Optimization of FX-70 refrigerant evaporative heat transfer and fluid flow characteristics inside the corrugated tubes using multi-objective genetic algorithm

doi:10.1016/j.cjche.2020.05.036

Chinese Journal of Chemical Engineering ›› 2020, Vol. 28 ›› Issue (8): 2142-2151.DOI: 10.1016/j.cjche.2020.05.036

• Chemical Engineering Thermodynamics • Previous Articles Next Articles

Optimization of FX-70 refrigerant evaporative heat transfer and fluid flow characteristics inside the corrugated tubes using multi-objective genetic algorithm

Mirollah Hosseini¹, Hamid Hassanzadeh Afrouzi², Sina Yarmohammadi², Hossein Arasteh³, Davood Toghraie⁴, A. Jafarian Amiri², Arash Karimipour⁵

1 Department of Mechanical Engineering, Islamic Azad University, Qaemshahr Branch, Qaemshahr, Mazandaran, Iran;
2 Faculty of Mechanical Engineering, Babol Noshirvani University of Technology, Babol, Iran;
3 Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran;
4 Department of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, Iran;
5 Sustainable Management of Natural Resources and Environment Research Group, Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

Received:2019-10-01 Revised:2020-04-28 Online:2020-09-19 Published:2020-08-28
Contact: Arash Karimipour

Optimization of FX-70 refrigerant evaporative heat transfer and fluid flow characteristics inside the corrugated tubes using multi-objective genetic algorithm

Mirollah Hosseini¹, Hamid Hassanzadeh Afrouzi², Sina Yarmohammadi², Hossein Arasteh³, Davood Toghraie⁴, A. Jafarian Amiri², Arash Karimipour⁵

1 Department of Mechanical Engineering, Islamic Azad University, Qaemshahr Branch, Qaemshahr, Mazandaran, Iran;
2 Faculty of Mechanical Engineering, Babol Noshirvani University of Technology, Babol, Iran;
3 Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran;
4 Department of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, Iran;
5 Sustainable Management of Natural Resources and Environment Research Group, Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

通讯作者: Arash Karimipour

Abstract

Abstract: In this study, the heat transfer optimization (evaporation) and the specification of the FX-70 zeotropic refrigerant flow inside a corrugated pipe have been investigated. Despite the low HTC (HTC), this type of refrigerant is highly applicable in low or medium temperature engineering systems during the evaporation process. To eliminate this defect, high turbulence and proper mixing are required. Therefore, using heat transfer (HT) augmentation methods will be necessary and effective. In order to find the most favorable operating conditions that lead to the optimum combination of pressure drop (PD) and HTC, empirical data, neural networks, and genetic algorithms (GA) for multi-objective (MO) (NSGA II) are used. To investigate the mentioned cases, the geometric parameters of corrugated pipes, vapor quality, and mass velocity of refrigerant were studied. The results showed that with vapor quality higher than 0.8 and corrugation depth and pitch of 1.5 and 7 mm, respectively, we would achieve the desired optimum design.

Key words: Optimization, Genetic algorithm, Neural network, Corrugated tube, FX-70 refrigerant

摘要： In this study, the heat transfer optimization (evaporation) and the specification of the FX-70 zeotropic refrigerant flow inside a corrugated pipe have been investigated. Despite the low HTC (HTC), this type of refrigerant is highly applicable in low or medium temperature engineering systems during the evaporation process. To eliminate this defect, high turbulence and proper mixing are required. Therefore, using heat transfer (HT) augmentation methods will be necessary and effective. In order to find the most favorable operating conditions that lead to the optimum combination of pressure drop (PD) and HTC, empirical data, neural networks, and genetic algorithms (GA) for multi-objective (MO) (NSGA II) are used. To investigate the mentioned cases, the geometric parameters of corrugated pipes, vapor quality, and mass velocity of refrigerant were studied. The results showed that with vapor quality higher than 0.8 and corrugation depth and pitch of 1.5 and 7 mm, respectively, we would achieve the desired optimum design.

