基于多模型智能组合算法的锅炉炉膛温度建模

炉膛温度是表征锅炉燃烧状态的重要参数，但是影响炉膛温度的参数多、机理复杂，导致难以建立准确的预测模型。针对这一问题，提出一种多模型智能组合算法(multi-model intelligent combination algorithm, MICA)实现对炉膛温度的建模预测。首先，对实际运行生产数据进行小波降噪，并结合机理分析和分类回归树（classification and regression tree，CART）算法选取预测模型输入参数。然后，通过多种数据驱动方法构建锅炉炉膛温度预测模型。最后，采用决策树C4.5算法建立多模型智能组合预测模型。基于实际生产数据的实验结果表明，所提出算法能够建立准确的炉膛温度预测模型。

Furnace temperature modeling based on multi-model intelligent combination algorithm

Furnace temperature is an important parameter which can reflect boiler combustion status. However, furnace temperature is affected by many parameters and the mechanism is complicated. Therefore, it is difficult to establish accurate prediction model. To solve this problem, a multi-model intelligent combination algorithm (MICA) is proposed to construct an accurate prediction model. First, the actual production data is pre-processed by wavelet denoising algorithm, and the model input variables were selected based on classification and regression trees algorithm and mechanism analysis. Then, several furnace temperature prediction models are constructed by many data-driven algorithms. Finally, a C4.5 algorithm is applied to combine these models into a multi-model intelligent combination model. The experimental results illustrate that the proposed algorithm can construct an accurate furnace temperature prediction model through actual operating data.