100 h.Für das trockene Reformieren (DRM) wurden Katalysatoren mit niedrigem Ni-Gehalt (2,5 Gew.-%) auf Mg-Al-Mischoxid entwickelt, die stabil bei stöchiometrischen oder CH4-reichen Bedingungen waren. Die Materialien wurden charakterisiert. Katalysatoren mit festen NiO-MgO-Al2O3-Lösungen waren schon bei 500 °C aktiv. Im stöchiometrischen DRM verringerte die Modifikation mit La/Zitronensäure die Reoxidation/Agglomeration von Ni und die Katalysatorstandzeit erreichte 160 h. Geeignete Trägervorbehandlung oder Gd3+-Dotierung führte zu stabilen und aktiven Ni-Katalysatoren für das CH4-reiche DRM über 100 h

Impact of Image Denoising Techniques on CNN-based Liver Vessel Segmentation using Synthesis Low-dose Contrast Enhanced CT Images

Liver vessel segmentation in contrast enhanced CT (CECT) image is relevant for several clinical applications. However, the liver segmentation on noisy images obtain incorrect liver vessel segmentation which may lead to distortion in the simulation of cooling effect near the vessels during the planning. In this study, we present a framework that consists of three well-known and state-of-the-art denoising techniques, Vesssel enhancing diffusion (VED), RED-CNN, and MAP-NN and using a state-of-the-art Convolution Neural Networks (nn-Unet) to segment the liver vessels from the CECT images. The impact of denoising methods on the vessel segmentation are ablated using with multi-level simulated low-dose CECT of the liver. The experiment is carried on CECT images of the liver from two public and one private datasets. We evaluate the performance of the framework using Dice score and sensitivity criteria. Furthermore, we investigate the efficient of denoising on roughness of the surface of liver vessel segmentation. The results from our experiment suggest that denoising methods can improve the liver vessel segmentation quality in the CECT image with high low-dose noise while they degrade the liver vessel segmentation accuracy for low-noise-level CECT images

Non-rigid registration of liver ct images for ct-guided ablation of liver tumors

CT-guided percutaneous ablation for liver cancer treatment is a relevant technique for patients not eligible for surgery and with tumors that are inconspicuous on US imaging. The lack of real-time imaging and the use of a limited amount of CT contrast agent make targeting the tumor with the needle challenging. In this study, we evaluate a registration framework that allows the integration of diagnostic pre-operative contrast enhanced CT images and intra-operative non-contrast enhanced CT images to improve image guidance in the intervention. The liver and tumor are segmented in the pre-operative contrast enhanced CT images. Next, the contrast enhanced image is registered to the intra-operative CT images in a two-stage approach. First, the contrast-enhanced diagnostic image is non-rigidly registered to a non-contrast enhanced image that is conventionally acquired at the start of the intervention. In case the initial registration is not sufficiently accurate, a refinement step is applied using non-rigid registration method with a local rigidity term. In the second stage, the intra-operative CT-images that are used to check the needle position, which often consist of only a few slices, are registered rigidly to the intra-operative image that was acquired at the start of the intervention. Subsequently, the diagnostic image is registered to the current intra-operative image, using both transformations, this allows the visualization of the tumor region extracted from pre-operative data in the intra-operative CT images containing needle. The method is evaluated on imaging data of 19 patients at the Erasmus MC. Quantitative evaluation is performed using the Dice metric, mean surface distance of the liver border and corresponding landmarks in the diagnostic and the intra-operative images. The registration of the diagnostic CT image to the initial intra-operative CT image did not require a refinement step in 13 cases. For those cases, the resulting registration had a Dice coefficient for the livers of 91.4%, a mean surface distance of 4.4 mm and a mean distance between corresponding landmarks of 4.7 mm. For the three cases with a refinement step, the registration result significantly improved (p<0.05) compared to the result of the initial non rigid registration method (DICE of 90.3% vs 71.3% and mean surface distance of 5.1 mm vs 11.3 mm and mean distanc

Development of Highly Stable Low Ni Content Catalyst for Dry Reforming of CH4-Rich Feedstocks

Highly active and coking-resistant Ni catalysts suited for the dry reforming of CH4-rich gases (70 vol %, e. g. biogas or sour natural gas) were prepared starting from a Mg-rich Mg−Al hydrotalcite support precursor. Calcination at 1000 °C yields two phases, MgO and MgAl2O4 spinel. Complexation-deposition of Ni with citric acid on the preformed support as well as lanthanum addition yields a catalyst with remarkably low carbon accumulation over 100 h on stream attributed to both high Ni dispersion and preferred interactions of Ni with MgO on MgAl2O4. © 2020 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA

