Void fraction measurement method of gas-liquid two-phase flow based on IPSO-Elman
In order to obtain non-invasive measurement results of gas-liquid two-phase flow gas holdup safely,a method of measuring the void fraction based on electrical resistance tomography(ERT)array resistance value and Elman neural network was proposed.Firstly,in order to accelerate the training speed of model and avoid redundancy of data,principal component analysis(PCA)algorithm was used to reduce the dimension of the resistance feature of the 120-dimensional array.Then,the adaptive inertia weights and nonlinear learning factors were introduced into the particle swarm optimization(PSO)algorithm,and the crossover and mutation behaviors of genetic algorithm(GA)were added to improve the rate of convergence of the algorithm.Finally,the initial weights and thresholds of Elman neural network were optimized by improved particle swarm optimization(IPSO)algorithm,and gas holdup measurement model was established.Through the comparison experiment,it is found that the mean absolute percentage error of the gas holdup measurement model named PCA-IPSO-Elman is 2.92%and the training time of the model is reduced by 68.8%compared with IPSO-Elman model,which manifests that the proposed method can achieve the expected measurement effect.
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