Performance Modeling and MPI Evaluation Using Westmere-based Infiniband HPC Cluster

2010 ◽  
Author(s):  
Basem Madai ◽  
Raed Al-Shaikh
Keyword(s):  
Performance Modeling

PERFORMANCE MODELING OF PARALLEL-CONNECTED RANQUE-HILSCH VORTEX TUBES USING A GENERALIZABLE AND ROBUST ANN

2020 ◽  
Vol 51 (15) ◽  
pp. 1399-1415
Author(s):  
Hüseyin Kaya ◽  
Volkan Kirmaci ◽  
Hüseyin Avni Es
Keyword(s):  
Performance Modeling ◽  
Vortex Tubes

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

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