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  • 1
    Keywords: analytical chemistry ; near-infrared spectroscopy ; NIRS ; interferometry
    Description / Table of Contents: Chapter 1: The NIRS Cap: Key Part of Emerging Wearable Brain-Device Interfaces by Amal Kassab and Mohamad Sawan --- Chapter 2: Near-Infrared Spectroscopy (NIRS): A Novel Tool for Intravascular Coronary Imaging by Marie-Jeanne Bertrand, Philippe Lavoie-L’Allier and Jean-Claude Tardif --- Chapter 3: Highly Sensitive Singlet Oxygen Spectroscopic System Using InGaAs PIN Photodiode by Iwao Mizumoto, Hiroshi Oguma and Yostumi Yoshi --- Chapter 4: Carbohydrate Analysis by NIRS-Chemometrics by Mercedes G. López, Ana Sarahí García-González and Elena Franco- Robles --- Chapter 5: Using Near-Infrared Spectroscopy in Agricultural Systems by Francisco García-Sánchez, Luis Galvez-Sola, Juan J. Martínez- Nicolás, Raquel Muelas-Domingo and Manuel Nieves --- Chapter 6: Near-Infrared Spectroscopy Combined with Multivariate Tools for Analysis of Trace Metals in Environmental Matrices by Philiswa N. Nomngongo, Tshimangadzo S. Munonde, Anele Mpupa and Nkositetile Raphael Biata
    Pages: Online-Ressource (150 Seiten)
    ISBN: 9789535130185
    Language: English
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  • 2
    Publication Date: 2020-08-29
    Description: In a world of fast technological advancements, it is increasingly important to see how hydrocracking applications can benefit from and adapt to digitalization. A review of hydrocracking processes from the perspective of modeling and characterization methods is presented next to an investigation on digitalization trends. Both physics-based and data-based models are discussed according to their scope of use, needs, and capabilities based on open literature. Discrete and continuous lumping, structure-oriented lumping, and single event micro-kinetic models are reported as well as artificial neural networks, convolutional neural networks, and surrogate models. Infrared, near-infrared, ultra-violet and Raman spectroscopic methods are given with their examples for the characterization of feed or product streams of hydrocracking processes regarding boiling point curve, API, SARA, sulfur, nitrogen and metal content. The critical points to consider while modeling the system and the soft sensor are reported as well as the problems to be addressed. Optimization, control, and diagnostics applications are presented together with suggested future directions of interdisciplinary studies. The links required between the models, soft sensors, optimization, control, and diagnostics are suggested to achieve the automation goals and, therefore, a sustainable operation.
    Electronic ISSN: 2071-1050
    Topics: Energy, Environment Protection, Nuclear Power Engineering
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  • 3
    Publication Date: 2019
    Description: The focus of this work is to present the feasibility of lowering the supply and return temperatures of district heating networks in order to achieve energy savings through the implementation of feed-forward model predictive control. The current level of district heating technology dictates a need for higher supply temperatures, which is not the case when considering the future outlook. In part, this can be attributed to the fact that current networks are being controlled by operator experience and outdoor temperatures. The prospects of reducing network temperatures can be evaluated by developing a dynamic model of the process which can then be used for control purposes. Two scenarios are presented in this work, to not only evaluate a controller’s performance in supplying lower network temperatures, but to also assess the boundaries of the return temperature. In Scenario 1, the historical load is used as a feed-forward signal to the controller, and in Scenario 2, a load prediction model is used as the feed-forward signal. The findings for both scenarios suggest that the new control approach can lead to a load reduction of 12.5% and 13.7% respectively for the heat being supplied to the network. With the inclusion of predictions with increased accuracy on end-user demand and feed-back, the return temperature values can be better sustained, and can lead to a decrease in supply temperatures and an increase in energy savings on the production side.
