This study evaluates emission characteristics of volatile organic compounds (VOCs) caused by standing loss (L S) and working loss (L W) of three vertical fixed-roof p-xylene (p-X) liquid tanks during 1-year storage and filling operation. The annual net throughput of the tanks reached 70,446 t, resulting in 9,425 kg of p-X vapor emission including 5,046 kg of L S (53.54 %) and 4,379 kg of L W (46.46 %). The estimated L W of AP-42 displayed better agreement with the measured values of a VOC detector than the estimated L S of AP-42. The L S was best correlated with the liquid height of the tanks, while the L W was best correlated with the net throughput of the tanks. As a result, decreasing vapor space volume of the tanks and avoiding high net throughput of the tanks in a high ambient temperature period were considered as effective means to lessen VOC emission from the fixed-roof organic liquid storage tank.
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http://dx.doi.org/10.1007/s10661-013-3067-9 | DOI Listing |
Anal Sci
January 2025
Graduate School of Pharmaceutical Sciences, Tokushima University, 1-78-1 Shomachi, Tokushima, 770-8505, Japan.
A digital-movie-based flow colorimetry for pH measurement using a universal indicator has been applied to the end point detection of acid-base titrations. A two-channel flow system of feedback-based flow ratiometry, primarily consisting of two peristaltic pumps, a digital microscope-based detector, and a laptop computer, was constructed; a Visual Basic.NET program written in-house was used for automating the analytical processes.
View Article and Find Full Text PDFNeural Netw
December 2024
Department of Earth Science and Engineering, Imperial College London, Prince Consort Road, London SW7 2BP, UK; Centre for AI-Physics Modelling, Imperial-X, White City Campus, Imperial College London, W12 7SL, UK.
Machine learning (ML) has benefited from both software and hardware advancements, leading to increasing interest in capitalising on ML throughout academia and industry. There have been efforts in the scientific computing community to leverage this development via implementing conventional partial differential equation (PDE) solvers with machine learning packages, most of which rely on structured spatial discretisation and fast convolution algorithms. However, unstructured meshes are favoured in problems with complex geometries.
View Article and Find Full Text PDFComput Med Imaging Graph
January 2025
The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China; Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai, China. Electronic address:
Parkinson disease (PD) is a prevalent neurodegenerative disorder, and its accurate diagnosis is crucial for timely intervention. We propose the PArkinson disease Denoising and Segmentation Network (PADS-Net), to simultaneously denoise and segment transcranial ultrasound images of midbrain for accurate PD diagnosis. The PADS-Net is built upon generative adversarial networks and incorporates a multi-task deep learning framework aimed at optimizing the tasks of denoising and segmentation for ultrasound images.
View Article and Find Full Text PDFPhytomedicine
February 2025
Animal-Derived Food Safety Innovation Team, College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, PR China. Electronic address:
Background: Widespread bacterial infection and the spread of multidrug resistance (MDR) exhibit increasing threats to the public and thus require new antibacterial strategies. Coupled with the current slow pace of antibiotic development, the use of antibiotic adjuvants to revitalize existing antibiotics offers great potential.
Purpose: We aim to explore the synergistic antimicrobial mechanism of glabrol (GLA) and colistin (COL) while developing an innovative multifunctional micelle-based drug delivery system to enhance therapeutic efficacy.
Sci Rep
January 2025
vivoVerse, LLC, Austin, TX, 78731, USA.
Developmental toxicity (DevTox) tests evaluate the adverse effects of chemical exposures on an organism's development. Although current testing primarily relies on large mammalian models, the emergence of new approach methodologies (NAMs) is encouraging industries and regulatory agencies to evaluate novel assays. C.
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