IJCMP | Modelling of fouling in heat exchangers using the Artificial Neural Network Approach

Indexing and Abstracting

Modelling of fouling in heat exchangers using the Artificial Neural Network Approach

Nadra Hussami , Hassan Al-Haj Ibrahim , Ahmad Safwat

International Journal of Chemistry, Mathematics And Physics(IJCMP), Vol-2,Issue-5, September - October 2018, Pages 33-52 , 10.22161/ijcmp.2.5.1

Download | Downloads : 6 | Total View : 1579

Abstract:

In this paper, modelling by neural networks was used for obtaining a model for the calculation of fouling factors in heat exchangers. The heat exchangers used in this study are a series of four exchangers where a model was obtained for each exchanger after due estimation of its heat load. The basic theme of this paper is the investigation of fouling factors and the determination of relevant indicators followed by combining design and operation factors along with fouling factors in a mathematical model that may be used for the calculation of the fouling factor. The devised model was tested for reliability and its accuracy in predicting new values for the fouling factor was greater than 98% in view of the design of the model Furthermore, the number of elements related to the design and operation was reduced to four developed formulae (developed factors) to which were added later the four factors selected as indicators of the occurrence of fouling. Both were then used as network input, whereas the output was the value of the fouling factor. The importance of this modelling lies in the fact that it enables the operator to continually predict the value of the fouling factor in heat exchangers and it assists him in taking appropriate measures to alleviate fouling effects ensuring thereby continuous operation of the unit and prevention of emergency shut downs.

Keywords:

naphtha hydro rating unit , heat exchanger ,fouling, moduling ,artificial neural network ANNW.

References:

[1] Process And Operating Manual Of Naphtha Hydro Treating Process, Homs Refinery Extension VI, 1989, p.280-290.
[2] Nalco Chemical Company, Refinery And Processing Chemicals Delayed Coker Overhead Corrosion, A Case History 1978.
[3] Modeling of fouling in heat exchangers of naphtha hydrotreating unit in Homs refinery , Hassan Al-Haj Ibrahim , Ahmad Safwate Safeiy , Nadra Hussami , 2006 .
[4] Betezdearborn, Hydrocarbon Processing Analysis Report, Homs Refinery, 2001.
[5] P.Chopey And Hicks, Hand Book Of Chemical Engineering Calculations, 1984, p. 400-420
[6] MATLAB – A Fundamental Tool for Scientific Computing and Engineering Applications –Fouling in Heat Exchangers, Hassan Al-Haj Ibrahim, Volume 3, 2012.
[7] ASTM , Annual Book Of American Society For Testing And Materials Standars ,Metals Test Methods And Analytical Procedures ,Section 3,Vol.0320,1991.
[8] Fouling in heat exchangers of naphtha hydrogenation unit in Homs refinery. Published in engineering in Quatar University Journal, 2005, no.18.
[9] The estimation rate of fouling in heat exchangers of naphtha hydrogenation unit. Published in Adan University Journal, 2005.
[10] [10] ESDU, Heat Exchanger Fouling In The Preheat Train of a Crude Oil Distillation Unit ESDU International Plc ,London ,2000.
[11] Baughman, D.R. And Y.A. Liu, Neural Network In Bioprocessing And Chemical engineering, Academic Press, San Diego, CA 1995
[12] S. L.Meyer, , Data Analysis For Scientists And Engineers, 1975, John Wiley And Sons, Inc., New work.
[13] S.Tan, And M. Mavrovouniotis, Reducing Data Dimensionality Through Optimizing neural Network Inputs, 1995,AICHE Journal.
[14] J.Tukey, , Exploratory Data Analysis, 1977, Addison-Wesley Publishing Company, New York.
[15] A.L. Comrey, And H.B. Lee, A First Course In Factor Analysis, 1992, Lawrence Erlbaumas sociates Publishers, New Jersey.
[16] A.Bastian, And J. Gasos, Selection Of Input Variables For Model Identification Of Static Nonlinear Systems,1996, Journal Of Intelligent And Robotic Systems, 16, 185