基于人工神经网络的粉煤灰科学分类

鉴于目前国内在粉煤灰分类方面存在的片面性,我们结合人工神经网络(ANN)理论,提出了基于人工神经网络的粉煤灰科学分类方法。该方法充分考虑了粉煤灰的火山灰活性,并考虑了细度、玻璃体、烧失量、K2O、SO3、CaO多种因素对分类的影响。实例表明,按本文提出的方法建立的网络模型比较合理,精度比较高,克服了以往分类方法的片面性,能比较全面地反映粉煤灰的品质性能,为粉煤灰的多元化利用提供依据。

Artifical Neural Network(ANN)-based Scientific Classification of Fly Ash

For the one-sidedness of classification of fly ash in china, an Artificial Neural Network(ANN)-based scientific classification method of fly ash was proposed in combination with the theory of ANN , The pozzolanic activity of fly ash, fineness, vitric, loss on ignition, K_2O, SO_3 and CaO, which are vital to scientific classification were discussed in this method. The results show that the proposed ANN model is reasonable and the method has overcome the one-sidedness of the former methods and can reflect all-roundly the quality of fly ash, laying a foundation for the diversification utilization of fly ash.