ANN applied in the Separation of Isopropanol/Water by Azeotropic Distillation by Pressure Oscillation

Daniel Chuquín-Vasco; Adriana Castillo-Cevallos; Erika Cazorla-García; María Augusta Guadalupe; Geoconda Velasco-Castelo; Fernando Mejía-Peñafiel

doi:10.18321/ectj1671

Authors

Daniel Chuquín-Vasco Universitat Politècnica de València, Valencia, España
Adriana Castillo-Cevallos SOLMA, Advanced Mechanical Solutions, Mechanical Engineering, and Construction Services, Quito, Ecuador
Erika Cazorla-García Escuela Superior Politécnica de Chimborazo (ESPOCH), Riobamba, Ecuador
María Augusta Guadalupe Escuela Superior Politécnica de Chimborazo (ESPOCH), Riobamba, Ecuador
Geoconda Velasco-Castelo Escuela Superior Politécnica de Chimborazo (ESPOCH), Riobamba, Ecuador
Fernando Mejía-Peñafiel Escuela Superior Politécnica de Chimborazo (ESPOCH), Riobamba, Ecuador

DOI:

https://doi.org/10.18321/ectj1671

Keywords:

Artificial neural network (ANN), DWSIM, Diisopropyl ether, Isopropanol, Simulation

Abstract

In this study, an artificial neural network (ANN) was developed to predict the concentrations of isopropanol (IPA) and diisopropyl ether (DIPE) in a pressure-swing azeotropic distillation system for the separation of isopropanol/water mixtures. To build the ANN, a simulation was validated using the open-source software DWSIM, and a sensitivity analysis was performed to determine the output variables (targets) to be predicted: IPA and DIPE molar fractions. Additionally, different experiments were carried out to obtain a set of 400 data pairs for the training and validation of the ANN. The design of the ANN was implemented in MATLAB, using the Bayesian regularization algorithm with 50 neurons in the hidden layer. The mean squared error obtained in the testing phase was 0.00186, and the regression coefficient was 0.964. To validate the ANN, an analysis of variance (ANOVA) was performed with a set of 25 data points, considering the input variables used in the ANN design. After the analysis of variance, it was concluded that the results predicted by the ANN did not present significant differences from the experimental values, with a reliability of 95%. Therefore, the developed ANN can be reliably used to predict the concentrations of IPA and DIPE in an isopropanol/water mixture separation process.

References

(1) G. Moreno-Cedeño, N. Tellería-Mata, S. Villanueva, M. Henríquez, Tech note: technologies for the production of isopropyl alcohol (IPA) / Nota técnica: tecnologías para la producción de alcohol isopropílico (IPA), (2019).

(2) W.J. Chua, G.P. Rangaiah, K. Hidajat, Design and optimization of isopropanol process based on two alternatives for reactive distillation, Chem. Eng. Process.: Process Intensif. 118 (2017) 108–116. Crossref DOI: https://doi.org/10.1016/j.cep.2017.04.021

(3) C. Guang, X. Shi, Z. Zhang, et al., Comparison of heterogeneous azeotropic and pressure-swing distillations for separating the diisopropylether/isopropanol/water mixtures, Chem. Eng. Res. Des. 143 (2019) 249–260. Crossref DOI: https://doi.org/10.1016/j.cherd.2019.01.021

(4) H. Hasanudin, W.R. Asri, L. Andini, et al., Enhanced Isopropyl Alcohol Conversion over Acidic Nickel Phosphate-Supported Zeolite Catalysts, ACS Omega 7 (2022) 38923–38932. Crossref DOI: https://doi.org/10.1021/acsomega.2c04647

(5) A. Yang, S. Jin, W. Shen, et al., Investigation of energy-saving azeotropic dividing wall column to achieve cleaner production via heat exchanger network and heat pump technique, J. Clean. Prod. 234 (2019) 410–422. Crossref DOI: https://doi.org/10.1016/j.jclepro.2019.06.224

(6) E. Palacio González, L.M. Tamayo, M. Asesor, J. Sebastián Gómez, (2013). Separación ternaria de una mezcla azeotrópica isopropanol - agua.

