DETERMINATION OF STATISTICAL DISTRIBUTION OF INPUT PARAMETERS OF THE QUEUING SYSTEM IN THE STORAGE SYSTEM

2019 ◽  
Keyword(s):  
Storage System ◽  
Statistical Distribution ◽  
Queuing System ◽  
Input Parameters

Analysis of methodologies for determination of DEM input parameters

2021 ◽  
Author(s):  
Rondinelli M. Lima ◽  
Rodolfo J. Brandao ◽  
Raphael L. Santos ◽  
Claudio R. Duarte ◽  
Marcos A. S. Barrozo
Keyword(s):  
Input Parameters

Archiving of Food Samples from Restaurants and Caterers—Quantitative Profiling of Outbreaks of Foodborne Salmonellosis in Japan

2004 ◽  
Vol 67 (9) ◽  
pp. 2024-2032 ◽  
Author(s):  
FUMIKO KASUGA ◽  
MASAMITSU HIROTA ◽  
MASAMICHI WADA ◽  
TOSHIHIKO YUNOKAWA ◽  
HAJIME TOYOFUKU ◽  
...  
Keyword(s):  
Dose Response ◽  
Storage System ◽  
Epidemiological Data ◽  
Food Samples ◽  
Food Storage ◽  
Disease Rate ◽  
Data Sets ◽  
Response Data ◽  
Quantitative Profiling

The Ministry of Health, Labor and Welfare (former MHW) of Japan issued a Directive in 1997 advising restaurants and caterers to freeze portions of both raw food and cooked dishes for at least 2 weeks. This system has been useful for determining vehicle foods at outbreaks. Enumeration of bacteria in samples of stored food provide data about pathogen concentrations in the implicated food. Data on Salmonella concentrations in vehicle foods associated with salmonellosis outbreaks were collected in Japan between 1989 and 1998. The 39 outbreaks that occurred during this period were categorized by the settings where the outbreaks took place, and epidemiological data from each outbreak were summarized. Characteristics of outbreak groups were analyzed and compared. The effect of new food-storage system on determination of bacterial concentration was evaluated. Freezing and nonfreezing conditions prior to microbial examination were compared in the dose-response relationship. Data from outbreaks in which implicated foods had been kept frozen suggested apparent correlation between the Salmonella dose ingested and the disease rate. Combined with results of epidemiological investigation, quantitative data from the ingested pathogen could provide complete dose-response data sets.

scholarly journals Design of a Test Platform for the Determination of Lithium-Ion Batteries State of Health

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Jules-Adrien Capitaine ◽  
Qing Wang
Keyword(s):  
Lithium Ion Batteries ◽  
Storage System ◽  
Lithium Ion ◽  
Energy Storage System ◽  
State Of Health ◽  
Battery Cell ◽  
Test Platform ◽  
Battery Energy ◽  
Novel Design

This paper presents a novel design for a test platform to determine the state of health (SOH) of lithium-ion batteries (LIBs). The SOH is a key parameter of a battery energy storage system and its estimation remains a challenging issue. The batteries that have been tested are 18,650 Li-ion cells as they are the most commonly used batteries on the market. The test platform design is detailed from the building of the charging and discharging circuitry to the software. Data acquired from the testing circuitry are stored and displayed in LabVIEW to obtain the charging and discharging curves. The resulting graphs are compared to the outcome predicted by the battery datasheets, to verify that the platform delivers coherent values. The SOH of the battery is then calculated using a Coulomb counting method in LabVIEW. The batteries will be discharged through various types of resistive circuits, and the differences in the resulting curves will be discussed. A single battery cell will also be tested over 30 cycles and the decrease in the SOH will be clearly identified.

