Optimizing product distribution in the heavy oil catalytic cracking (MIP) process. (3rd July 2017)

• Record Type: Journal Article
• Authors: Ouyang, Fusheng; Zhang, Jianhua; Fang, Weigang
• Is Part Of: Petroleum science and technology. Volume 35:Number 13(2017)
• Page Start: 1315
• Page End: 1320
• Publication Date: 2017-07-03
• Subjects: BP neural network -- FCC -- genetic algorithm -- MIP process -- simulation; Liquid fuels -- Periodicals; Petroleum -- Periodicals; 665.505
• DOI: 10.1080/10916466.2017.1297826
• Languages: English
• ISSNs: 1091-6466

Abstract

ABSTRACT: The artificial neural network provides an effective way to handle a non-linear and strong coupled reaction system because of its strong nonlinear prediction and self-learning ability. A 19–24-4 type of back propagation (BP) neural network that can predict the product distribution of a fluid catalytic cracking maximizing iso-paraffin unit was established using 19 input variables including properties of feedstock and regenerated catalyst and operating variables. The influences of the operating variables on product distribution were simulated, and the operating variables were optimized to maximize gasoline (GS) yield by a genetic algorithm. The predicting results agreed well with the industrial data, and a significant improvement in the GS yield was gained.