(7) H. Xia, Q. Ye, S. Feng, et al., A novel energy-saving pressure swing distillation process based on self-heat recuperation technology, Energy 141 (2017) 770–781. Crossref DOI: https://doi.org/10.1016/j.energy.2017.09.108

(8) V.N. Kiva, E.K. Hilmen, S. Skogestad, Azeotropic phase equilibrium diagrams: a survey, Chem. Eng. Sci. 58 (2003) 1903–1953. Crossref DOI: https://doi.org/10.1016/S0009-2509(03)00018-6

(9) I.Q. Matos, J.S. Varandas, J.P.L. Santos, Thermodynamic modeling of azeotropic mixtures with [EMIM][TFO] with cubic-plus-association and cubic EOSS, Braz. Soc. Chem. Eng. 35 (2018) 363–372. Crossref DOI: https://doi.org/10.1590/0104-6632.20180352s20160025

(10) A. Avilés-Martínez, N. Medina-Herrera, A. Jiménez-Gutiérrez, et al., Risk Analysis Applied to Bioethanol Dehydration Processes: Azeotropic Distillation versus Extractive Distillation, Comput. Aided Chem. Eng. 37 (2015) 1835–1840. Crossref DOI: https://doi.org/10.1016/B978-0-444-63577-8.50151-0

(11) J. Acosta, A. Arce, J. Martı́nez-Ageitos, et al., Vapor−Liquid Equilibrium of the Ternary System Ethyl Acetate + Hexane + Acetone at 101.32 kPa, J. Chem. Eng. Data 47 (2002) 849–854. Crossref DOI: https://doi.org/10.1021/je0102917

(12) A. Iqbal, S.A. Ahmad, Pressure swing distillation of azeotropic mixture – A simulation study, Perspect. Sci. 820 (2016) 4–6. Crossref DOI: https://doi.org/10.1016/j.pisc.2016.01.001

(13) J. Buitrago, D. Amaya, O. Ramos, Model and simulation of a hydrotreatment reactor for diesel hydrodesulfurization in oil refining, Contemp. Eng. Sci. 10 (2017) 1245–1254. Crossref DOI: https://doi.org/10.12988/ces.2017.710135

(14) M.W. Niu, G.P. Rangaiah, Process Retrofitting via Intensification: A Heuristic Methodology and Its Application to Isopropyl Alcohol Process, Ind. Eng. Chem. Res. 55 (2016) 3614–3629. Crossref DOI: https://doi.org/10.1021/acs.iecr.5b02707

(15) Y. Xu, K.T. Chuang, A.R. Sanger, Design of a Process for Production of Isopropyl Alcohol by Hydration of Propylene in a Catalytic Distillation Column, Chem. Eng. Res. Des. 80 (2002) 686–694. Crossref DOI: https://doi.org/10.1205/026387602760312908

(16) S. Arifin, I.L. Chien, Combined preconcentrator/recovery column design for isopropyl alcohol dehydration process, Ind. Eng. Chem. Res. 46 (2007) 2535–2543. Crossref DOI: https://doi.org/10.1021/ie061446c

(17) C. Guang, X. Zhao, Z. Zhang, et al., Optimal design and performance enhancement of heteroazeotropic and pressure-swing coupling distillation for downstream isopropanol separation, Sep. Purif. Technol. 242 (2020) 116836. Crossref DOI: https://doi.org/10.1016/j.seppur.2020.116836

(18) F. Shi, J. Gao, X. Huang, An affine invariant approach for dense wide baseline image matching, Int. J. Distrib. Sens. Netw., 2016. Crossref DOI: https://doi.org/10.1177/1550147716680826

(19) Q. Guo, Z. He, Z. Wang, Simulating daily PM2.5 concentrations using wavelet analysis and artificial neural network with remote sensing and surface observation data, Chemosphere 340 (2023) 139886. Crossref DOI: https://doi.org/10.1016/j.chemosphere.2023.139886

(20) A.A. Elgibaly, M. Ghareeb, S. Kamel, M. El-Sayed El-Bassiouny, Prediction of gas-lift performance using neural network analysis, AIMS Energy 9 (2021) 355–378. Crossref DOI: https://doi.org/10.3934/energy.2021019

(21) Q. Guo, Z. He, Z. Wang, Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China, Toxics 11 (2023) 51. Crossref DOI: https://doi.org/10.3390/toxics11010051

(22) M. Hamdi, H.A. El Salmawy, R. Ragab, Optimum configuration of a dispatchable hybrid renewable energy plant using artificial neural networks: Case study of Ras Ghareb, Egypt, AIMS Energy 11 (2023) 171–196. Crossref DOI: https://doi.org/10.3934/energy.2023010

(23) Q. Guo, Z. He, Z. Wang, Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models, Front. For. Glob. Change 6 (2023). Crossref DOI: https://doi.org/10.3389/ffgc.2023.1249300

(24) A.A. Aly, B. Saleh, M.M. Bassuoni, et al., Artificial neural network model for performance evaluation of an integrated desiccant air conditioning system activated by solar energy, AIMS Energy 7 (2019) 395–412. Crossref DOI: https://doi.org/10.3934/energy.2019.3.395

(25) Q. Guo, Z. He, Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence. Environ. Sci. Pollut. Res. 28 (2021) 11672–11682. Crossref DOI: https://doi.org/10.1007/s11356-020-11930-6

