Parameter identification of proton exchange membrane fuel cell via Levenberg-Marquardt backpropagation algorithm

Yang, Bo; Zeng, Chunyuan; Wang, Long; Guo, Yinyuan; Chen, Guanghua; Guo, Zhengxun; Chen, Yijun; Li, Danyang; Cao, Ping; Shu, Hongchun; Yu, Tao; Zhu, Jiawei

doi:10.1016/j.ijhydene.2021.04.130

Cited by 44 publications

(13 citation statements)

References 51 publications

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“…The results were added from the porosimeter and analyzed by an NLDFT/GCMC program as most suitable for mixed‐matrix membrane characteristics as in 23, 24. The average pore radius was calculated by the program using formulas.…”

Section: Methodsmentioning

confidence: 99%

Removal of Phenol from Sour Water by Poly(vinyl Alcohol)/Polyamide‐Reverse Osmosis Membranes

et al. 2022

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A commercial thin‐film membrane (unmodified) of polyamide improved by poly(vinyl alcohol) (PVA)‐coated membranes (modified) was investigated for separating phenol in sour water by reverse osmosis. Dependences of pressure and pore surface area on flux and phenol rejection were tested. A graphical correlation was found between the relative flux decline and phenol concentration decrease in the feed. The modified membrane provided rejection of 86 % at 2 bar with the highest permeate flux of 8.46 L m−2h−1. The average contact angle for the former membrane was 58.4°, while that for the modified membrane was 49.1°. The reduction in contact angle enhances the surface hydrophilicity of the membrane leading to the antifouling effect. The modified membrane provides 95.4 % flux recovery compared to the unmodified membrane that provides only 66.2 %.

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Section: Methodsmentioning

confidence: 99%

Removal of Phenol from Sour Water by Poly(vinyl Alcohol)/Polyamide‐Reverse Osmosis Membranes

et al. 2022

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“…e RNN forward propagation algorithm is used as the basis, and the RNN backpropagation algorithm is calculated. e process of the RNN backpropagation algorithm is to calculate the gradient of each parameter of the model, that is, the gradients of U, W, V, b, and c through the transferred property of the gradient descent error [18]. e loss function is set to the crossentropy loss function L, the output activation function is the softmax function, and the activation in the hidden layer is the tanh function.…”

Section: Backpropagation Algorithmmentioning

confidence: 99%

Educational Evaluation of Piano Performance by the Deep Learning Neural Network Model

Liao

2022

Mobile Information Systems

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In recent years, the piano education industry has occupied a huge market. However, the automatic evaluation function of piano performance has shortcomings in existing piano education products. Deep learning (DL) algorithm and the recurrent neural network (RNN) structure can help in automatics evaluation function of the piano performance. This paper proposes a Musical Instrument Digital Interface (MIDI) piano evaluation scheme based on the RNN structure and the Spark computing engine using the Deeplearning4J DL framework. The Deeplearning4J framework can run on the Java Virtual Machine; therefore, the entire system does not require cross-platform development. The Spark distributed computing engine realizes parallelization in music data preprocessing, feature extraction, and model training. Combined with the training user interface (UI) provided by the Deeplearning4J, it can improve developmental efficiency. Additionally, the RNN parameters are analyzed. The results demonstrate that the error value of the three-layer RNN structure is smaller than other closest rivals’ techniques. In particular, few piano training institutions and MIDI website datasets are used as the basis, and the experimental samples are collected. The neural network is trained, and the performance of the evaluation model is tested. The results show that the evaluation outcomes of the designed performance evaluation model for the piano are fundamentally consistent with the real levels of the players with assured feasibility; after 3k times of the training periods, the error of the RNN model is close to 0.01 and the network converges.

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“…As a new type of chemical power source, the fuel cell generation process is not a direct combustion of fuel compared to thermal power generation, the power generation efficiency is not limited by the Carnot cycle and the emission of harmful substances is extremely low (Yang et al, 2020;Yang et al, 2018). Its energy conversion rate is as high as 80 %, and its actual efficiency is double that of an ordinary internal combustion engine (Bougrine et al, 2013) The fuel cell is therefore a new power source with high efficiency and clean features, combining new technologies in energy, chemicals, materials and automatic control (Yang et al, 2019a;Yang et al, 2021c).…”

Section: Introductionmentioning

confidence: 99%

Control Method for PEMFC Using Improved Deep Deterministic Policy Gradient Algorithm

2021

Front. Energy Res.

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A data-driven PEMFC output voltage control method is proposed. Moreover, an Improved deep deterministic policy gradient algorithm is proposed for this method. The algorithm introduces three techniques: Clipped multiple Q-learning, policy delay update, and policy smoothing to improve the robustness of the control policy. In this algorithm, the hydrogen controller is treated as an agent, which is pre-trained to fully interact with the environment and obtain the optimal control policy. The effectiveness of the proposed algorithm is demonstrated experimentally.

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Parameter identification of proton exchange membrane fuel cell via Levenberg-Marquardt backpropagation algorithm

Cited by 44 publications

References 51 publications

Removal of Phenol from Sour Water by Poly(vinyl Alcohol)/Polyamide‐Reverse Osmosis Membranes

Removal of Phenol from Sour Water by Poly(vinyl Alcohol)/Polyamide‐Reverse Osmosis Membranes

Educational Evaluation of Piano Performance by the Deep Learning Neural Network Model

Control Method for PEMFC Using Improved Deep Deterministic Policy Gradient Algorithm

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