Development of computational methods for estimation of current efficiency and cell voltage in a Chlor-alkali membrane cell

References

• Abdolbaghi, S., A. Mohamadnazar, M. Hasanipanah, and A. Barati-Harooni. 2020. ‘Comparison between a soft computing model and thermodynamic models for prediction of phase equilibria in binary mixtures containing 1-alkanol, n-alkane, and CO2ʹ, Fluid Phase Equilibria, Vol. 503, 112307, Elsevier, Netherlands.
   Google Scholar
• Ashrafizadeh, S. N., F. Mohammadi, A. Sattari, and N. Shojaikaveh. 2010. ‘Prediction of Cell Voltage and Chlorine Current Efficiency of Aqueous HCl Electrolysis Utilizing an Oxygen Reducing Cathode Based on Artificial Neural Network’. ECS Transactions 25 (33):27–42. doi:10.1149/1.3334789.
   Google Scholar
• Baghban, A., M. Bahadori, A. S. Lemraski, and A. Bahadori. 2016. ‘Prediction of solubility of ammonia in liquid electrolytes using least square support vector machines’. Ain Shams Engineering Journal 9:1303–1312.
   Google Scholar
• Barati-Harooni, A., A. Najafi-Marghmaleki, M. Arabloo, and A. H. Mohammadi. 2016. ‘An accurate CSA-LSSVM model for estimation of densities of ionic liquids’. Journal of Molecular Liquids 224:954–64. doi:10.1016/j.molliq.2016.10.027.
   Web of Science ®Google Scholar
• Barati-Harooni, A., A. Najafi-Marghmaleki, S.-A. Hoseinpour, A. Tatar, A. Karkevandi-Talkhooncheh, A. Hemmati-Sarapardeh, and A. H. Mohammadi. 2019. ‘Estimation of minimum miscibility pressure (MMP) in enhanced oil recovery (EOR) process by N2 flooding using different computational schemes’. Fuel 235:1455–74. doi:10.1016/j.fuel.2018.08.066.
   Web of Science ®Google Scholar
• Barati-Harooni, A., A. Najafi-Marghmaleki, and A. H. Mohammadi. 2016. ‘ANFIS modeling of ionic liquids densities’. Journal of Molecular Liquids 224:965–75. doi:10.1016/j.molliq.2016.10.050.
   Web of Science ®Google Scholar
• Bemani, A., A. Baghban, A. H. Mohammadi, and P. Ø. Andersen. 2020. ‘Estimation of adsorption capacity of CO2, CH4, and their binary mixtures in Quidam shale using LSSVM: Application in CO2 enhanced shale gas recovery and CO2 storage’. Journal of Natural Gas Science and Engineering 76:103204. doi:10.1016/j.jngse.2020.103204.
   Web of Science ®Google Scholar
• Cao, B., J. Zhao, Z. Lv, Y. Gu, P. Yang, and S. K. Halgamuge. 2020. ‘Multiobjective Evolution of Fuzzy Rough Neural Network via Distributed Parallelism for Stock Prediction’. IEEE Transactions on Fuzzy Systems 28 (5):939–52. doi:10.1109/TFUZZ.2020.2972207.
   Web of Science ®Google Scholar
• Chao, L., K. Zhang, Z. Li, Y. Zhu, J. Wang, and Z. Yu. 2018. ‘Geographically weighted regression based methods for merging satellite and gauge precipitation’. Journal of Hydrology 558:275–89. doi:10.1016/j.jhydrol.2018.01.042.
   Web of Science ®Google Scholar
• Chen, H., A. Chen, L. Xu, H. Xie, H. Qiao, Q. Lin, and K. Cai. 2020. ‘A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources’. Agricultural Water Management 240:106303. doi:10.1016/j.agwat.2020.106303.
