Case Study on Tubular Reactor Hot-Spot Temperature Control for Throughput Maximization

In electrical competitive markets, where deregulation and privatisation have determined changes in the organizational structures of the electricity supply industry as well as in the operation of power systems, utilities necessitate to change dynamically the loadability rating of power components without penalizing their serviceability. When assessing network load capability, the prediction of the Hot Spot Temperature (HST) of power components represents the most critical factor since it is essential to assess the thermal stress of the components, the loss of insulation life and the consequent risks of both technical and economical nature. In this chapter a general adaptive framework for power components dynamic loadability is proposed. In order to estimate the effectiveness of the adaptive framework, based on grey-box modelling, a specific case study, concerning the problem of forecasting the HST of a mineral-oil-immersed transformer, is presented.

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Modeling of the Winding Hot-Spot Temperature in Power Transformers: Case Study of the Low-Loaded Fleet

Energies ◽

10.3390/en12183561 ◽

2019 ◽

Vol 12 (18) ◽

pp. 3561 ◽

Cited By ~ 5

Author(s):

Kunicki ◽

Borucki ◽

Cichoń ◽

Frymus

Keyword(s):

Hot Spot ◽

Electrical Power ◽

Real Life ◽

Power Transformers ◽

Measuring Systems ◽

On Line ◽

Method Accuracy ◽

Technical Assessment ◽

Spot Temperature

A proposal of the dynamic thermal rating (DTR) applied and optimized for low-loaded power transformers equipped with on-line hot-spot (HS) measuring systems is presented in the paper. The proposed method concerns the particular population of mid-voltage (MV) to high-voltage (HV) transformers, a case study of the population of over 1500 units with low average load is analyzed. Three representative real-life working units are selected for the method evaluation and verification. Temperatures used for analysis were measured continuously within two years with 1 h steps. Data from 2016 are used to train selected models based on various machine learning (ML) algorithms. Data from 2017 are used to verify the trained models and to validate the method. Accuracy analysis of all applied ML algorithms is discussed and compared to the conventional thermal model. As a result, the best accuracy of the prediction of HS temperatures is yielded by a generalized linear model (GLM) with mean prediction error below 0.71% for winding HS. The proposed method may be implemented as a part of the technical assessment decision support systems and freely adopted for other electrical power apparatus after relevant data are provided for the learning process and as predictors for trained models.

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Pyrolysis of brown algae (Bifurcaria Bifurcata) in a stainless steel tubular reactor: From a review to a case study

Progress in Agricultural Engineering Sciences ◽

10.1556/446.14.2018.1.2 ◽

2018 ◽

Vol 14 (1) ◽

pp. 31-60 ◽

Cited By ~ 1

Author(s):

M. Y. Guida ◽

F. E. Laghchioua ◽

A. Hannioui

Keyword(s):

Particle Size ◽

Stainless Steel ◽

Flow Rate ◽

Brown Algae ◽

Tubular Reactor ◽

Nitrogen Flow ◽

Nitrogen Flow Rate ◽

Bifurcaria Bifurcata ◽

Bio Oil

This article deals with fast pyrolysis of brown algae, such as Bifurcaria Bifurcata at the range of temperature 300–800 °C in a stainless steel tubular reactor. After a literature review on algae and its importance in renewable sector, a case study was done on pyrolysis of brown algae especially, Bifurcaria Bifurcata. The aim was to experimentally investigate how the temperature, the particle size, the nitrogen flow rate (N2) and the heating rate affect bio-oil, bio-char and gaseous products. These parameters were varied in the ranges of 5–50 °C/min, below 0.2–1 mm and 20–200 mL. min–1, respectively. The maximum bio-oil yield of 41.3wt% was obtained at a pyrolysis temperature of 600 °C, particle size between 0.2–0.5 mm, nitrogen flow rate (N2) of 100 mL. min–1 and heating rate of 5 °C/min. Liquid product obtained under the most suitable and optimal condition was characterized by elemental analysis, 1H-NMR, FT-IR and GC-MS. The analysis of bio-oil showed that bio-oil from Bifurcaria Bifurcata could be a potential source of renewable fuel production and value added chemicals.

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Application of Lock-in Thermography on PCB for Fault Localization and Validation of Failure Mechanism Due to External Discrete Component Variation

ISTFA 2010: Conference Proceedings from the 36th International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2010p0191 ◽

2010 ◽

Author(s):

William Ng ◽

Kevin Weaver ◽

Zachary Gemmill ◽

Herve Deslandes ◽

Rudolf Schlangen

Keyword(s):

Failure Mechanism ◽

Integrated Circuit ◽

Printed Circuit Board ◽

Hot Spot ◽

Circuit Board ◽

Discrete Component ◽

Printed Circuit ◽

Thermal Signature ◽

Lock In

Abstract This paper demonstrates the use of a real time lock-in thermography (LIT) system to non-destructively characterize thermal events prior to the failing of an integrated circuit (IC) device. A case study using a packaged IC mounted on printed circuit board (PCB) is presented. The result validated the failing model by observing the thermal signature on the package. Subsequent analysis from the backside of the IC identified a hot spot in internal circuitry sensitive to varying value of external discrete component (inductor) on PCB.

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Development and Evaluation of Geostatistical Methods for Estimating Weather Related Collisions: A Large-Scale Case Study

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211020008 ◽

2021 ◽

pp. 036119812110200

Author(s):

Andy H. Wong ◽

Tae J. Kwon

Keyword(s):

Large Scale ◽

Mean Squared Error ◽

Hot Spot ◽

High Ratio ◽

Contributing Factors ◽

Geostatistical Methods ◽

Road Users ◽

Statistical Measures ◽

Weather Events

Winter driving conditions pose a real hazard to road users with increased chance of collisions during inclement weather events. As such, road authorities strive to service the hazardous roads or collision hot spots by increasing road safety, mobility, and accessibility. One measure of a hot spot would be winter collision statistics. Using the ratio of winter collisions (WC) to all collisions, roads that show a high ratio of WC should be given a high priority for further diagnosis and countermeasure selection. This study presents a unique methodological framework that is built on one of the least explored yet most powerful geostatistical techniques, namely, regression kriging (RK). Unlike other variants of kriging, RK uses auxiliary variables to gain a deeper understanding of contributing factors while also utilizing the spatial autocorrelation structure for predicting WC ratios. The applicability and validity of RK for a large-scale hot spot analysis is evaluated using the northeast quarter of the State of Iowa, spanning five winter seasons from 2013/14 to 2017/18. The findings of the case study assessed via three different statistical measures (mean squared error, root mean square error, and root mean squared standardized error) suggest that RK is very effective for modeling WC ratios, thereby further supporting its robustness and feasibility for a statewide implementation.

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