References
- Abdul Jaleel E, Aparna K. 2018. Identification of realistic distillation column using NARX based hybrid artificial neural network and artificial bee colony algorithm. IFS. 34(4):2075–2086. doi:https://doi.org/10.3233/JIFS-161966
- Araromi D, Sonibare J, Emuoyibofarhe J. 2014. Fuzzy identification of reactive distillation for acetic acid recovery from waste water. J Environ Chem Eng. 2(3):1394–1403. doi:https://doi.org/10.1016/j.jece.2014.05.008
- Bachnas A, Tóth R, Ludlage J, Mesbah A. 2014. A review on data-driven linear parameter-varying modeling approaches: a high-purity distillation column case study. J Process Control. 24(4):272–285. doi:https://doi.org/10.1016/j.jprocont.2014.01.015
- Chang L, Liu X. 2014. Non-equilibrium stage based modeling of heat integrated air separation columns. Sep Purif Technol. 134:73–81. doi:https://doi.org/10.1016/j.seppur.2014.07.013
- Cristianini N, Shawe-Taylor J. 2000. An introduction to support vector machines and other kernel-based learning methods. Cambridge: Cambridge University Press.
- Cui C, Sun J, Li X. 2017. A hybrid design combining double-effect thermal integration and heat pump to the methanol distillation process for improving energy efficiency. Chem Eng Process. 119:81–92. doi:https://doi.org/10.1016/j.cep.2017.06.003
- Eberhart R, Kennedy J. 1995. A new optimizer using particle swarm theory. Paper presented at: the Sixth International Symposium on Micro Machine and Human Science (MHS’95); Nagoya, Japan. p. 39–43.
- Gadalla M, Olujic Z, Sun L, De Rijke A, Jansens P. 2005. Pinch analysis-based approach to conceptual design of internally heat-integrated distillation columns. Chem Eng Res Des. 83(8):987–993. doi:https://doi.org/10.1205/cherd.04301
- Gadalla MA. 2009. Internal heat integrated distillation columns (iHiDiCs)—new systematic design methodology. Chem Eng Res Des. 87(12):1658–1666. doi:https://doi.org/10.1016/j.cherd.2009.06.005
- Golberg DE. 1989. Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addion-Wesley.
- Jaleel A, Aparna K. 2015a. Identification of ethane-ethylene distillation column using neural network and ANFIS. Paper presented at: the 2015 Fifth International Conference on Advances in Computing and Communications (ICACC); Kochi, India. p. 358–361.
- Jaleel AE, Aparna K. 2018. Identification of heat-integrated distillation column using hybrid support vector regression and particle swarm optimization. CI&CEQ. 24(2):101–115. doi:https://doi.org/10.2298/CICEQ161118023J
- Jaleel EA, Aparna K. 2015b. Identification of benzene-toluene distillation column using neuro-fuzzy algorithm. Paper presented at: the 2015 Annual IEEE India Conference (INDICON); New Delhi, India. p. 1–6.
- Jaleel EA, Aparna K. 2016. Neuro-fuzzy soft sensor estimator for benzene toluene distillation column. Proc Technol. 25:92–99. doi:https://doi.org/10.1016/j.protcy.2016.08.085
- Jaleel EA, Aparna K. 2019. Identification of realistic distillation column using hybrid particle swarm optimization and NARX based artificial neural network. Evol Syst. 10(2):149–166. doi:https://doi.org/10.1007/s12530-018-9220-5
- Jana AK. 2010. Heat integrated distillation operation. Appl Energy. 87(5):1477–1494. doi:https://doi.org/10.1016/j.apenergy.2009.10.014
- Jiang Q, Fu X, Yan S, Li R, Du W, Cao Z, Qian F, Grima R. 2021. Neural network aided approximation and parameter inference of non-Markovian models of gene expression. Nat Commun. 12(1):2618–2612. doi:https://doi.org/10.1038/s41467-021-22919-1
- Kang F, Li J. 2016. Artificial bee colony algorithm optimized support vector regression for system reliability analysis of slopes. J Comput Civ Eng. 30(3):04015040. doi:https://doi.org/10.1061/(ASCE)CP.1943-5487.0000514
- Karaboga D, Akay B, Ozturk C. 2007. Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. Paper presented at: the International Conference on Modeling Decisions for Artificial Intelligence, Kitakyushu, Japan. p. 318–329.