    Electronic ISSN: 1996-1944
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Published by MDPI
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  • 4
    Publication Date: 2017-03-09
    Description: Observation of a discrete time crystal Nature 543, 7644 (2017). doi:10.1038/nature21413 Authors: J. Zhang, P. W. Hess, A. Kyprianidis, P. Becker, A. Lee, J. Smith, G. Pagano, I.-D. Potirniche, A. C. Potter, A. Vishwanath, N. Y. Yao & C. Monroe Spontaneous symmetry breaking is a fundamental concept in many areas of physics, including cosmology, particle physics and condensed matter. An example is the breaking of spatial translational symmetry, which underlies the formation of crystals and the phase transition from liquid to solid. Using the analogy of crystals in space, the breaking of translational symmetry in time and the emergence of a ‘time crystal’ was recently proposed, but was later shown to be forbidden in thermal equilibrium. However, non-equilibrium Floquet systems, which are subject to a periodic drive, can exhibit persistent time correlations at an emergent subharmonic frequency. This new phase of matter has been dubbed a ‘discrete time crystal’. Here we present the experimental observation of a discrete time crystal, in an interacting spin chain of trapped atomic ions. We apply a periodic Hamiltonian to the system under many-body localization conditions, and observe a subharmonic temporal response that is robust to external perturbations. The observation of such a time crystal opens the door to the study of systems with long-range spatio-temporal correlations and novel phases of matter that emerge under intrinsically non-equilibrium conditions.
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Published by Springer Nature
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  • 5
    Publication Date: 1977-11-01
    Print ISSN: 0340-3793
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics , Physics
    Published by Springer
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  • 6
    Publication Date: 1990-11-01
    Print ISSN: 0038-1098
    Electronic ISSN: 1879-2766
    Topics: Physics
    Published by Elsevier
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  • 7
    Publication Date: 1991-07-01
    Print ISSN: 0038-1098
    Electronic ISSN: 1879-2766
    Topics: Physics
    Published by Elsevier
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  • 8
    Publication Date: 2019
    Description: Gas-path diagnostics is an essential part of gas turbine (GT) condition-based maintenance (CBM). There exists extensive literature on GT gas-path diagnostics and a variety of methods have been introduced. The fundamental limitations of the conventional methods such as the inability to deal with the nonlinear engine behavior, measurement uncertainty, simultaneous faults, and the limited number of sensors available remain the driving force for exploring more advanced techniques. This review aims to provide a critical survey of the existing literature produced in the area over the past few decades. In the first section, the issue of GT degradation is addressed, aiming to identify the type of physical faults that degrade a gas turbine performance, which gas-path faults contribute more significantly to the overall performance loss, and which specific components often encounter these faults. A brief overview is then given about the inconsistencies in the literature on gas-path diagnostics followed by a discussion of the various challenges against successful gas-path diagnostics and the major desirable characteristics that an advanced fault diagnostic technique should ideally possess. At this point, the available fault diagnostic methods are thoroughly reviewed, and their strengths and weaknesses summarized. Artificial intelligence (AI) based and hybrid diagnostic methods have received a great deal of attention due to their promising potentials to address the above-mentioned limitations along with providing accurate diagnostic results. Moreover, the available validation techniques that system developers used in the past to evaluate the performance of their proposed diagnostic algorithms are discussed. Finally, concluding remarks and recommendations for further investigations are provided.
    Electronic ISSN: 2226-4310
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Published by MDPI
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  • 9
    Publication Date: 2019
    Description: The correct and early detection of incipient faults or severe degradation phenomena in gas turbine systems is essential for safe and cost-effective operations. A multitude of monitoring and diagnostic systems were developed and tested in the last few decades. The current computational capability of modern digital systems was exploited for both accurate physics-based methods and artificial intelligence or machine learning methods. However, progress is rather limited and none of the methods explored so far seem to be superior to others. One solution to enhance diagnostic systems exploiting the advantages of various techniques is to fuse the information coming from different tools, for example, through statistical methods. Information fusion techniques such as Bayesian networks, fuzzy logic, or probabilistic neural networks can be used to implement a decision support system. This paper presents a comprehensive review of information and decision fusion methods applied to gas turbine diagnostics and the use of probabilistic reasoning to enhance diagnostic accuracy. The different solutions presented in the literature are compared, and major challenges for practical implementation on an industrial gas turbine are discussed. Detecting and isolating faults in a system is a complex problem with many uncertainties, including the integrity of available information. The capability of different information fusion techniques to deal with uncertainty are also compared and discussed. Based on the lessons learned, new perspectives for diagnostics and a decision support system are proposed.
    Electronic ISSN: 2071-1050
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by MDPI
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  • 10
    Publication Date: 1990-04-01
    Print ISSN: 0038-1098
    Electronic ISSN: 1879-2766
    Topics: Physics
    Published by Elsevier
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