关键词: Optimization, Genetic algorithm, Neural network, Corrugated tube, FX-70 refrigerant

Mirollah Hosseini, Hamid Hassanzadeh Afrouzi, Sina Yarmohammadi, Hossein Arasteh, Davood Toghraie, A. Jafarian Amiri, Arash Karimipour. Optimization of FX-70 refrigerant evaporative heat transfer and fluid flow characteristics inside the corrugated tubes using multi-objective genetic algorithm[J]. Chinese Journal of Chemical Engineering, 2020, 28(8): 2142-2151.

References

[1] A.A.R. Darzi, H.H. Afrouzi, A. Moshfegh, M. Farhadi, Absorption and desorption of hydrogen in long metal hydride tank equipped with phase change material jacket, Int. J. Hydrog. Energy 41(22) (2016) 9595-9610.
[2] H. Mahyari, H.H. Afrouzi, M. Shams, Three dimensional transient multiphase flow simulation in a dead end anode polymer electrolyte fuel cell, J. Mol. Liq. 225(2017) 391-405.
[3] H. Pourdel, H.H. Afrouzi, O.A. Akbari, M. Miansari, D. Toghraie, A. Marzban, A. Koveiti, Numerical investigation of turbulent flow and heat transfer in flat tube, J. Therm. Anal. Calorim. 135(6) (2019) 3471-3483.
[4] A.A. Rabinataj Darzi, H. Hassanzadeh Afrouzi, M. Khaki, M. Abbasi, Unconstrained melting and solidification inside rectangular enclosure, J. Fundam. Appl. Sci. 7(3) (2015) 436-451.
[5] A.A. Lalami, H.H. Afrouzi, A. Moshfegh, Investigation of MHD effect on nanofluid heat transfer in microchannels, J. Therm. Anal. Calorim. 136(5) (2019) 1959-1975.
[6] A. Javadzadegan, M. Joshaghani, A. Moshfegh, O.A. Akbari, H.H. Afrouzi, D. Toghraie, Accurate meso-scale simulation of mixed convective heat transfer in a porous media for a vented square with hot elliptic obstacle:An LBM approach, Physica A:Statistical Mechanics and Its Applications 537(2020) 122439.
[7] M. Hosseini, H.H. Afrouzi, H. Arasteh, D. Toghraie, Energy analysis of a proton exchange membrane fuel cell (PEMFC) with an open-ended anode using agglomerate model:A CFD study, Energy 188(2019) 116090.
[8] A. Javadzadegan, S.H. Motaharpour, A. Moshfegh, O.A. Akbari, H.H. Afrouzi, D. Toghraie, Lattice-Boltzmann method for analysis of combined forced convection and radiation heat transfer in a channel with sinusoidal distribution on walls, Physica A:Statistical Mechanics and Its Applications 526(2019) 121066.
[9] H. Karimi-Maleh, C.T. Fakude, N. Mabuba, G.M. Peleyeju, O.A. Arotiba, The determination of 2-phenylphenol in the presence of 4-chlorophenol using nano-Fe₃O₄/ionic liquid paste electrode as an electrochemical sensor, Journal of Colloid and Interface Science 554(2019) 603-610.
[10] H. Karimi-Maleh, O.A. Arotiba, Simultaneous determination of cholesterol, ascorbic acid and uric acid as three essential biological compounds at a carbon paste electrode modified with copper oxide decorated reduced graphene oxide nanocomposite and ionic liquid, Journal of Colloid and Interface Science 560(2020) 208-212.
[11] S. Mousavi, Mohit Biglarian Morteza, A. Ali Rabienataj Darzi, Mousa Farhadi, Hamid Hassanzadeh Afrouzi, Davood Toghraie, Heat transfer enhancement of ferrofluid flow within a wavy channel by applying a non-uniform magnetic field, J. Therm. Anal. Calorim. 139(5) (2020) 3331-3343.
