Abstract:

The traditional functional link artificial neural network (FLANN) is inefficient in the high-dimensional data modeling of the chemical process, where the data has characteristics of multi-dimensional, strongly coupled and noisy. In order to dealing with this problem, the FLANN based on analytical hierarchy process (AHP-FLANN) is proposed. The analytical hierarchy process (AHP) is constructed to filter redundant information and extract characteristic components. And then these characteristic components are trained by the FLANN. Meanwhile, the proposed AHP-FLANN method is applied to analyze the ethylene production data in the chemical industry. Compared with the BP network and the FLANN, the AHP-FLANN has the advantages of fast convergence speed with high modeling accuracy and strong network stability. The experimental result shows that the proposed method can guide the ethylene production conditions and improve the efficiency of energy utilization during ethylene production process. It has the practical value in practice.

Key words: ethylene plant, production capacity forecast, analytic hierarchy process, neural networks, model-predictive control, production

摘要：

针对传统函数链接型神经网络(functional link artificial neural networks,FLANN)不能有效处理化工过程中强耦合、带噪声的高维数据建模问题,提出了一种基于层次分析(analytic hierarchy process, AHP)的FLANN神经网络(AHP-FLANN)。通过层析分析模型过滤输入数据中的冗余信息,提取特征分量,并把提取的特征分量作为函数链接神经网络的输入进行建模。同时利用化工行业乙烯生产数据进行了验证,并和BP神经网络及FLANN神经网络进行了对比。结果表明,AHP-FLANN神经网络在处理复杂高维数据时具有收敛速度快、建模精度高、网络稳定性强等特点,同时能够指导乙烯生产,提高能效,具有良好的实用价值。

关键词: 乙烯装置, 生产能力预测, 层次分析法, 神经网络, 模型预测控制, 生产