(26) Z. He, Q. Guo, Z. Wang, X. Li, Prediction of Monthly PM2.5 Concentration in Liaocheng in China Employing Artificial Neural Network, Atmosphere 13 (2022). Crossref DOI: https://doi.org/10.3390/atmos13081221

(27) Q. Guo, Z. He, Z. Wang, Prediction of Hourly PM2.5 and PM10 Concentrations in Chongqing City in China Based on Artificial Neural Network, Aerosol Air Qual. Res. 23 (2023). Crossref DOI: https://doi.org/10.4209/aaqr.220448

(28) A. Kandil, S. Khaled, T. Elfakharany, Prediction of the equivalent circulation density using machine learning algorithms based on real-time data, AIMS Energy 11 (2023) 425–453. Crossref DOI: https://doi.org/10.3934/energy.2023023

(29) Q. Guo, Z. He, S. Li, et al., Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions, Aerosol Air Qual. Res. 20 (2020) 1429–1439. Crossref DOI: https://doi.org/10.4209/aaqr.2020.03.0097

(30) M. Pirdashti, S. Curteanu, M.H. Kamangar, et al., Artificial neural networks: applications in chemical engineering, Rev. Chem. Eng. 29 (2013) 205–239. Crossref DOI: https://doi.org/10.1515/revce-2013-0013

(31) J. Panerati, M.A. Schnellmann, C. Patience, et al., Experimental methods in chemical engineering: Artificial neural networks–ANNs, Can. J. Chem. Eng. 97 (9) (2019) 2372–2382. Crossref DOI: https://doi.org/10.1002/cjce.23507

(32) V.M. Cristea, M. Baigulbayeva, Y. Ongarbayev, et al., Prediction of Oil Sorption Capacity on Carbonized Mixtures of Shungite Using Artificial Neural Networks, Processes 11 (2023) 518. Crossref DOI: https://doi.org/10.3390/pr11020518

(33) D.M. Himmelblau, Applications of artificial neural networks in chemical engineering, Korean J. Chem. Eng. 17 (2000) 373–392. Crossref DOI: https://doi.org/10.1007/BF02706848

(34) Y. Shin, R. Smith, S. Hwang, Development of model predictive control system using an artificial neural network: A case study with a distillation column, J. Clean. Prod. 277 (2020) 124124. Crossref DOI: https://doi.org/10.1016/j.jclepro.2020.124124

(35) L.M. Ochoa-Estopier, M. Jobson, R. Smith, Operational optimization of crude oil distillation systems using artificial neural networks, Comput. Chem. Eng. 59 (2013) 178–185. Crossref DOI: https://doi.org/10.1016/j.compchemeng.2013.05.030

(36) A.C. Dimian, C.S. Bildea, A.A. Kiss, Introduction in Process Simulation, Comput. Aided Chem. Eng. 35 (2014) 35–71. Crossref DOI: https://doi.org/10.1016/B978-0-444-62700-1.00002-4

(37) A.A. Kiss, Advanced distillation technologies - Design, Control and Applications, First, Wiley, Noida, India., 2013. Crossref DOI: https://doi.org/10.1002/9781118543702

(38) G. Soave, S. Gamba, L.A. Pellegrini, SRK equation of state: Predicting binary interaction parameters of hydrocarbons and related compounds, Fluid Ph. Equilibria 299 (2010) 285–293. Crossref DOI: https://doi.org/10.1016/j.fluid.2010.09.012

(39) Z. Feng, W. Shen, G.P. Rangaiah, L. Dong, Design and control of vapor recompression assisted extractive distillation for separating n-hexane and ethyl acetate, Sep. Purif. Technol. 240 (2020) 116655. Crossref DOI: https://doi.org/10.1016/j.seppur.2020.116655

(40) Z. Chen, Z. Zhang, J. Zhou, et al., Efficient Synthesis of Isobutylene Dimerization by Catalytic Distillation with Advanced Heat-Integrated Technology, Ind. Eng. Chem. Res. 60 (2021) 6121–6136. Crossref DOI: https://doi.org/10.1021/acs.iecr.1c00945

(41) V. Singh, I. Gupta, H.O. Gupta, ANN based estimator for distillation—inferential control, Chem. Eng. Process.: Process Intensif. 44 (2005) 785–795. Crossref DOI: https://doi.org/10.1016/j.cep.2004.08.010

(42) F. Pedregosa, G. Varoquaux, A. Gramfort, et al., Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12 (2011) 2825–2830. URL

(43) M.D. Bloice, A. Holzinger, A Tutorial on Machine Learning and Data Science Tools with Python. In: Holzinger, A. (eds) Machine Learning for Health Informatics. Lecture Notes in Computer Science (2016), vol 9605. Springer, Cham. Crossref DOI: https://doi.org/10.1007/978-3-319-50478-0_22