[12] M. Hosseini, H.H. Afrouzi, H. Arasteh, D. Toghraie, Energy analysis of a proton exchange membrane fuel cell (PEMFC) with an open-ended anode using agglomerate model:A CFD study, Energy 188(2019) 116090.
[13] R. Darzi, AhmadAli, A.H. Eisapour, A. Abazarian, F. Hosseinnejad, H.H. Afrouzi, Mixed convection heat transfer analysis in an enclosure with two hot cylinders:A lattice Boltzmann approach, Heat Transfer-Asian Research 46(3) (2017) 218-236.
[14] H. Hassanzadeh Afrouzi, A. Moshfegh, M. Farhadi, K. Sedighi, Dissipative particle dynamics simulation hydrated Nafion EW 1200 as fuel cell membrane in nanoscopic scale, Transp Phenom Nano Micro Scales 5(1) (2016) 44-53.
[15] Z. Shamsadin-Azad, M.A. Taher, S. Cheraghi, H. Karimi-Maleh, A nanostructure voltammetric platform amplified with ionic liquid for determination of tertbutylhydroxyanisole in the presence kojic acid, Journal of Food Measurement and Characterization Volume 13(2019) 1781-1787, https://doi.org/10.1007/s11694-019-00096-6.
[16] E.U. Küçüksille, R. Selbaş, A. Şencan, Data mining techniques for thermophysical properties of refrigerants, Energy Convers. Manag. 50(2) (2009) 399-412.
[17] K. Comakli, F. Simsek, O. Comakli, B. Sahin, Determination of optimum working conditions R22 and R404A refrigerant mixtures in heat-pumps using Taguchi method, Appl. Energy 86(11) (2009) 2451-2458.
[18] D. Toghraie, A. Karimipour, M.R. Safaei, M. Goodarzi, H. Alipour, M. Dahari, Investigation of rib's height effect on heat transfer and flow parameters of laminar water-Al₂O₃ nanofluid in a rib-microchannel author-name:Akbari, Omid Ali, Appl. Math. Comput. 290(C) (2016) 135-153.
[19] Z. Nikkhah, A. Karimipour, M.R. Safaei, P. Forghani-Tehrani, M. Goodarzi, M. Dahari, S. Wongwises, Forced convective heat transfer of water/functionalized multi-walled carbon nanotube nanofluids in a microchannel with oscillating heat flux and slip boundary condition, Int. Commun. Heat Mass Transfer 68(2015) 69-77.
[20] S. Foroutani, A. Rahbari, Numerical investigation of laminar forced convection heat transfer in rectangular channels with different block geometries using nano-fluids, Therm. Sci. 21(5) (2017) 2129-2138.
[21] M. Fakour, A. Rahbari, E. Khodabandeh, D.D. Ganji. Nanofluid thin film flow and heat transfer over an unsteady stretching elastic sheet by LSM. J. Mech. Sci. Technol. 32(1) (2018) 177-183.
[22] M. Shahidi, M. Aligoodarz, A.-B. Mohammad, S. Foroutani, A. Rahbari, Experimental and numerical invesitgation on turbulent flow of Mwcnt-water nanofluid inside vertical coiled wire inserted tubes, Therm. Sci. 22(2018) 125-136.
[23] E. Khodabandeh, M. Bahiraei, R. Mashayekhi, B. Talebjedi, D. Toghraie, Thermal performance of Ag-water nanofluid in tube equipped with novel conical strip inserts using two-phase method:geometry effects and particle migration considerations, Powder Technol. 338(2018) 87-100.
[24] A. Karimipour, M.H. Esfe, M.R. Safaei, D.T. Semiromi, S. Jafari, S.N. Kazi, Mixed convection of copper-water nanofluid in a shallow inclined lid driven cavity using the lattice Boltzmann method, Physica A:Statistical Mechanics and Its Applications 402(2014) 150-168.
