Abstract
The carburizing and coking of ethylene cracking furnace tubes are the important factors that affect the energy efficiency of ethylene production. To realize the diagnosis and prediction of the different coking degrees of cracking furnace tubes, and then take corresponding treatment measures, is of great significance for improving ethylene production and prolonging the service life of the furnace tube. Therefore, a fusion diagnosis and prediction method based on artificial bee colony (ABC) and adaptive neural fuzzy inference system (ANFIS) is proposed, which also introduces a coking-time factor (CTF). The actual data verification shows that the method not only improves the training efficiency and diagnosis accuracy of the coking diagnosis and inference system of the cracking furnace tube, but also realizes the prediction of the development trend of the coking degree of the furnace tube.
Highlights
The petrochemical industry is one of the important energy-based industries for the development of the national economy [1]
Based on the above research, a fusion-diagnosis and prediction method for the coking degree of cracking furnace tubes based on the artificial bee colony algorithm and adaptive fuzzy neural is proposed in this paper, which alsowhich introduces a coking-time factor (CTF), named
By using the trained coking diagnosis and inference system and combining with the coking-time factor proposed in this paper, the development trend of the coking degree of the cracking furnace tube in the future period can be predicted, which plays the function of early warning and efficiency protection of the furnace tube
Summary
The petrochemical industry is one of the important energy-based industries for the development of the national economy [1]. Based on the above research, a fusion-diagnosis and prediction method for the coking degree of cracking furnace tubes based on the artificial bee colony algorithm and adaptive fuzzy neural network. Based on the above research, a fusion-diagnosis and prediction method for the coking degree of cracking furnace tubes based on the artificial bee colony algorithm and adaptive fuzzy neural is proposed in this paper, which alsowhich introduces a coking-time factor (CTF), named. Method mainly has the following three contributions: based on ANFIS is proposed, and an adjacent (1) A coking diagnosis and inference system based function layer layer is added added after after the the output output layer layer of the system, system, which which can can make make the the system system processing function output of the quantified coking degree of the cracking furnace tube more accurately.
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