Predictive models for PEM-electrolyzer performance using adaptive neuro-fuzzy inference systems
Predictive models for PEM-electrolyzer performance using adaptive neuro-fuzzy inference systems
复制标题
使用自适应神经模糊推理系统的 PEM 电解槽性能预测模型
DOI：
10.1016/j.ijhydene.2009.11.060
复制
发表时间：
2010
影响因子：
7.2
通讯作者：
Vishy Karri
中科院分区：
文献类型：
--
作者：
Steffen Becker;Vishy Karri
采用基于神经网络的自适应神经模糊推理系统分别建立了氢气流量、电解槽系统效率和电堆效率的预测模型。一个全面的实验数据库构成了预测模型的基础。有人认为，由于与氢测量设备相关的高成本，这些可靠的预测模型可以作为虚拟传感器来实现。这些模型也可用于在线监测和氢气设备的安全。预测模型的定量准确性评估使用统计技术。这些数学模型被认为是可靠的预测工具，与实验值相比，具有±3%的优异精度。这些模型的预测性质没有显示出任何显著的偏置，无论是预测或预测不足。这些预测模型建立在良好的数学和定量基础上，可以被视为建立氢性能预测模型作为通用虚拟传感器的一个步骤，用于更广泛的安全和监测应用。
Predictive models were built using neural network based Adaptive Neuro-Fuzzy Inference Systems for hydrogen flow rate, electrolyzer system-efficiency and stack-efficiency respectively. A comprehensive experimental database forms the foundation for the predictive models. It is argued that, due to the high costs associated with the hydrogen measuring equipment; these reliable predictive models can be implemented as virtual sensors. These models can also be used on-line for monitoring and safety of hydrogen equipment. The quantitative accuracy of the predictive models is appraised using statistical techniques. These mathematical models are found to be reliable predictive tools with an excellent accuracy of ±3% compared with experimental values. The predictive nature of these models did not show any significant bias to either over prediction or under prediction. These predictive models, built on a sound mathematical and quantitative basis, can be seen as a step towards establishing hydrogen performance prediction models as generic virtual sensors for wider safety and monitoring applications.