[25] E. Gholamalizadeh, F. Pahlevanzadeh, K. Ghani, A. Karimipour, T.K. Nguyen, M.R. Safaei, Simulation of water/FMWCNT nanofluid forced convection in a microchannel filled with porous material under slip velocity and temperature jump boundary conditions, Int. J. Numer. Methods for Heat & Fluid Flow 30(5) (2019) 2329-2349.
[26] A. Behnampour, O.A. Akbari, M.R. Safaei, M. Ghavami, A. Marzban, G.A.S. Shabani, R. Mashayekhi, Analysis of heat transfer and nanofluid fluid flow in microchannels with trapezoidal, rectangular and triangular shaped ribs, Physica E:Low-Dimensional Systems and Nanostructures 91(2017) 15-31.
[27] W. Targanski, J.T. Cieslinski, Evaporation of R407C/oil mixtures inside corrugated and micro-fin tubes, Appl. Therm. Eng. 27(13) (2007) 2226-2232.
[28] P.G. Vicente, A. Garcıa, A. Viedma, Experimental investigation on heat transfer and frictional specification of spirally corrugated tubes in turbulent flow at different Prandtl numbers, Int. J. Heat Mass Transf. 47(4) (2004) 671-681.
[29] W. Wang, Y. Zhang, Y. Li, H. Han, B. Li, Multi-objective optimization of turbulent heat transfer flow in novel outward helically corrugated tubes, Appl. Therm. Eng. 138(2018) 795-806.
[30] H. Han, R. Yu, B. Li, Y. Zhang, Multi-objective optimization of corrugated tube inserted with multi-channel twisted tape using RSM and NSGA-II, Appl. Therm. Eng. 159(2019) 113731.
[31] R. Llopis, E. Torrella, R. Cabello, D. Sánchez, Performance evaluation of R404A and R507A refrigerant mixtures in an experimental double-stage vapour compression plant, Appl. Energy 87(5) (2010) 1546-1553.
[32] Pradeep A. Patil, S.N. Sapali, Condensation pressure drop of HFC-134a and R-404A in a smooth and micro-fin U-tube, Exp. Thermal Fluid Sci. 35(1) (2011) 234-242.
[33] S.N. Sapali, Pradeep A. Patil, Heat transfer during condensation of HFC-134a and R-404A inside of a horizontal smooth and micro-fin tube, Exp. Thermal Fluid Sci. 34(8) (2010) 1133-1141.
[34] W. Kuczyński, H. Charun, T. Bohdal, Influence of hydrodynamic instability on the HTC during condensation of R134a and R404A refrigerants in pipe mini-channels, Int. J. Heat Mass Transf. 55(4) (2012) 1083-1094.
[35] T. Bohdal, H. Charun, M. Sikora, Comparative investigations of the condensation of R134a and R404A refrigerants in pipe minichannels, Int. J. Heat Mass Transf. 54(9-10) (2011) 1963-1974.
[36] B.O. Bolaji, Performance investigation of ozone-friendly R404A and R507 refrigerants as alternatives to R22 in a window air-conditioner, Energy and Buildings 43(11) (2011) 3139-3143.
[37] H. Charun, Thermal and flow characteristics of the condensation of R404A refrigerant in pipe minichannels, Int. J. Heat Mass Transf. 55(9-10) (2012) 2692-2701.
[38] M.R. Salimpour, S. Yarmohammadi, Heat transfer augmentation during R-404A vapor condensation in swirling flow, Int. J. Refrig. 35(7) (2012) 2014-2021.
[39] Z. Zhou, B. Chen, Y. Wang, L. Guo, G. Wang, An experimental study on pulsed spray cooling with refrigerant R-404a in laser surgery, Appl. Therm. Eng. 39(2012) 29-36.
[40] H. Charun, T. Bohdal, M. Czapp, Experimental investigation of the condensation of R134a and R404A refrigerants in a long, water-cooled, serpentine coils, Int. J. Heat Mass Transf. 67(2013) 602-612.