Accuracy of semi-automated versus manual localisation of liver tumours in CT-guided ablation procedures

Objectives: To compare the accuracy of liver tumour localisation in intraprocedural computed tomography (CT) images of computer-based rigid registration or non-rigid registration versus mental registration performed by interventional radiologists. Methods: Retrospectively (2009-2017), 35 contrast-enhanced CT (CECT) images incorporating 56 tumours, acquired during CT-guided ablation procedures and their corresponding pre-procedural diagnostic CECTs were retrieved from the picture archiving and communication system (PACS). The original intraprocedural CECTs were de-enhanced to create a virtually unenhanced CT image (VUCT). Alignment of diagnostic CECTs to their corresponding intraprocedural VUCTs was performed with non-rigid or rigid registration. Mental registration was performed by four interventional radiologists. The original intraprocedural CECT served as the reference standard. Accuracy of tumour localisation was assessed with the target registration error (TRE). Statistical differences were analysed with the Wilcoxon sig

Development of Active and Stable Low Nickel Content Catalysts for Dry Reforming of Methane

Methane dry reforming (DRM) was investigated over highly active Ni catalysts with low metal content (2.5 wt %) supported on Mg-Al mixed oxide. The aim was to minimize carbon deposition and metal sites agglomeration on the working catalyst which are known to cause catalyst deactivation. The solids were characterized using N2 adsorption, X-ray diffraction, temperature-programmed reduction, X-ray photoelectron spectroscopy, and UV-Vis diffuse reflectance spectroscopy. The results showed that MgO-Al2O3 solid solution phases are obtained when calcining Mg-Al hydrotalcite precursor in the temperature range of 550–800 °C. Such phases contribute to the high activity of catalysts with low Ni content even at low temperature (500 °C). Modifying the catalyst preparation with citric acid significantly slows the coking rate and reduces the size of large octahedrally coordinated NiO-like domains, which may easily agglomerate on the surface during DRM. The most effective Ni catalyst shows a stable DRM course over 60 h at high weight hourly space velocity with very low coke deposition. This is a promising result for considering such catalyst systems for further development of an industrial DRM technology

Removal of Nutrients from Fertilizer Plant Wastewater Using Scenedesmus sp.: Formation of Bioflocculation and Enhancement of Removal Efficiency

Eutrophication of surface water has become an environmental concern in recent decades. High concentrations of nutrients, especially nitrogen- and phosphorus-rich species, have contributed to the process of eutrophication, highlighting a demand for effective and economical methods of removing nitrogen and phosphorus from wastewater. This study aimed to investigate the ability of a green microalga species, Scenedesmus sp., to remove nitrogen and phosphorus, as well as chemical oxygen demand (COD) and biochemical oxygen demand (BOD5), from fertilizer plant wastewater. Different microalgae concentrations from 10 mg/L to 60 mg/L were used to assess the growth rate, biomass production, and removal ability. The results indicated that Scenedesmus sp. grew well in the wastewater (with a growth rate from 0.3 to 0.38/day) and produced up to 70.2 mg/L of dry biomass. The algal species was able to remove ammonium (NH4+), nitrate (NO3−), phosphate (PO43−), total phosphorus (TP), COD, and BOD5 with removal rates up to 93%, 84%, 97%, 96%, 93%, and 84%, respectively. Autobioflocculation (AFL) was observed in all cultures with flocculation activity of up to 88.3% in the highest algal biomass treatment. The formation of bioflocculation enhanced the removal of nutrients, COD, and BOD5 from wastewater effluent. The results indicated that wastewater from a fertilizer plant could be used as a cost-effective growth medium for algal biomass. The autoflocculation of microalgae could be used as a more practical approach for wastewater treatment using microalgae to eliminate eutrophication

Liver Segmentation on a Variety of Computed Tomography (CT) Images Based on Convolutional Neural Networks Combined with Connected Components

Liver segmentation is relevant for several clinical applications. Automatic liver segmentation using convolutional neural networks (CNNs) has been recently investigated. In this paper, we propose a new approach of combining a largest connected component (LCC) algorithm, as a post-processing step, with CNN approaches to improve liver segmentation accuracy. Specifically, in this study, the algorithm is combined with three well-known CNNs for liver segmentation: FCN-CRF, DRIU and V-net. We perform the experiment on a variety of liver CT images, ranging from non-contrast enhanced CT images to low-dose contrast enhanced CT images. The methods are evaluated using Dice score, Haudorff distance, mean surface distance, and false positive rate between the liver segmentation and the ground truth. The quantitative results demonstrate that the LCC algorithm statistically significantly improves results of the liver segmentation on non-contrast enhanced and low-dose images for all three CNNs. 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