Device Modeling of a-Si:H Alloy Solar Cells: Calibration Procedure for Determination of Model Input Parameters

1998 ◽  
Vol 507 ◽  
Author(s):  
M. Zeman ◽  
R.A.C.M.M. Van Swaaij ◽  
E. Schroten ◽  
L.L.A. Vosteen ◽  
J.W. Metselaar
Keyword(s):  
Solar Cell ◽  
Material Properties ◽  
Calibration Procedure ◽  
Model Parameters ◽  
Model Input ◽  
A Value ◽  
Input Parameters ◽  
Simulated Material ◽  
Good Agreement

ABSTRACTA calibration procedure for determining the model input parameters of standard a-Si:H layers, which comprise a single junction a-Si:H solar cell, is presented. The calibration procedure consists of: i) deposition of the separate layers, ii) measurement of the material properties, iii) fitting the model parameters to match the measured properties, iv) simulation of test devices and comparison with experimental results. The inverse modeling procedure was used to extract values of the most influential model parameters by fitting the simulated material properties to the measured ones. In case of doped layers the extracted values of the characteristic energies of exponentially decaying tail states are much higher than the values reported in literature. Using the extracted values of model parameters a good agreement between the measured and calculated characteristics of a reference solar cell was reached. The presented procedure could not solve directly an important issue concerning a value of the mobility gap in a-Si:H alloys.

scholarly journals Design of a Test Platform for the Determination of Lithium-Ion Batteries State-of-Health

2018 ◽  
Author(s):  
Jules-Adrien Capitaine ◽  
Qing Wang
Keyword(s):  
Lithium Ion Batteries ◽  
Storage System ◽  
Lithium Ion ◽  
Energy Storage System ◽  
State Of Health ◽  
Battery Cell ◽  
Test Platform ◽  
Battery Energy ◽  
Novel Design

This paper presents a novel design for a test platform to determine the State of Health (SOH) of lithium-ion batteries. The SOH is a key parameter of a battery energy storage system and its estimation remains a challenging issue. The batteries that have been tested are 18650 li-ion cells as they are the most commonly used batteries on the market. The test platform design is detailed from the building of the charging and discharging circuitry to the software. Data acquired from the testing circuitry is stored and displayed in LabView to obtain charging and discharging curves. The resulting graphs are compared to the outcome predicted by the battery datasheets, to verify the platform delivers coherent values. The SOH of the battery is then calculated using a Coulomb Counting method in LabView. The batteries will be discharged through various types of resistive circuits, and the differences in the resulting curves will be discussed. A single battery cell will also be tested over 30 cycles and the decrease in the SOH will be clearly pointed out.

Modelling of tribological responses of composites using integrated ANN-GA technique

2020 ◽  
pp. 002199832096052
Author(s):  
Santanu Sardar ◽  
Swati Dey ◽  
Debdulal Das
Keyword(s):  
Optimization Technique ◽  
Matrix Composites ◽  
Large Numbers ◽  
Wide Range ◽  
Input Parameters ◽  
Input Variables ◽  
Velocity Input ◽  
Abrasive Size

In the present article, artificial neural networks (ANNs) and genetic algorithm (GA) methodology were integrated to model tribological characteristics of stir-cast Al-Zn-Mg-Cu matrix composites under two-body abrasion considering large numbers of experimentally generated results. Tribo-responses of wear rate (Wrt), coefficient of friction (COF) and roughness of abraded surface (RAS) were evaluated under wide range of intrinsic ( i.e., particle quantity) and extrinsic ( i.e., abrasive size, load, distance and velocity) input parameters. Characteristics of Wrt, COF and RAS are often mutually contradictory in nature and so, multi-objective optimization technique becomes imperative for selection and design of machine components. Accordingly, those were optimized through Pareto solutions. Sensitivity of different factors was analyzed on each of the tribo-performances and validated via experimental evidences. Amongst the input variables, particle quantity and abrasive size dominated significantly over other variables except load which imparted modest influences. The role of various input parameters was explained through determination of different micromechanisms via exhaustive post wear characterizations, microstructural and surface topography attributes. Lowest values of Wrt and COF with a modest value of RAS were identified at 15 ± 2 wt.% particle quantity.

Sign in / Sign up

Export Citation Format

Share Document