Research Papers: Thermal Systems

Modeling of Thermal Cracking Furnaces Via Exergy Analysis Using Hybrid Artificial Neural Network–Genetic Algorithm

[+] Author and Article Information
M. Alizadeh

Chemical Engineering Department,
Tarbiat Modares University,
Tehran 14115-114, Iran

S. M. Sadrameli

Chemical Engineering Department,
Tarbiat Modares University,
Tehran 14115-114, Iran
e-mail: sadramel@modares.ac.ir

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received December 17, 2014; final manuscript received December 1, 2015; published online January 20, 2016. Assoc. Editor: Antonio Barletta.

J. Heat Transfer 138(4), 042801 (Jan 20, 2016) (11 pages) Paper No: HT-14-1819; doi: 10.1115/1.4032171 History: Received December 17, 2014; Revised December 01, 2015

In this study, we try to make an exergy analysis of an olefin cracking furnace more understandable by coupling it with the use of an artificial neural network–generic algorithm (ANN–GA) modeling. The presented method permits to provide an energy diagnosis of the process under a wide range of operating conditions. As a case study, one of the petrochemical complexes in Iran has been considered. The Petrosim process simulator software was used to obtain thermodynamic properties of the process streams and to perform exergy balances. The results are validated with industrial data obtained from the plant. The exergy destruction and exergetic efficiency for the main system components and the entire system were calculated. The simulation results reveal that the exergetic loss of the process increases with increasing steam ratio (SR) and decreases with coil outlet temperature (COT) and residence time (RT). The results show that the overall exergetic efficiency of the system is about 65%. The recorded and calculated data have been used as inputs for the neural network. The results show that ANN–GA is a highly effective method to optimize the performance of the neural networks, predicting the overall exergy efficiency. Comparing to phenomenological modeling based on the detailed knowledge of the furnace condition, the use of the introduced ANN–GA model saves significant amount of the time needed for the performance prediction of cracking furnaces.