- Karaboga D, Akay B. 2009. A comparative study of artificial bee colony algorithm. Appl Math Comput. 214(1):108–132. doi:https://doi.org/10.1016/j.amc.2009.03.090
- Karaboga D, Basturk B. 2007. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim. 39(3):459–471. doi:https://doi.org/10.1007/s10898-007-9149-x
- Karaboga D, Basturk B. 2008. On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput. 8(1):687–697. doi:https://doi.org/10.1016/j.asoc.2007.05.007
- Karaboga D. 2005. An idea based on honey bee swarm for numerical optimization. Technical report-TR06, Kayseri, Turkey: CiteSeer.
- Kiss AA. 2014. Distillation technology–still young and full of breakthrough opportunities. J Chem Technol Biotechnol. 89(4):479–498. doi:https://doi.org/10.1002/jctb.4262
- Kong X, Liu X, Shi R, Lee KY. 2015. Wind speed prediction using reduced support vector machines with feature selection. Neurocomputing. 169:449–456. doi:https://doi.org/10.1016/j.neucom.2014.09.090
- Lennart L. 1999. System identification: theory for the user. Upper Saddle River, NJ: PTR Prentice Hall. p. 28.
- Li R, Ye Q, Suo X, Dai X, Yu H. 2016. Heat-integrated pressure-swing distillation process for separation of a maximum-boiling azeotrope ethylenediamine/water. Chem Eng Res Des. 105:1–15. doi:https://doi.org/10.1016/j.cherd.2015.10.038
- Linnhoff B, Dunford H, Smith R. 1983. Heat integration of distillation columns into overall processes. Chem Eng Sci. 38(8):1175–1188. doi:https://doi.org/10.1016/0009-2509(83)80039-6
- Luyben WL. 2013. Distillation design and control using Aspen simulation. Bethlehem, PA: Wiley.
- Ma J, Li M, Chen H, Huang K, Wei N, Xia C. 2013. A comparative study of controlling the externally heat-integrated double distillation columns (ehiddic). Chem Eng Res Des. 91(12):2299–2308. doi:https://doi.org/10.1016/j.cherd.2013.04.013
- MacMurray JC, Himmelblau D. 1995. Modeling and control of a packed distillation column using artificial neural networks. Comput Chem Eng. 19(10):1077–1088. doi:https://doi.org/10.1016/0098-1354(94)00098-9
- Mah RS, Nicholas JJ Jr, Wodnik RB. 1977. Distillation with secondary reflux and vaporization: a comparative evaluation. AIChE J. 23(5):651–658. doi:https://doi.org/10.1002/aic.690230505
- Mokhtia M, Eftekhari M, Saberi-Movahed F. 2020. Feature selection based on regularization of sparsity based regression models by hesitant fuzzy correlation. Appl Soft Comput. 91:106255. doi:https://doi.org/10.1016/j.asoc.2020.106255
- Naito K, Nakaiwa M, Huang K, Endo A, Aso K, Nakanishi T, Nakamura T, Noda H, Takamatsu T. 2000. Operation of a bench-scale ideal heat integrated distillation column (HiDiC): an experimental study. Comput Chem Eng. 24(2–7):495–499. doi:https://doi.org/10.1016/S0098-1354(00)00513-5
- Najafzadeh M, Etemad-Shahidi A, Lim SY. 2016. Scour prediction in long contractions using ANFIS and SVM. Ocean Eng. 111:128–135. doi:https://doi.org/10.1016/j.oceaneng.2015.10.053
- Najafzadeh M, Homaei F, Farhadi H. 2021. Reliability assessment of water quality index based on guidelines of national sanitation foundation in natural streams: integration of remote sensing and data-driven models. Artif Intell Rev. 54(6):4619–4633. doi:https://doi.org/10.1007/s10462-021-10007-1
- Najafzadeh M, Oliveto G. 2020. Riprap incipient motion for overtopping flows with machine learning models. J Hydroinformatics. 22(4):749–767. doi:https://doi.org/10.2166/hydro.2020.129
- Najafzadeh M, Oliveto G. 2021. Exploring 3D wave-induced scouring patterns around subsea pipelines with artificial intelligence techniques. Appl Sci. 11(9):3792. doi:https://doi.org/10.3390/app11093792
