scholarly journals Different Tools on Multi-Objective Optimization of a Hybrid Artificial Neural Network – Genetic Algorithm for Plasma Chemical Reactor Modelling

10.5772/38290 ◽  
2012 ◽  
Author(s):  
Nor Aishah Saidina Amin ◽  
I. Istadi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Chemical Reactor ◽  
Multi Objective Optimization ◽  
Plasma Chemical ◽  
Plasma Chemical Reactor ◽  
Multi Objective ◽  
Artificial Neural ◽  
Hybrid Artificial Neural Network

Multi-Objective Optimization on Inlet Pipe of a Vertical Inline Pump Based on Genetic Algorithm and Artificial Neural Network

2018 ◽  
Author(s):  
Xingcheng Gan ◽  
Ji Pei ◽  
Shouqi Yuan ◽  
Wenjie Wang ◽  
Yajing Tang
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Standard Deviation ◽  
Multi Objective Optimization ◽  
Inlet Pipe ◽  
Multi Objective ◽  
Design Variables ◽  
Artificial Neural

In order to save the space for installation, a bent pipe is adopted for inlet of vertical inline pump. In this paper, to improve the performance of inlet pipe, a multi-objective optimization on the inlet pipe is proposed based on Genetic Algorithm (GA) and Artificial Neural Network (ANN) model. A 5th-order Bezier curve is applied to fit the mean line of the inlet pipe and 3rd-order Bezier curves are used for depicting the variation trend of shape of sections. As the outlet of inlet pipe is fixed, 11 design variables are utilized for optimization, and the three optimization objectives are efficiency, head and standard deviation of velocity at the outlet of inlet pipe. To get the surrogate model, 149 different models obtained from Latin hypercube sampling are solved with numerical simulation. The results showed the numerical simulation has a great agreement with the experiment. Artificial neural network can accurately fit the target functions and design variables. The deviation of efficiency, head and standard deviation of velocity between predicted value and actual value were 0.26%, 0.05m and −0.27m/s, respectively. After optimization, an improvement on flow condition and a decrease of standard deviation of velocity before impeller were obtained. The efficiency and head were improved by 1.16% and 0.2m, respectively.

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

Aerodynamic Optimization Design of Compressor Cascade Based on Parallel Multi-Objective Genetic Algorithm and Artificial Neural Network

2011 ◽  
Vol 138-139 ◽  
pp. 534-539
Author(s):  
Li Hai Chen ◽  
Qing Zhen Yang ◽  
Jin Hui Cui
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Optimization Design ◽  
Compressor Cascade ◽  
Aerodynamic Optimization ◽  
Navier Stokes Equation ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Artificial Neural

Genetic algorithm (GA) is improved with fast non-dominated sort approach and crowded comparison operator. A new algorithm called parallel multi-objective genetic algorithm (PMGA) is developed with the support of Massage Passing Interface (MPI). Then, PMGA is combined with Artificial Neural Network (ANN) to improve the optimization efficiency. Training samples of the ANN are evaluated based on the two-dimensional Navier-Stokes equation solver of cascade. To demonstrate the feasibility of the hybrid algorithm, an optimization of a controllable diffusion cascade is performed. The optimization results show that the present method is efficient and trustiness.

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