[41] S. Laohalertdecha, S. Wongwises, Condensation heat transfer and flow specification of R-134a flowing through corrugated tubes, Int. J. Heat Mass Transf. 54(11-12) (2011) 2673-2682.
[42] S. Laohalertdecha, S. Wongwises, An experimental study into the evaporation heat transfer and flow characteristics of R-134a refrigerant flowing through corrugated tubes, Int. J. Refrig. 34(1) (2011) 280-291.
[43] K. Aroonrat, S. Wongwises, Evaporation heat transfer and friction specification of R-134a flowing downward in a vertical corrugated tube, Exp. Thermal Fluid Sci. 35(1) (2011) 20-28.
[44] D. Khoeini, M.A. Akhavan-Behabadi, A. Saboonchi, Experimental study of condensation heat transfer of R-134a flow in corrugated tubes with different inclinations, International Communications in Heat and Mass Transfer 39(1) (2012) 138-143.
[45] S. Laohalertdecha, S. Wongwises, The effects of corrugation pitch on the condensation heat transfer coefficient and pressure drop of R-134a inside horizontal corrugated tube, Int. J. Heat Mass Transf. 53(13-14) (2010) 2924-2931.
[46] S. Laohalertdecha, A.S. Dalkilic, S. Wongwises, Correlations for evaporation heat transfer coefficient and two-phase friction factor for R-134a flowing through horizontal corrugated tubes, Int. Communi. Heat Mass Transfer 38(10) (2011) 1406-1413.
[47] M.A. Akhavan-Behabadi, M. Esmailpour, Experimental study of evaporation heat transfer of R-134a inside a corrugated tube with different tube inclinations, International Communications in Heat and Mass Transfer 55(2014) 8-14.
[48] Z.S. Kareem, M.N. M. Jaafar, T.M. Lazim, S. Abdullah, A.F. Abdulwahid, Passive heat transfer enhancement review in corrugation, Exp. Thermal Fluid Sci. 68(2015) 22-38.
[49] A. Sözen, E. Arcaklioğlu, T. Menlik, Derivation of empirical equations for thermodynamic properties of a ozone safe refrigerant (R404a) using artificial neural network, Expert Syst. Appl. 37(2) (2010) 1158-1168.
[50] M. Balcilar, K. Aroonrat, A.S. Dalkilic, S. Wongwises, A generalized numerical correlation study for the determination of pressure drop during condensation and boiling of R134a inside smooth and corrugated tubes, Int. Commun. Heat Mass Transfer 49(2013) 78-85.
[51] M. Balcilar, K. Aroonrat, A.S. Dalkilic, S. Wongwises, A numerical correlation development study for the determination of Nusselt numbers during boiling and condensation of R134a inside smooth and corrugated tubes, Int. Commun. Heat Mass Transfer 48(2013) 141-148.
[52] M.P. Porto, H.T.C. Pedro, L. Machado, R.N.N. Koury, C.U.S. Lima, C.F.M. Coimbra, Genetic optimization of heat transfer correlations for evaporator tube flows, Int. J. Heat Mass Transf. 70(2014) 330-339.
[53] H. Safikhani, S. Eiamsa-ard, Pareto based multi-objective optimization of turbulent heat transfer flow in helically corrugated tubes, Appl. Therm. Eng. 95(2016) 275-280.
[54] J. Holland, Adaptation in natural and artificial systems:An introductory analysis with application to biology, control, and artificial intelligence, University of Michigan Press (1975).
[55] E.G. David, Genetic algorithms in search, Optimization, and Machine Learning, Ethnographic Praxis in Industry Conference Proceeding, 9, Addison-Wesley Longman Publishing Co., Inc, MA, United States, 1988(2).
