Optimizing Electromagnetic Cigarette Heaters Using PSO-NSGA II Algorithm: An Effective Strategy to Improve Temperature Control and Production Rate

References

• Aszyk, J., P. Kubica, J. Namieśnik, A. Kot-Wasik, and A. Wasik. 2018. New approach for e-cigarette aerosol collection by an original automatic aerosol generator utilizing melt-blown non-woven fabric. Analytica Chimica Acta 1038:67–2001. doi:10.1016/j.aca.2018.08.015.
   PubMed Web of Science ®Google Scholar
• Cheng, C.-W., H.-S. Kou, S.-M. Wu, and C.-C. Wang. 2022. A chemometric experimental design with three-step stacking capillary electrophoresis for analysis of five tobacco-specific nitrosamines in cigarette products. Journal of Chromatography A 1677:463283. doi:10.1016/j.chroma.2022.463283.
   PubMed Web of Science ®Google Scholar
• de Falco, B., A. Petridis, P. Paramasivan, A. D. Troise, A. Scaloni, Y. Deeni, W. E. Stephens, and A. Fiore. 2020. Reducing toxic reactive carbonyl species in e-cigarette emissions: Testing a harm-reduction strategy based on dicarbonyl trapping. RSC Advances 10 (36):21535–44. doi:10.1039/D0RA02138E.
   PubMed Web of Science ®Google Scholar
• DeRousseau, M. A., J. R. Kasprzyk, and W. V. Srubar III. 2021. Multi-objective optimization methods for designing low-carbon concrete mixtures. Frontiers in Materials 8:680895. doi:10.3389/fmats.2021.680895.
   Web of Science ®Google Scholar
• Guo, J., J. Ikuemonisan, D. K. Hatsukami, and S. S. Hecht. 2021. Liquid chromatography-nanoelectrospray ionization-high-resolution tandem mass spectrometry analysis of apurinic/apyrimidinic sites in oral cell DNA of cigarette smokers, e-cigarette users, and nonsmokers. Chemical Research in Toxicology 34 (12):2540–48. doi:10.1021/acs.chemrestox.1c00308.
   PubMed Web of Science ®Google Scholar
• Guo, Y., H. Yang, M. Chen, J. Cheng, and D. Gong. 2019. Ensemble prediction-based dynamic robust multi-objective optimization methods. Swarm and Evolutionary Computation 48:156–71. doi:10.1016/j.swevo.2019.03.015.
   Web of Science ®Google Scholar
• Hao, J., S. Luo, and L. Pan. 2021. Computer-aided intelligent design using deep multi-objective cooperative optimization algorithm. Future Generation Computer Systems 124:49–53. doi:10.1016/j.future.2021.05.014.
   Web of Science ®Google Scholar
• He, G., T. Zhang, F. Zheng, and Q. Zhang. 2018. An efficient multi-objective optimization method for water quality sensor placement within water distribution systems considering contamination probability variations. Water Research 143:165–75. doi:10.1016/j.watres.2018.06.041.
   PubMed Web of Science ®Google Scholar
• Kannan, V., A. Fisher, and E. Birgersson. 2021. Monte Carlo assisted sensitivity analysis of a Li-ion battery with a phase change material. Journal of Energy Storage 35:102269. doi:10.1016/j.est.2021.102269.
   Web of Science ®Google Scholar
• Li, Y., A. E. Burns, G. J. P. Burke, M. E. Poindexter, A. K. Madl, K. E. Pinkerton, and T. B. Nguyen. 2020. Application of high-resolution mass spectrometry and a theoretical model to the quantification of multifunctional carbonyls and organic acids in e-cigarette aerosol. Environmental Science & Technology 54 (9):5640–50. doi:10.1021/acs.est.9b07387.
   PubMed Web of Science ®Google Scholar
• Narimani, M., G. da Silva, and K. Mukherjee. 2020. Does ‘Dry Hit’vaping of vitamin E acetate contribute to EVALI? Simulating toxic ketene formation during e-cigarette use. Plos One 15 (9):e0238140. doi:10.1371/journal.pone.0238140.
   PubMed Web of Science ®Google Scholar
• Pavithra, M., R. Rajmohan, T. Ananth Kumar, S. Usharani, and P. Manju Bala. 2022. Investigation of energy optimization for spectrum sensing in distributed cooperative iot network using deep learning techniques. Hybrid Intelligent Approaches for Smart Energy: Practical Applications 6:107–27.
   Google Scholar
• Primavessy, D., J. Metz, S. Schnur, M. Schneider, C.-M. Lehr, and M. Hittinger. 2021. Pulmonary in vitro instruments for the replacement of animal experiments. European Journal of Pharmaceutics and Biopharmaceutics 168:62–75. doi:10.1016/j.ejpb.2021.08.005.
   PubMed Web of Science ®Google Scholar
• Ridha, H. M., C. Gomes, H. Hizam, M. Ahmadipour, A. A. Heidari, and H. Chen. 2021. Multi-objective optimization and multi-criteria decision-making methods for optimal design of standalone photovoltaic system: A comprehensive review. Renewable and Sustainable Energy Reviews 135:110202. doi:10.1016/j.rser.2020.110202.
   Web of Science ®Google Scholar
• She, C., R. Jia, B.-N. Hu, Z.-K. Zheng, Y.-P. Xu, and D. Rodriguez. 2021. Life cycle cost and life cycle energy in zero-energy building by multi-objective optimization. Energy Reports 7:5612–26. doi:10.1016/j.egyr.2021.08.198.
   Web of Science ®Google Scholar
• Singh, A. V., R. S. Maharjan, C. Kromer, P. Laux, A. Luch, T. Vats, V. Chandrasekar, S. P. Dakua, and B.-W. Park. 2021. Advances in smoking related in vitro inhalation toxicology: A perspective case of challenges and opportunities from progresses in lung-on-chip technologies. Chemical Research in Toxicology 34 (9):1984–2002. doi:10.1021/acs.chemrestox.1c00219.
   PubMed Web of Science ®Google Scholar
• Soulet, S., and R. A. Sussman. 2022a. A critical review of recent literature on metal contents in E-Cigarette aerosol. Toxics 10 (9):510. doi:10.3390/toxics10090510.
   PubMed Web of Science ®Google Scholar
• Soulet, S., and R. A. Sussman. 2022b. Critical review of the recent literature on organic byproducts in E-Cigarette aerosol emissions. Toxics 10 (12):714. doi:10.3390/toxics10120714.
   PubMed Web of Science ®Google Scholar
• Zhai, Y., Y. Wang, Y. Huang, and X. Meng. 2019. A multi-objective optimization methodology for window design considering energy consumption, thermal environment and visual performance. Renewable Energy 134:1190–99. doi:10.1016/j.renene.2018.09.024.
   Web of Science ®Google Scholar