An MDS-based prediction model of RBF for petrochemical industry production capacity

Abstract

The invention discloses an MDS-based prediction model of RBF for petrochemical industry production capacity. The model comprises the following steps: obtaining ethylene data; Carrying out dimension reduction processing on the ethylene data by using a multi-dimensional scale analysis algorithm, so that the distance of the ethylene data in a low-dimensional space is the same as the distance of the ethylene data in a high-dimensional space; Obtaining a corresponding category in the low-dimensional space as a training set and a test set of a radial basis function neural network; Forming a radial basis function neural network prediction model according to the training set and the test set; And predicting the ethylene yield according to the primary function neural network prediction model. According to the technical scheme provided by the invention, the prediction precision of the ethylene production energy efficiency is improved, so that the effective prediction of the energy efficiency ofthe petrochemical industry is realized, the inaccuracy of a traditional neural network model on the prediction of the energy efficiency of the petrochemical industry is solved, the energy efficiency of the complex petrochemical industry is improved, and the purposes of energy conservation and emission reduction are realized.

Description

technical field [0001] The invention relates to the technical field of ethylene production prediction, in particular to an MDS-based RBF prediction model for petrochemical industry production capacity. Background technique [0002] At present, the petrochemical industry is the largest energy-consuming industry in my country. As the "grain of the petrochemical industry", ethylene is the basic organic chemical raw material for synthetic manufacturing materials, synthetic fibers and other products, and is widely used in various fields such as life, national defense, and science and technology. The scale, output and technical level of the ethylene production process represent the level of a country's petrochemical industry. In 2015, Sinopec's ethylene production and average fuel consumption were 11,005.2 thousand tons / year and 559.06 kg / ton ethylene, respectively. China National Petroleum Corporation's ethylene production and average fuel consumption are 5,032 kilotons per yea...

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Problems solved by technology

[0003] Existing technologies have proposed many solutions to the energy efficiency prediction of the petrochemical industry, including using neural networks such as BP and RBF to make predictions, but the prediction results are not accurate and complete

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