[56] Z. Michalewicz, Genetic Algorithms + Data Structures=Evolution Programs, Springer Science & Business Media 2013.
[57] D.S. Weile, E. Michielssen, Genetic algorithm optimization applied to electromagnetics:A review, IEEE Trans. Antennas Propag. 45(3) (1997) 343-353.
[58] H. Karimi-Maleh, F. Karimi, M. Alizadeh, A.L. Sanati, Electrochemical Sensors, a Bright Future in the Fabrication of Portable Kits in Analytical Systems, The Chemical Record 20(2020) https://doi.org/10.1002/tcr.201900092.
[59] F. Tahernejad-Javazmi, M. Shabani-Nooshabadi, H. Karimi-Maleh, 3D reduced graphene oxide/FeNi3-ionic liquid nanocomposite modified sensor; an electrical synergic effect for development of tert-butylhydroquinone and folic acid sensor, Composites Part B:Engineering 172(2019) 666-670.
[60] M.B. Kadri, W.A. Khan, Application of genetic algorithms in nonlinear heat conduction problems, Sci. World J. 2014(2014) 451274, https://doi.org/10.1155/2014/451274.
[61] M.R. Salimpour, S. Yarmohammadi, Effect of twisted tape inserts on pressure drop during R-404A condensation, Int. J. Refrig. 35(2) (2012) 263-269.
[62] Nidamarthi Srinivas, Kalyanmoy Deb, Muiltiobjective optimization using nondominated sorting in genetic algorithms, Evol. Comput. 2(3) (1994) 221-248.
[63] Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, T.A.M.T. Meyarivan, A fast and elitist multiobjective genetic algorithm:NSGA-II, IEEE Trans. Evol. Comput. 6(2) (2002) 182-197.
[64] H. Peng, X. Ling, Optimal design approach for the plate-fin heat exchangers using neural networks cooperated with genetic algorithms, Appl. Therm. Eng. 28(5-6) (2008) 642-650.
[65] T.L. Cong, R.H. Chen, G.H. Su, S.Z. Qiu, W.X. Tian, Analysis of CHF in saturated forced convective boiling on a heated surface with impinging jets using artificial neural network and genetic algorithm, Nucl. Eng. Des. 241(9) (2011) 3945-3951.
[66] R. Beigzadeh, M. Rahimi, M. Parvizi, S. Eiamsa-ard, Application of ANN and GA for the prediction and optimization of thermal and flow characteristics in a rectangular channel fitted with twisted tape vortex generators, Numerical Heat Transfer, Part A:Applications 65(2) (2014) 186-199.
[67] P.G. Vicente, A. Garcia, A. Viedma, Mixed convection heat transfer and isothermal pressure drop in corrugated tubes for laminar and transition flow, Int. Commun. Heat Mass Transfer 31(5) (2004) 651-662.
[68] W.R. Huang, S. Foo, Neural network modeling of salinity variation in Apalachicola River, Water Res. 36(1) (2002) 356-362.
[69] Y. Islamoglu, A new approach for the prediction of the heat transfer rate of the wireon-tube type heat exchanger——use of an artificial neural network model, Appl. Therm. Eng. 23(2) (2003) 243-249.
[70] M. Mohanraj, S. Jayaraj, C. Muraleedharan, Applications of artificial neural networks for thermal analysis of heat exchangers-A review, Int. J. Therm. Sci. 90(2015) 150-172.
[71] R. Beigzadeh, M. Rahimi, M. Parvizi, S. Eiamsa-ard, Application of ANN and GA for the prediction and optimization of thermal and flow characteristics in a rectangular channel fitted with twisted tape vortex generators, Numerical Heat Transfer, Part A:Applications 65(2) (2014) 186-199.

