Artificial intelligence techniques for modeling and optimization of the HDS process over a new graphene based catalyst. (1st September 2016)

• Record Type: Journal Article
• Authors: Hajjar, Zeinab; Kazemeini, Mohammad; Rashidi, Alimorad; Tayyebi, Shokoufe
• Is Part Of: Phosphorus, sulfur, and silicon and the related elements. Volume 191:Number 9(2016)
• Page Start: 1256
• Page End: 1261
• Publication Date: 2016-09-01
• Subjects: Graphene -- hydrodesulfurization -- nanocatalysis -- genetic algorithm -- neural network; Sulfur compounds -- Periodicals; Organosulfur compounds -- Periodicals; Phosphorus compounds -- Periodicals; Organophosphorus compounds -- Periodicals; Silicon compounds -- Periodicals; Organosilicon compounds -- Periodicals; 546
• DOI: 10.1080/10426507.2016.1166428
• Languages: English
• ISSNs: 1042-6507

Abstract

GRAPHICAL ABSTRACT: ABSTRACT: A Co-Mo/graphene oxide (GO) catalyst has been synthesized for the first time for application in a defined hydrodesulfurization (HDS) process to produce sulfur free naphtha. An intelligent model based upon the neural network technique has then been developed to estimate the total sulfur output of this process. Process operating variables include temperature, pressure, LHSV and H2 /feed volume ratio. The three-layer, feed-forward neural network developed consists of five neurons in a hidden layer, trained with Levenberg–Marquardt, back-propagation gradient algorithm. The predicted amount of residual total sulfur is in very good agreement with the corresponding experimental values revealing a correlation coefficient of greater than 0.99. In addition, a genetic algorithm (GA) has been employed to optimize values of total sulfur as well as reaction conditions.