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首页> 外文期刊>Circuits and systems >Fault Diagnostics on Steam Boilers and Forecasting System Based on Hybrid Fuzzy Clustering and Artificial Neural Networks in Early Detection of Chamber Slagging/Fouling
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Fault Diagnostics on Steam Boilers and Forecasting System Based on Hybrid Fuzzy Clustering and Artificial Neural Networks in Early Detection of Chamber Slagging/Fouling

机译：基于混合模糊聚类和人工神经网络的汽锅预测系统故障诊断。

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摘要

The slagging/fouling due to the accession of fireside deposits on the steam boilers decreases boiler efficiency and availability which leads to unexpected shut-downs. Since it is inevitably associated with the three major factors namely the fuel characteristics, boiler operating conditions and ash behavior, this serious slagging/fouling may be reduced by varying the above three factors. The research develops a generic slagging/fouling prediction tool based on hybrid fuzzy clustering and Artificial Neural Networks (FCANN). The FCANN model presents a good accuracy of 99.85% which makes this model fast in response and easy to be updated with lesser time when compared to single ANN. The comparison between predictions and observations is found to be satisfactory with less input parameters. This should be capable of giving relatively quick responses while being easily implemented for various furnace types.
机译：由于蒸汽锅炉上沉积了炉边沉积物而导致的结渣/结垢降低了锅炉效率和可用性，从而导致意外停机。由于不可避免地与三个主要因素相关，即燃料特性，锅炉运行条件和灰烬行为，因此通过改变上述三个因素可以减少这种严重的结渣/结垢。该研究开发了一种基于混合模糊聚类和人工神经网络（FCANN）的通用排渣/结垢预测工具。与单个ANN相比，FCANN模型具有99.85％的良好准确性，这使该模型响应速度快且易于更新，且所需时间更少。发现预测和观察值之间的比较在输入参数较少的情况下是令人满意的。这应该能够给出相对快速的响应，同时可以轻松地用于各种类型的熔炉。

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