Optimization of FX-70 refrigerant evaporative heat transfer and fluid flow characteristics inside the corrugated tubes using multi-objective genetic algorithm

Optimization of FX-70 refrigerant evaporative heat transfer and fluid flow characteristics inside the corrugated tubes using multi-objective genetic algorithm

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Chuang Liang, Zhihao Liu, Baochang Sun, Haikui Zou, Guangwen Chu. Improvement in discharge characteristics and energy yield of ozone generation via configuration optimization of a coaxial dielectric barrier discharge reactor [J]. Chinese Journal of Chemical Engineering, 2023, 60(8): 61-68.
[2]	Yaran Bu, Changchun Wu, Lili Zuo, Qian Chen. The calculation and optimal allocation of transmission capacity in natural gas networks with MINLP models [J]. Chinese Journal of Chemical Engineering, 2023, 59(7): 251-261.
[3]	Danlei Chen, Yiqing Luo, Xigang Yuan. Cascade refrigeration system synthesis based on hybrid simulated annealing and particle swarm optimization algorithm [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 244-255.
[4]	Shanwei Xiong, Li Zhou, Yiyang Dai, Xu Ji. Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis [J]. Chinese Journal of Chemical Engineering, 2023, 56(4): 1-14.
[5]	Haoshan Duan, Xi Meng, Jian Tang, Junfei Qiao. Prediction of NO_x concentration using modular long short-term memory neural network for municipal solid waste incineration [J]. Chinese Journal of Chemical Engineering, 2023, 56(4): 46-57.
[6]	Jikai Dong, Bing Wang, Xinjie Wang, Chenxi Cao, Shikuan Chen, Wenli Du. Optimization of sensor deployment sequences for hazardous gas leakage monitoring and source term estimation [J]. Chinese Journal of Chemical Engineering, 2023, 56(4): 169-179.
[7]	Dandan Ren, Shanshan Xiang, Yuwen Yan, Ruiying Kong, Xingchu Gong. Design and optimization of purification process of sinomenine hydrochloride [J]. Chinese Journal of Chemical Engineering, 2023, 55(3): 63-72.
[8]	Yingjun Lin. Whole-process optimization for industrial production of glucosamine sulfate sodium chloride based on QbD concept [J]. Chinese Journal of Chemical Engineering, 2023, 54(2): 153-161.
[9]	Shan Liu, Wenqi Zhong, Xi Chen, Li Sun, Lukuan Yang. Multiobjective economic model predictive control using utopia-tracking for the wet flue gas desulphurization system [J]. Chinese Journal of Chemical Engineering, 2023, 54(2): 343-352.
[10]	Kun Ren, Zheng Jiao, Xiaolong Wu, Honggui Han. Multivariable identification of membrane fouling based on compacted cascade neural network [J]. Chinese Journal of Chemical Engineering, 2023, 53(1): 37-45.
[11]	Chao Zhang, Youzhi Liu, Weizhou Jiao, Hongyan Shen, Xigang Yuan, Shengkun Jia. An optimization method for enhancement of gas–liquid mass transfer in a bubble column reactor based on the entropy generation extremum principle [J]. Chinese Journal of Chemical Engineering, 2023, 53(1): 83-88.
[12]	Monique Juna L. Leite, Ingrid Ramalho Marques, Mariane Carolina Proner, Pedro H.H. Araújo, Alan Ambrosi, Marco Di Luccio. Catalytically active membranes for esterification: A review [J]. Chinese Journal of Chemical Engineering, 2023, 53(1): 142-154.
[13]	Xinqiang You, Kai Zhao, Ling Li, Ting Qiu. Ionic liquids as entrainer in extractive distillation for effectively separating 1-propanol–water azeotropic mixture [J]. Chinese Journal of Chemical Engineering, 2022, 49(9): 224-233.
[14]	Jia Ren, Zengqiang Chen, Mingwei Sun, Qinglin Sun, Zenghui Wang. Proportion integral-type active disturbance rejection generalized predictive control for distillation process based on grey wolf optimization parameter tuning [J]. Chinese Journal of Chemical Engineering, 2022, 49(9): 234-244.
[15]	Na Wang, Chong Peng, Zhenmin Cheng, Zhiming Zhou. Molecular reconstruction of vacuum gas oils using a general molecule library through entropy maximization [J]. Chinese Journal of Chemical Engineering, 2022, 48(8): 21-29.