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Ren, T. , Patel, M. , and Blok, K. , 2006, “ Olefins From Conventional and Heavy Feedstocks, Energy Use in Steam Cracking and Alternative Processes,” Energy, 31(4), pp. 425–451. [CrossRef]
Karimzadeh, R. , Godini, H. R. , and Ghashghaee, M. , 2009, “ Flowsheeting of Steam Cracking Furnaces,” Chem. Eng. Res. Des., 87(1), pp. 36–46. [CrossRef]
Sadrameli, S. M. , and Green, A. E. S. , 2005, “ Systematics and Modeling Representations of Naphtha Thermal Cracking for Olefin Production,” J. Anal. Appl. Pyrolysis, 73(2), pp. 305–313. [CrossRef]
Niaei, A. , Towfighi, J. , and Sadrameli, M. S. , 2004, “ The Combined Simulation of Heat Transfer and Pyrolysis Reaction in Industrial Cracking Furnaces,” Appl. Therm. Eng., 24(14–15), pp. 2251–2265. [CrossRef]
Masoomi, M. E. , Sadrameli, S. M. , and Towfighi, J. , 2006, “ Simulation, Optimization and Control of Thermal Cracking Pilot Plant Furnace,” Energy, 31(4), pp. 516–527. [CrossRef]
Ghannadzadeh, A. , Thery-Hetreux, R. , Baudouin, O. , Baudet, P. , Floquet, P. , and Joulia, X. , 2012, “ General Methodology for Exergy Balance in ProSimPlus Process Simulator,” Energy, 44(1), pp. 38–59. [CrossRef]
Gaggioli, R. A. , 1998, “ Available Energy and Exergy,” Int. J. Appl. Thermodyn., 1(1–4), pp. 1–8.
Mehrpooya, M. , Vatani, M. , and Mousavi, A. , 2010, “ Optimum Design of Integrated Liquid Recovery Plants by Variable Population Size Genetic Algorithm,” Can. J. Chem. Eng., 88(6), pp. 1054–1064. [CrossRef]
Moran, M. J. , 1982, Availability Analysis: A Guide to Efficient Energy Use, Prentice-Hall, Englewood Cliffs, NJ.
Safarian, S. , and Aramoun, F. , 2015, “ Energy and Exergy Assessments of Modified Organic Rankine Cycles (ORCs),” Energy Rep., 1(1), pp. 1–7. [CrossRef]
Kotas, T. J. , 1995, The Exergy Method of Thermal Plant Analysis, Butterworths, London.
Kanoglu, M. , 2002, “ Exergy Analysis of Multistage Cascade Refrigeration Cycle Used for Natural Gas Liquefaction,” Int. J. Energy Res., 26(8), pp. 763–774. [CrossRef]
Li, Q. , and Lin, Y. , 2016, “ Exergy Analysis of the LFC Process,” Energy Conver. Manage., 108(15), pp. 348–354. [CrossRef]
Sciubba, E. , and Wall, G. , 2007, “ A Brief Commented History of Exergy From the Beginnings to 2004,” Int. J. Thermodyn., 10(1), pp. 1–26.
Mafi, M. , Naeynian, S. M. , and Amidpour, M. , 2008, “ Exergy Analysis of Multistage Cascade Low Temperature Refrigeration Systems Used in Olefin Plants,” Int. J. Refrig., 32(2), pp. 279–294. [CrossRef]
Mehrpooya, M. , Jarrahian, A. , and Pishvaie, M. R. , 2006, “ Simulation and Exergy-Method Analysis of an Industrial Refrigeration Cycle Used in NGL Recovery Units,” Int. J. Energy Res., 30(15), pp. 1336–1351. [CrossRef]
Remeljej, C. W. , and Hoadley, A. , 2006, “ An Exergy Analysis of Small-Scale Liquefied Natural Gas (LNG) Liquefaction Processes,” Energy, 31(12), pp. 2005–2019. [CrossRef]
Fabrega, F. M. , Rossi, J. S. , and d Angelo, J. V. H. , 2010, “ Exergetic Analysis of the Refrigeration System in Ethylene and Propylene Production Process,” Int. J. Refrig., 35(4), pp. 1224–1231.
Mehrpooya, M. , Vatani, A. , and Mousavian, S. M. A. , 2010, “ Introducing a Novel Integrated NGL Recovery Process Configuration (With a Self-Refrigeration System (Open–Closed Cycle)) With Minimum Energy Requirement,” Chem. Eng. Process., 47, pp. 376–388. [CrossRef]
Fissore, D. , and Sokeipirim, D. , 2011, “ Simulation and Energy Consumption Analysis of a Propane Plus Recovery Plant From Natural Gas,” Fuel Process. Technol., 92(3), pp. 656–662. [CrossRef]
Ghorbani, B. , Salehi, G. R. , Ghaemmaleki, H. , Amidpour, M. , and Hamedi, M. H. , 2012, “ Simulation and Optimization of Refrigeration Cycle in NGL Recovery Plants With Exergy-Pinch Analysis,” J. Nat. Gas Sci. Eng., 7, pp. 35–43. [CrossRef]
MohdShariq, K. L. , and Moonyong, K. L. , 2013, “ Design Optimization of Single Mixed Refrigerant Natural Gas Liquefaction Process Using the Particle Swarm Paradigm With Nonlinear Constraints,” Energy, 49, pp. 146–155. [CrossRef]
Vatani, A. , Mehrpooya, M. , and Palizdar, A. , 2014, “ Advanced Exergetic Analysis of Five Natural Gas Liquefaction Processes,” Energy Convers. Manage., 78, pp. 720–737. [CrossRef]
Karimi, H. , and Yousefi, S. , 2012, “ Application of Artificial Neural Network–Genetic Algorithm (ANN–GA) to Correlation of Density in Nanofluids,” Fluid Phase Equilib., 336, pp. 79–83. [CrossRef]
Sivapathasekaran, C. , Mukherjee, S. , Ray, A. , Gupta, A. , and Sen, R. , 2010, “ Artificial Neural Network Modeling and Genetic Algorithm Based Medium Optimization for the Improved Production of Marine Biosurfactant,” Bioresour. Technol., 101(8), pp. 2884–2887. [CrossRef] [PubMed]
Soleimani, R. , Shoushtari, N. A. , Mirza, B. , and Salahi, A. , 2012, “ Experimental Investigation, Modeling and Optimization of Membrane Separation Using Artificial Neural Network and Multi-Objective Optimization Using Genetic Algorithm,” Chem. Eng. Res. Des., 91, pp. 883–903. [CrossRef]
Kalyanmoy, D. , 1996, Optimizations for Engineering Design–Algorithm and Examples, Prentice Hall of India, New Delhi, pp. 290–333.
Szargut, J. , Morris, D. R. , and Steward, F. R. , 1988, Exergy Analysis of Thermal, Chemical, and Metallurgical Processes, Hemisphere, New York.
Rivero, R. , and Garfias, M. , 2006, “ Standard Chemical Exergy of Elements Updated,” Energy, 31(15), pp. 3310–3326. [CrossRef]
Demuth, H. , and Beale, M. , 2002, Neural Network for Use With MATLAB. User's Guide, Ver. 4, The Mathworks, Natick, MA.
Plagnol, V. , Padhukasahasram, B. , Wall, J. D. , Marjoram, P. , and Nordborg, M. , 2006, “ Relative Influences of Crossing Over and Gene Conversion on the Pattern of Linkage Disequilibrium in Arabidopsis thaliana,” Genetics, 172(4), pp. 2441–2448. [CrossRef] [PubMed]
Peng, D. Y. , and Robinson, D. B. , 1976, “ A New Two-Constant Equation of State,” Ind. Eng. Chem.: Fundam., 15(1), pp. 59–64. [CrossRef]


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Fig. 1

Integrated heating system in a thermal cracking furnace of an olefin plant

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Fig. 2

Grassmann representation of a process or system

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Fig. 3

Typical architecture of a feed forward BP neural network

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Fig. 4

Framework of the ANN–GA method

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Fig. 5

Schematic diagram of the cracking furnace for the considered petrochemical Co

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Fig. 6

Exergy efficiency of convection section banks

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Fig. 7

Process fluid and flue gas temperature along the convection section

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Fig. 8

Effect of SR on furnace exergetic efficiency

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Fig. 9

Effect of RT on furnace exergetic efficiency

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Fig. 10

Scatter diagram for exergetic efficiency based on ANN–GA approach in terms of correlation coefficient (R2)

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Fig. 11

The comparison between ANN–GA outcomes and real values of exergetic efficiency for all data

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Fig. 12

Relative error of the determined exergetic efficiency values of the GA–ANN model from real values



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