Development of the ignition delay prediction model of n-butane/hydrogen mixtures based on artificial neural network
Based on the experimental ignition delay results of n-butane/hydrogen mixtures in a rapid compression machine, a Genetic Algorithm (GA) optimized Back Propagation (BP) neural network model is originally developed for ignition delay prediction. In the BP model, the activation function, learning rate...
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doaj-55684159e7d843c485f389c24a2c7f132020-12-13T04:19:54ZengElsevierEnergy and AI2666-54682020-11-012100033Development of the ignition delay prediction model of n-butane/hydrogen mixtures based on artificial neural networkYanqing Cui0Qianlong Wang1Haifeng Liu2Zunqing Zheng3Hu Wang4Zongyu Yue5Mingfa Yao6State Key Laboratory of Engines, Tianjin University, Tianjin 300072, ChinaCorresponding authors.; State Key Laboratory of Engines, Tianjin University, Tianjin 300072, ChinaCorresponding authors.; State Key Laboratory of Engines, Tianjin University, Tianjin 300072, ChinaState Key Laboratory of Engines, Tianjin University, Tianjin 300072, ChinaState Key Laboratory of Engines, Tianjin University, Tianjin 300072, ChinaState Key Laboratory of Engines, Tianjin University, Tianjin 300072, ChinaState Key Laboratory of Engines, Tianjin University, Tianjin 300072, ChinaBased on the experimental ignition delay results of n-butane/hydrogen mixtures in a rapid compression machine, a Genetic Algorithm (GA) optimized Back Propagation (BP) neural network model is originally developed for ignition delay prediction. In the BP model, the activation function, learning rate and the neurons number in the hidden layer are optimized, respectively. The prediction ability of the BP model is validated in wide operating ranges, i.e., compression pressures from 20 to 25 bar, compression temperatures from 722 to 987 K, equivalence ratios from 0.5 to 1.5 and molar ratios of hydrogen (XH2) from 0 to 75%. Compared with the BP model, the GA optimized BP model could increase the average correlation coefficient from 0.9745 to 0.9890, in the opposite, the average Mean Square Error (MSE) decreased from 2.21 to 1.06. On the other hand, to assess the BP-GA model prediction ability in the never-seen-before cases, a limited BP-GA model is fostered in the XH2 range from 0 to 50% to predict the ignition delays at the cases of XH2=75%. It is found that the predicted ignition delays are underestimated due to the training dataset lacking of “acceleration feature” that happened at XH2=75%. However, three possible options are reported to improve the prediction accuracy in such never-seen-before cases.http://www.sciencedirect.com/science/article/pii/S2666546820300331Back propagation (BP) neural networkGenetic algorithm (GA)Ignition delayn-Butane/hydrogen mixtures |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yanqing Cui Qianlong Wang Haifeng Liu Zunqing Zheng Hu Wang Zongyu Yue Mingfa Yao |
spellingShingle |
Yanqing Cui Qianlong Wang Haifeng Liu Zunqing Zheng Hu Wang Zongyu Yue Mingfa Yao Development of the ignition delay prediction model of n-butane/hydrogen mixtures based on artificial neural network Energy and AI Back propagation (BP) neural network Genetic algorithm (GA) Ignition delay n-Butane/hydrogen mixtures |
author_facet |
Yanqing Cui Qianlong Wang Haifeng Liu Zunqing Zheng Hu Wang Zongyu Yue Mingfa Yao |
author_sort |
Yanqing Cui |
title |
Development of the ignition delay prediction model of n-butane/hydrogen mixtures based on artificial neural network |
title_short |
Development of the ignition delay prediction model of n-butane/hydrogen mixtures based on artificial neural network |
title_full |
Development of the ignition delay prediction model of n-butane/hydrogen mixtures based on artificial neural network |
title_fullStr |
Development of the ignition delay prediction model of n-butane/hydrogen mixtures based on artificial neural network |
title_full_unstemmed |
Development of the ignition delay prediction model of n-butane/hydrogen mixtures based on artificial neural network |
title_sort |
development of the ignition delay prediction model of n-butane/hydrogen mixtures based on artificial neural network |
publisher |
Elsevier |
series |
Energy and AI |
issn |
2666-5468 |
publishDate |
2020-11-01 |
description |
Based on the experimental ignition delay results of n-butane/hydrogen mixtures in a rapid compression machine, a Genetic Algorithm (GA) optimized Back Propagation (BP) neural network model is originally developed for ignition delay prediction. In the BP model, the activation function, learning rate and the neurons number in the hidden layer are optimized, respectively. The prediction ability of the BP model is validated in wide operating ranges, i.e., compression pressures from 20 to 25 bar, compression temperatures from 722 to 987 K, equivalence ratios from 0.5 to 1.5 and molar ratios of hydrogen (XH2) from 0 to 75%. Compared with the BP model, the GA optimized BP model could increase the average correlation coefficient from 0.9745 to 0.9890, in the opposite, the average Mean Square Error (MSE) decreased from 2.21 to 1.06. On the other hand, to assess the BP-GA model prediction ability in the never-seen-before cases, a limited BP-GA model is fostered in the XH2 range from 0 to 50% to predict the ignition delays at the cases of XH2=75%. It is found that the predicted ignition delays are underestimated due to the training dataset lacking of “acceleration feature” that happened at XH2=75%. However, three possible options are reported to improve the prediction accuracy in such never-seen-before cases. |
topic |
Back propagation (BP) neural network Genetic algorithm (GA) Ignition delay n-Butane/hydrogen mixtures |
url |
http://www.sciencedirect.com/science/article/pii/S2666546820300331 |
work_keys_str_mv |
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1724385412284153856 |