摘要
采用BP型神经网络对某炼油企业汽油调合数学模型进行研究,依据汽油生产装置特点,确定了神经网络的拓扑结构,利用采集的汽油生产数据,确定了隐含层节点数和模型学习算法,并经过模型训练,得到了拟合能力和预测能力均较强的企业汽油调合神经网络。所建立的模型不需要调合机理的支持,因而具有较强的自适应能力。实际应用结果表明,该神经网络模型对调合过程中的非线性参数预测精度较高,可提供汽油调合的优化方案。
Back-Propagation Neural Network was used to develop the gasoline blending mathematical model for a specific petroleum refinery. Based on the characteristics of its gasoline production units, the topological structure of the neural network was decided. By using real gasoline production data of the refinery, the learning function of the network and the number of neurons in the hidden layers were selected, the model was trained, and a gasoline blending model with reasonable fitting and predicting ability was established. Compared with oth- er blending models, the model established in this paper is much more adaptive because it does not require know- ing blending mechanism. Application results showed that the neural network model could precisely predict those nonlinear parameters like octane number, induction period, etc. , and also provide optimized gasoline blending schemes according to real production data.
出处
《石油炼制与化工》
CAS
CSCD
北大核心
2013年第5期71-75,共5页
Petroleum Processing and Petrochemicals
关键词
神经网络
汽油调合
优化
数学模型
neural network
gasoline blending
optimization
mathematical model
