关键词: 乙烯装置, 生产能力预测, 模糊C均值聚类, 径向基神经网络, 模型预测控制, 神经网络, 生产

Abstract:

For the conventional radial basis function (RBF) neural network, there are many problems like uncertain nodes in the hidden layer, sensitivity to initial centers and slow convergence speed, etc. This paper proposes an RBF neural network model based on the fuzzy C-means method (FCM-RBF), with each cluster center obtained by fuzzy C-means clustering. And weights between the hidden layer and the output layer are trained by the gradient descent method based on error back-propagation (BP). The proposed method overcomes the sensitivity of the data center for traditional RBF model, determines optimally the number of nodes in the hidden layer of RBF neural network, and improves the network training speed and precision. Finally, the proposed method is applied in the production capacity forecast of the ethylene plants. The production statuses of ethylene plants of different technologies or different scales are analyzed and predicted to guide the ethylene production and improve energy efficiency. The empirical results demonstrate the effectiveness and practicability of the proposed algorithm.

Key words: ethylene plant, production capacity forecast, fuzzy C-means cluster, radial basis function neural network, model-predictive control, neural networks, production