- Nakaiwa M, Huang K, Endo A, Ohmori T, Akiya T, Takamatsu T. 2003. Internally heat-integrated distillation columns: a review. Chem Eng Res Des. 81(1):162–177. doi:https://doi.org/10.1205/026387603321158320
- Nakaiwa M, Huang K, Owa M, Akiya T, Nakane T, Sato M, Takamatsu T. 1997. Energy savings in heat-integrated distillation columns. Energy. 22(6):621–625. doi:https://doi.org/10.1016/S0360-5442(96)00157-0
- Nakaiwa M, Huang K, Owa M, Akiya T, Nakane T, Takamatsu T. 1998. Operating an ideal heat integrated distillation column with different control algorithms. Comput Chem Eng. 22:S389–S393. doi:https://doi.org/10.1016/S0098-1354(98)00079-9
- Nakanishi T, Takamatsu T, Nakaiwa M, Aso K, Noda H, Kuratani N. 1999. A case study of HiDiC design and energy saving. Comput Chem Eng. 23:S855–S858. doi:https://doi.org/10.1016/S0098-1354(99)80210-5
- Ndlovu P. 2017. Commissioning of a refrigerant test unit and assessing the performance of refrigerant blends [PhD thesis]. NUST Zimbabwe: University of KwaZulu-Natal.
- Nooraii A, Romagnoli J, Figueroa J. 1999. Process identification, uncertainty characterisation and robustness analysis of a pilot-scale distillation column. J Process Control. 9(3):247–264. doi:https://doi.org/10.1016/S0959-1524(98)00042-0
- Nørgård PM, Ravn O, Poulsen NK, Hansen LK. 2000. Neural networks for modelling and control of dynamic systems-a practitioner’s handbook. London: Springer.
- Passino KM. 2002. Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag. 22(3):52–67.
- Ponce GHSF, Alves M, Miranda JC, Maciel Filho R, Maciel MRW. 2015. Using an internally heat-integrated distillation column for ethanol–water separation for fuel applications. Chem Eng Res Des. 95:55–63. doi:https://doi.org/10.1016/j.cherd.2015.01.002
- Ramchandran S, Rhinehart RR. 1995. A very simple structure for neural network control of distillation. J Process Control. 5(2):115–128. doi:https://doi.org/10.1016/0959-1524(95)90348-I
- Ramli NM, Hussain MA, Jan BM, Abdullah B. 2014. Composition prediction of a debutanizer column using equation based artificial neural network model. Neurocomputing. 131:59–76. doi:https://doi.org/10.1016/j.neucom.2013.10.039
- Rezaei-Ravari M, Eftekhari M, Saberi-Movahed F. 2021. Regularizing extreme learning machine by dual locally linear embedding manifold learning for training multi-label neural network classifiers. Eng Appl Artif Intell. 97(104062):104062. doi:https://doi.org/10.1016/j.engappai.2020.104062
- Saberi-Movahed F, Eftekhari M, Mohtashami M. 2020. Supervised feature selection by constituting a basis for the original space of features and matrix factorization. Int J Mach Learn Cyber. 11(7):1405–1417. doi:https://doi.org/10.1007/s13042-019-01046-w
- Saberi-Movahed F, Najafzadeh M, Mehrpooya A. 2020. Receiving more accurate predictions for longitudinal dispersion coefficients in water pipelines: training group method of data handling using extreme learning machine conceptions. Water Resour Manage. 34(2):529–561. doi:https://doi.org/10.1007/s11269-019-02463-w
- Sadeghi G, Najafzadeh M, Ameri M. 2020a. Thermal characteristics of evacuated tube solar collectors with coil inside: an experimental study and evolutionary algorithms. Renew Energy. 151:575–588. doi:https://doi.org/10.1016/j.renene.2019.11.050
- Sadeghi G, Najafzadeh M, Safarzadeh H. 2020b. Utilizing gene-expression programming in modelling the thermal performance of evacuated tube solar collectors. J Storage Mater. 30:101546. doi:https://doi.org/10.1016/j.est.2020.101546
- Seader JD, Henley EJ, Roper DK. 1998. Separation process principles. Vol. 25. New York: Wiley.
