Neural network ARX model for gas conditioning tower. (3rd July 2019)

• Record Type: Journal Article
• Authors: Haddouche, Rezki; Chetate, Boukhemis; Boumedine, Mohand Said
• Is Part Of: International journal of modelling & simulation. Volume 39:Number 3(2019)
• Page Start: 166
• Page End: 177
• Publication Date: 2019-07-03
• Subjects: gas conditioning tower -- artificial neural network -- nnrx model -- system identification -- dust collector; Mathematical models -- Periodicals; Simulation methods -- Periodicals; Mathematical models; Simulation methods; Periodicals; 003.3
• DOI: 10.1080/02286203.2018.1538848
• Languages: English
• ISSNs: 0228-6203

Abstract

ABSTRACT: This work focuses on the identification of the gas conditioning tower (GCT) operating in a cement plant. It is an important element in the cement production line. Mathematical modeling of such a process proves to be very complex. This is due to the phenomena that occur during the operation of the system. An artificial neural network-based auto-regressive with exogenous inputs (NNARX) model is constructed with the aim to study the system as well as used to control the process. Resulted models are tested and validated using data extracted on a GCT operating at Chlef cement plant in Algeria.