Prediction of void fraction for gas–liquid flow in horizontal, upward and downward inclined pipes using artificial neural network

2016 ◽  
Vol 87 ◽  
pp. 35-44 ◽  
Author(s):  
Sadra Azizi ◽  
Ebrahim Ahmadloo ◽  
Mohamed M. Awad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Void Fraction ◽  
Liquid Flow ◽  
Artificial Neural ◽  
Gas Liquid Flow ◽  
Inclined Pipes

Gas Void Fraction Prediction for Air-Water and Air-Oil Two-Phase Flows via Artificial Neural Network

2019 ◽  
Author(s):  
Tiago Ferreira Souza ◽  
Caio Araujo ◽  
Maurício Figueiredo ◽  
FLAVIO SILVA ◽  
Ana Maria Frattini Fileti
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Void Fraction ◽  
Two Phase Flows ◽  
Two Phase ◽  
Artificial Neural ◽  
Gas Void Fraction

Modeling and Optimization of a Liquid Flow Process using an Artificial Neural Network-Based Flower Pollination Algorithm

2018 ◽  
Vol 29 (1) ◽  
pp. 787-798 ◽  
Author(s):  
Pijush Dutta ◽  
Asok Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flow Rate ◽  
Liquid Flow ◽  
Liquid Flow Rate ◽  
Pipe Diameter ◽  
Flower Pollination Algorithm ◽  
Sensor Output ◽  
Flower Pollination ◽  
Artificial Neural

Abstract Controlling liquid flow is one of the most important parameters in the process control industry. It is challenging to optimize the liquid flow rate for its highly nonlinear nature. This paper proposes a model of liquid flow processes using an artificial neural network (NN) and optimizes it using a flower pollination algorithm (FPA) to avoid local minima and improve the accuracy and convergence speed. In the first phase, the NN model was trained by the dataset obtained from the experiments, which were carried out. In these conditions, the liquid flow rate was measured at different sensor output voltages, pipe diameter and liquid conductivity. The model response was cross-verified with the experimental results and found to be satisfactory. In the second phase of work, the optimized conditions of sensor output voltages, pipe diameter and liquid conductivity were found to give the minimum flow rate of the process using FPA. After cross-validation and testing subdatasets, the accuracy was nearly 94.17% and 99.25%, respectively.

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