- Shahandeh H, Ivakpour J, Kasiri N. 2014. Internal and external HiDiCs (heat-integrated distillation columns) optimization by genetic algorithm. Energy. 64:875–886. doi:https://doi.org/10.1016/j.energy.2013.10.042
- Sriniwas GR, Arkun Y, Chien I-L, Ogunnaike BA. 1995. Nonlinear identification and control of a high-purity distillation column: a case study. J Process Control. 5(3):149–162. doi:https://doi.org/10.1016/0959-1524(95)97302-9
- Sulaiman MH, Mustafa MW, Shareef H, Khalid SNA. 2012. An application of artificial bee colony algorithm with least squares support vector machine for real and reactive power tracing in deregulated power system. Int J Electr Power Energy Syst. 37(1):67–77. doi:https://doi.org/10.1016/j.ijepes.2011.12.007
- Tian Y, Fu M, Wu F. 2015. Steel plates fault diagnosis on the basis of support vector machines. Neurocomputing. 151:296–303. doi:https://doi.org/10.1016/j.neucom.2014.09.036
- Vapnik V, Vapnik V. 1998. Statistical learning theory. New York: Wiley. p. 156–160.
- Wang Y, Cui P, Zhang Z. 2014. Heat-integrated pressure-swing-distillation process for separation of tetrahydrofuran/methanol with different feed compositions. Ind Eng Chem Res. 53(17):7186–7194. doi:https://doi.org/10.1021/ie500235f
- Wu Q, Xi C, Ding L, Wei C, Ren H, Law R, Dong H, Li XL. 2017. Classification of EMG signals by BFA-optimized GSVCM for diagnosis of fatigue status. IEEE Trans Automat Sci Eng. 14(2):915–930. doi:https://doi.org/10.1109/TASE.2016.2564419
- Xia X-L, Li K, Irwin GW. 2009. Two-stage gene selection for support vector machine classification of microarray data. IJMIC. 8(2):164–171. doi:https://doi.org/10.1504/IJMIC.2009.029029
- Yang D, Liu Y, Li S, Li X, Ma L. 2015. Gear fault diagnosis based on support vector machine optimized by artificial bee colony algorithm. Mech Mach Theory. 90:219–229. doi:https://doi.org/10.1016/j.mechmachtheory.2015.03.013
- Yin S, Zhu X, Jing C. 2014. Fault detection based on a robust one class support vector machine. Neurocomputing. 145:263–268. doi:https://doi.org/10.1016/j.neucom.2014.05.035
- Yuan B, Yang Z, Yang A, Tao J, Ren J, Wei S, Shen W. 2021. Target localization optimization of a superstructure triple-column extractive distillation with four-parallel evaporator organic Rankine cycles system based on advanced exergy analysis. Sep Purif Technol. 272:118894. doi:https://doi.org/10.1016/j.seppur.2021.118894
- Zhang X, Huang K, Chen H, Wang S. 2011. Comparing three configurations of the externally heat-integrated double distillation columns (EHIDDICs). Comput Chem Eng. 35(10):2017–2033. doi:https://doi.org/10.1016/j.compchemeng.2010.11.008
- Zhao H-B, Yin S. 2009. Geomechanical parameters identification by particle swarm optimization and support vector machine. Appl Math Model. 33(10):3997–4012. doi:https://doi.org/10.1016/j.apm.2009.01.011
- Zhong M, Nie X, Yan A, Yuan Q. 2013. Carcinogenicity prediction of noncongeneric chemicals by a support vector machine. Chem Res Toxicol. 26(5):741–749.