   Web of Science ®Google Scholar
• Chen, S., M. K. Hassanzadeh-Aghdam, and R. Ansari. 2018. An analytical model for elastic modulus calculation of SiC whisker-reinforced hybrid metal matrix nanocomposite containing SiC nanoparticles. Journal of Alloys and Compounds 767:632–41. doi:10.1016/j.jallcom.2018.07.102.
   Web of Science ®Google Scholar
• Chikhi, M., M. Rakib, P. Viers, S. Laborie, A. Hita, and G. Durand. 2002. ‘Current distribution in a chlor-alkali membrane cell: Experimental study and modeling’. Desalination 149 (1–3):375–81. doi:10.1016/S0011-9164(02)00834-2.
   Web of Science ®Google Scholar
• Dias, A. C., M. J. Pereira, L. Brandao, P. Araujo, and A. Mendes. 2010. ‘Characterization of the Chlor-Alkali Membrane Process by EIS I. Ohmic Resistance’. Journal of the Electrochemical Society 157 (5):E75–E81. doi:10.1149/1.3328487.
   Web of Science ®Google Scholar
• Ershadnia, R., M. A. Amooie, R. Shams, S. Hajirezaie, Y. Liu, S. Jamshidi, and M. R. Soltanian. 2020. ‘Non-Newtonian fluid flow dynamics in rotating annular media: Physics-based and data-driven modeling’. Journal of Petroleum Science and Engineering 185:106641. doi:10.1016/j.petrol.2019.106641.
   Web of Science ®Google Scholar
• Jalali, A. A., F. Mohammadi, and S. N. Ashrafizadeh. 2009. ‘Effects of process conditions on cell voltage, current efficiency and voltage balance of a chlor-alkali membrane cell’. Desalination 237 (1–3):126–39. doi:10.1016/j.desal.2007.11.056.
   Web of Science ®Google Scholar
• Kaveh, N. S., S. N. Ashrafizadeh, and F. Mohammadi. 2008. ‘Development of an artificial neural network model for prediction of cell voltage and current efficiency in a chlor-alkali membrane cell’. Chemical Engineering Research and Design 25 (5):461–72. doi:10.1016/j.cherd.2007.12.009.
   Google Scholar
• Kaveh, N. S., F. Mohammadi, and S. N. Ashrafizadeh. 2009. ‘Prediction of cell voltage and current efficiency in a lab scale chlor-alkali membrane cell based on support vector machines’. Chemical Engineering Journal 147 (2–3):161–72. doi:10.1016/j.cej.2008.06.030.
   Web of Science ®Google Scholar
• Keybondorian, E., B. S. Soulgani, and A. Bemani. 2018. ‘Application of ANFIS-GA algorithm for forecasting oil flocculated asphaltene weight percentage in different operation conditions’. Petroleum Science and Technology 36 (12):862–68. doi:10.1080/10916466.2018.1447960.
   Web of Science ®Google Scholar
• Keybondorian, E., H. Zanbouri, A. Bemani, and T. Hamule. 2017. ‘Estimation of the higher heating value of biomass using proximate analysis’. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 39 (20):2025–30. doi:10.1080/15567036.2017.1400609.
   Web of Science ®Google Scholar
• Li, T., M. Xu, C. Zhu, R. Yang, Z. Wang, and Z. Guan. 2019. ‘A deep learning approach for multi-frame in-loop filter of HEVC’. IEEE Transactions on Image Processing 28 (11):5663–78. doi:10.1109/TIP.2019.2921877.
   PubMed Web of Science ®Google Scholar
• Malmir, P., M. Suleymani, and A. Bemani. 2018. ‘Application of ANFIS-PSO as a novel method to estimate effect of inhibitors on Asphaltene precipitation’. Petroleum Science and Technology 36 (8):597–603. doi:10.1080/10916466.2018.1437637.
