scholarly journals A network model of the egg supply chain in Germany implemented as a FSKX compliant object

2021 ◽  
Vol 2 ◽  
Author(s):  
Petra Ganas ◽  
Marcel Fuhrmann ◽  
Matthias Filter
Keyword(s):  
Supply Chain ◽  
Network Model ◽  
Input Parameter ◽  
Knowledge Exchange ◽  
Network Models ◽  
Food Products ◽  
Supply Chain Network ◽  
Global Networks ◽  
Commodity Flow ◽  
Food Supply Chains

In our days, food supply chains are becoming more and more complex, generating global networks involving production, processing, distribution and sale of food products. To follow the "farm to fork" paradigm when assessing risks from various hazards linked to food products, supply chain network models are useful and versatile tools. The objective of the present "egg supply chain network model" is to allow users to predict and visualise the spatial commodity flow within the German egg supply chain. The network model provides for the user the option to select values for the input parameter "actor" in order to allow simulation of estimates for different supply chain scenarios. It generates a data frame as output regarding the estimates of food flows for the product "chicken eggs" in Germany on NUTS-3 level according to the selected parameter and a chloropleth map for illustrating the distribution of product quantities. The network model and all required resources are provided as a fully annotated file compliant to the community standard Food Safety Knowledge Exchange (FSKX) and can be executed online or with the desktop FSK-Lab software.

Halal Food Supply Chain: The Proposed Extension Towards the New Islamic Supply Chain Network Model Development

2018 ◽  
Vol 24 (11) ◽  
pp. 8063-8066
Author(s):  
Adam Mohd Saifudin ◽  
Ezanee Mohamed Elias ◽  
Nizamuddin Zainuddin ◽  
Nik Kamariah Nik Mat
Keyword(s):  
Supply Chain ◽  
Network Model ◽  
Food Supply ◽  
Model Development ◽  
Supply Chain Network ◽  
Food Supply Chain ◽  
Halal Food

Modeling on Hydrocyclone Separation Performance by Neural Network

2011 ◽  
Vol 105-107 ◽  
pp. 185-188
Author(s):  
Feng Qin He ◽  
Ping Zhou ◽  
Jian Gang Wang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Structural Parameters ◽  
Input Parameter ◽  
Network Models ◽  
Separation Performance ◽  
Sampled Data ◽  
Neural Network Models

A 17-27-5 type BP neural network model was built, whose sampled data was got by hydrocyclone separation experiments; another 6-30-5 type BP neural network was also built, whose sampled data came from the simulation results of the LZVV of a hydrocyclone with CFD code FLUENT. The two neural network models also have good predictive validity aimed at hydrocyclone separation performance. It demonstrates LZVV structural parameters can embody hydrocyclone separation performance and reduce input parameter numbers of neural network model. It also indicates that the predictive model of hydrocyclone separation performance can be built by neural network.

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