(44) A. Hafiz Al Hariri, A.E. Khalifa, M. Talha, et al., Artificial neural network and differential evolution optimization of a circulated permeate gap membrane distillation unit, Sep. Purif. Technol. 338 (2024) 126517. Crossref DOI: https://doi.org/10.1016/j.seppur.2024.126517

(45) Y. Chen, L. Song, Y. Liu, et al., A Review of the Artificial Neural Network Models for Water Quality Prediction, Appl. Sci. 10 (2020) 5776. Crossref DOI: https://doi.org/10.3390/app10175776

(46) L. Zhang, X. Sun, S. Gao, Temperature prediction and analysis based on improved GA-BP neural network, AIMS Environ. Sci. 9 2022 735–753. Crossref DOI: https://doi.org/10.3934/environsci.2022042

(47) L. Wang, B. Wu, Q. Zhu, Y.R. Zeng, Forecasting Monthly Tourism Demand Using Enhanced Back propagation Neural Network, Neural Process. Lett. 52 (2020) 2607–2636. Crossref DOI: https://doi.org/10.1007/s11063-020-10363-z

(48) K. Suphawan, K. Chaisee, Gaussian process regression for predicting water quality index: A case study on Ping River basin, Thailand, AIMS Environ. Sci. 8 (2021) 268–282. Crossref DOI: https://doi.org/10.3934/environsci.2021018

(49) O. Narivs’kyi, R. Atchibayev, A. Kemelzhanova, et al., Mathematical Modeling of the Corrosion Behavior of Austenitic Steels in Chloride-Containing Media During the Operation of Plate-Like Heat Exchangers, Eurasian Chem.-Technol. J. 24 (2022) 295–301. Crossref DOI: https://doi.org/10.18321/ectj1473

(50) M. Kayri, Predictive Abilities of Bayesian Regularization and Levenberg-Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data, Math. Comput. Appl. 21 (2016) 20. Crossref DOI: https://doi.org/10.3390/mca21020020

(51) S. Bharati, M. Rahman, P. Podder, et al., Comparative Performance Analysis of Neural Network Base Training Algorithm and Neuro-Fuzzy System with SOM for the Purpose of Prediction of the Features of Superconductors. In: Abraham, A., Siarry, P., Ma, K., Kaklauskas, A. (eds) Intelligent Systems Design and Applications. ISDA 2019. Advances in Intelligent Systems and Computing, 1181 (2021). Springer, Cham. Crossref DOI: https://doi.org/10.1007/978-3-030-49342-4_7

(52) L.M. Saini, Peak load forecasting using Bayesian regularization, Resilient and adaptive backpropagation learning based artificial neural networks, Electr. Power Syst. Res. 78 (2008) 1302–1310. Crossref DOI: https://doi.org/10.1016/j.epsr.2007.11.003

(53) D. Chuquin-Vasco, G. Torres-Yanacallo, C. Calderón-Tapia, et al., ANN for the prediction of isobutylene dimerization through catalytic distillation for a preliminary energy and environmental evaluation, AIMS Environ. Sci. 11 (2024) 157–183. Crossref DOI: https://doi.org/10.3934/environsci.2024009

(54) A. Suliman, B. Omarov, Applying Bayesian Regularization for Acceleration of Levenberg-Marquardt based Neural Network Training, Int. J. Interact. Multimed. Artif. Intell. 5 (2018) 68. Crossref DOI: https://doi.org/10.9781/ijimai.2018.04.004

(55) H. Garoosiha, J. Ahmadi, H. Bayat, The assessment of Levenberg–Marquardt and Bayesian Framework training algorithm for prediction of concrete shrinkage by the artificial neural network, Cogent Eng. 6 (2019) 1609179. Crossref DOI: https://doi.org/10.1080/23311916.2019.1609179

(56) D. Chuquín-Vasco, J. Osorio-Getial, N. Chuquín-Vasco, et al., Computational Modeling and Machine Learning for Predicting the Volumetric Flows in Crude Distillation Units: A Detailed Simulation and Validation Approach, Eurasian Chem.-Technol. J. 27 (2025) 111–125. Crossref DOI: https://doi.org/10.18321/ectj1659

(57) D. Chuquin-Vasco, D. Chicaiza-Sagal, C. Calderón-Tapia, et al., Forecasting mixture composition in the extractive distillation of n-hexane and ethyl acetate with n-methyl-2-pyrrolidone through ANN for a preliminary energy assessment, AIMS Energy 12 (2024) 439–463. Crossref DOI: https://doi.org/10.3934/energy.2024020