   Web of Science ®Google Scholar
• Mazloom, M. S., F. Rezaei, A. Hemmati-Sarapardeh, M. M. Husein, S. Zendehboudi, and A. Bemani. 2020. ‘Artificial Intelligence Based Methods for Asphaltenes Adsorption by Nanocomposites: Application of Group Method of Data Handling, Least Squares Support Vector Machine, and Artificial Neural Networks’. Nanomaterials 10 (5):890. doi:10.3390/nano10050890.
   Web of Science ®Google Scholar
• Mir, M., M. Kamyab, M. J. Lariche, A. Bemani, and A. Baghban. 2018. ‘Applying ANFIS-PSO algorithm as a novel accurate approach for prediction of gas density’. Petroleum Science and Technology 36 (12):820–26. doi:10.1080/10916466.2018.1446176.
   Web of Science ®Google Scholar
• Muthumeenal, A. S. A. S. M., M. Sri Abirami Saraswathi, D. Rana, A. Nagendran, and S. Sridhar. 2018. “Recent research trends in polymer nanocomposite proton exchange membranes for electrochemical energy conversion and storage devices.”. In Membrane Technology: Sustainable Solutions in Water, Health,Energy and Environmental Sectors: Taylor & Francis/CRC Press Boca Raton, FL, 17, pp. 351–374. doi:10.1201/9781315105666.
   Google Scholar
• Najafi-Marghmaleki, A., A. Tatar, A. Barati-Harooni, M. Arabloo, S. Rafiee-Taghanaki, and A. H. Mohammadi. 2018. ‘Reliable modeling of constant volume depletion (CVD) behaviors in gas condensate reservoirs’. Fuel 231:146–56.
   Web of Science ®Google Scholar
• Quan, Q., Z. Hao, H. Xifeng, and L. Jingchun. 2020. ‘Research on water temperature prediction based on improved support vector regression’. Neural Computing and Applications 32:1–10.
   Web of Science ®Google Scholar
• Rana, D., T. Matsuura, and S. M. Javaid Zaidi. 2009. Research and development on polymeric membranes for fuel cells: An overview, In Polymer Membranes for Fuel Cells, 550, Springer: Boston.
   Google Scholar
• Shi, K., J. Wang, Y. Tang, and S. Zhong. 2020. ‘Reliable asynchronous sampled-data filtering of T–S fuzzy uncertain delayed neural networks with stochastic switched topologies’. Fuzzy Sets and Systems 381:1–25.
   Web of Science ®Google Scholar
• Shojaikaveh, N., S. N. Ashrafizadeh, F. Mohammadi, and A. Amerighasrodashti. 2010. ‘Optimization of Predicted Cell Voltage & Caustic Current Efficiency in a Chlor-Alkali Membrane Cell with Application of Genetic Algorithm’. ECS Transactions 25:43–56.
   Google Scholar
• Tatar, A., A. Barati-Harooni, A. Najafi-Marghmaleki, and A. Bahadori. 2017. ‘Accurate prediction of CO2 solubility in eutectic mixture of levulinic acid (or furfuryl alcohol) and choline chloride’. International Journal of Greenhouse Gas Control 58:212–22.
   Web of Science ®Google Scholar
• Tripathi, B. P., and V. K. Shahi. 2011. ‘Organic–inorganic nanocomposite polymer electrolyte membranes for fuel cell applications’. Progress in Polymer Science 36:945–79.
   Web of Science ®Google Scholar
• Yang, S., B. Deng, J. Wang, L. Huiyan, L. Meili, Y. Che, X. Wei, and K. A. Loparo. 2019. ‘Scalable digital neuromorphic architecture for large-scale biophysically meaningful neural network with multi-compartment neurons’. IEEE Transactions on Neural Networks and Learning Systems 31:148–62.
   PubMed Web of Science ®Google Scholar
• Zhu, Q. 2019. ‘Research on Road Traffic Situation Awareness System Based on Image Big Data’. IEEE Intelligent Systems 35:18–26.
   Web of Science ®Google Scholar