Modeling and comparative study of heat exchangers fouling in phosphoric acid concentration plant using experimental data
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ORIGINAL
Modeling and comparative study of heat exchangers fouling in phosphoric acid concentration plant using experimental data Rania Jradi 1 & Christophe Marvillet 2 & Mohamed Razak Jeday 1 Received: 5 November 2019 / Accepted: 27 May 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Fouling still remains one of the most difficult problems for the use of heat exchangers. A methodological process of advanced analysis of experimental data on heat exchangers fouling allowing building predictive models is necessary to determine the fouling degree. Here, three different methods were used to predict the fouling resistance from some easily measurable variables of the system which are: Kern and Seaton, Partial Least Squares (PLS) and Artificial Neural Networks (ANN). Indeed, the fouling resistance was estimated according to the inlet and outlet temperature of the cold fluid, the temperature of the hot fluid, the density and the volume flow rate of the cold fluid and time for three types of heat exchangers, i.e. tubular stainless-steel and graphite blocks (Supplier (A) and Supplier (B)).The best modeling was determined by maximizing certain statistical accuracy indices. Results show that modeling by the use of Artificial Neural Networks is very performing compared with modeling by Partial Least Squares regression and Kern and Seaton. One of the key features of ANN model is their small levels of error in comparison with other models. Keywords Heat exchanger . Fouling . Artificial neural networks . Kern and Seaton model . Partial least squares regression . Experimental data
Abbreviations AARD Absolute average relative deviation MSE Mean square error RMSE Root mean square error R2 Determination coefficient r2 Correlation coefficient Nomenclature C1 Coefficient in Eq. (15), Pa−1.s−1 Cb Bulk concentration of particles, kg.m−3 Cp Specific heat capacity, J.Kg−1.K−1 Cw Concentration of particles at the wall, kg.m−3 F Correction Factor (=1 for a steam condenser) g Gravity acceleration (=9.81 m.s−2) HMT Total manometric head, m kp Mass transfer coefficient, m.s−1 * Rania Jradi [email protected] 1
Research Laboratory “Process, Energy, Environment & Electrical Systems”, National Engineering School of Gabès, Gabès, Tunisia
2
CMGPCE Laboratory, French Institute of Refrigeration (IFFI), National Conservatory of Arts and Crafts (CNAM), Paris, France
mp ṁ N P Q Rf A T t U v˙
Mass of deposited particles per surface area, kg.m−2 Mass flow rate, kg.s−1 Observation number Pressure, bar Thermal power,W Fouling resistance, m2.K.W−1 Area, m2 Temperature, K Time, h Global exchange coefficient, W.m−2.K−1 Volume flow rate, m3.h−1
Greek symbols Density, kg.m−3 ρ Particle deposition rate, kg.m−2.s−1 Φd Particle removal rate, kg.m−2.s−1 Φr Shear stress, Pa τ Time required to reach 63.2% of Rf*, h Δ Difference of greatness between two points Subscripts and superscripts ac Acid b Bulk cir Circulation d Deposition
Heat Mass Transfer
dis exp in ml 0 out p r st suc w *
Discharge Experimental Input Logarithmic mean c
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