Optimization of the concentration of ozone generated by DBD using PSO algorithm for water treatment process


  • Nassour Kamel
  • Said Nemmich
  • Touhami Ghaitaoui
  • Oulad Naoui Brahim El Khalil
  • Yassine Bouroumeid
  • Amar Tilmatine
  • Youcef Halali




ozone generation, DBD, water treatment, particle swarm optimization, PSO


The water treatment process with ozone is influenced by various operating parameters and environmental factors that can impact its efficiency. In this study, experiments were conducted using a Venturi pumping frame to investigate the effects of three controllable variables: oxygen flow height, applied voltage level, and water flow rate. The tests aimed to develop a mathematical model that accurately represents the relationship between these input variables and the resulting ozone concentration in the treated water. The experimental data was analyzed using the MODDE 5.0 software, a specialized application for statistical modeling and design of experiments. By fitting the data to appropriate model equations, a mathematical model was obtained that quantifies the influence of each variable and their interactions on the ozone concentration response. To optimize the process performance, a particle swarm optimization (PSO) algorithm was employed to extract the best-fit parameters for the mathematical model. PSO is a computational technique inspired by the social behavior of bird flocks or fish schools, utilizing a population of candidate solutions that evolve iteratively to converge on the global optimum solution. In this case, PSO searched for the model parameter values that minimized the error between predicted and experimentally measured ozone concentrations, rapidly converging to an accurate solution. The optimized mathematical model enables predicting the ozone concentration under any combination of oxygen flow height, voltage, and water flow rate within the experimental range. This predictive capability facilitates identifying the optimum operating conditions to maximize ozone concentration, thereby enhancing the efficiency of the water treatment process. The model serves as a valuable tool for process control, monitoring, and optimization, ensuring consistent treatment quality while minimizing resource consumption and operational costs.


ARMAGHANI, D.; JAHED, M.; HAJIHASSANI, E.; TONNIZAM MOHAMAD, A.; MARTO; NOORANI, S. A. Blasting-Induced Flyrock and Ground Vibration Prediction through an Expert Artificial Neural Network Based on Particle Swarm Optimization. Arabian Journal of Geosciences, v. 7, n. 12, p. 5383–96, 2014. https://doi.org/10.1007/s12517-013-1174-0.

ASTERIS, P. G.; MOKOS, V. G. Concrete Compressive Strength Using Artificial Neural Networks. Neural Computing and Applications, v. 32, n. 15, p. 11807–26, 2020. https://doi.org/10.1007/s00521-019-04663-2.

BENABDELKRIM, B.; BENATIALLAH, A.; GHAITAOUI, T. (). Parameters Estimation Methods of Thin-Film Solar Module Using Numerical Algorithms and Artificial Neural Networks. In: HATTI, M. (Eds.). Advanced Computational Techniques for Renewable Energy Systems. IC-AIRES 2022. Lecture Notes in Networks and Systems, v. 591. Springer, Cham. 2023. https://doi.org/10.1007/


BORKAR, S. B.; NEGI, M.; ACHARYA, T. R.; LAMICHHANE, P.; KAUSHIK, N.; CHOI, E. H.; KAUSHIK, N. K. Mitigation of T3SS-Mediated Virulence in Waterborne Pathogenic Bacteria by Multi-Electrode Cylindrical-DBD Plasma-Generated Nitric Oxide Water. Chemosphere, v. 350, p. 140997, February 2024. https://doi.org/10.1016/j.chemosphere.2023.140997.

DHAKAL, O. B.; DAHAL, R.; ACHARYA, T. R.; LAMICHHANE, P.; GAUTAM, S.; LAMA, B.; KHANAL, R.; KAUSHIK, N. K.; CHOI, E. H.; CHALISE. R. Effects of Spark Dielectric Barrier Discharge Plasma on Water Sterilization and Seed Germination. Current Applied Physics, v. 54, p. 49–58, October 2023. https://doi.org/10.1016/j.cap.2023.08.006.

DRAOU, A.; NEMMICH, S.; NASSOUR, K.; BENMIMOUN, Y.; TILMATINE, A. Experimental Analysis of a Novel Ozone Generator Configuration for Use in Water Treatment Applications. International Journal of Environmental Studies, v. 76, n. 2, p. 338–50, 2019. https://doi.org/10.1080/00207233.2018.1499698.

EBERHART, R.; KENNEDY, J. A New Optimizer Using Particle Swarm Theory. In: MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Nagoya, Japan: IEEE, 1995. p. 39–43. https://doi.org/10.1109/MHS.1995.494215.

GONG, Y.-J.; LI, J.-J.; ZHOU, Y.; LI, Y.; CHUNG, H. S.-H.; SHI, Y.-H.; ZHANG, J. Genetic Learning Particle Swarm Optimization. IEEE Transactions on Cybernetics 46, n. 10, p. 2277–90, 2016. https://doi.org/10.1109/TCYB.2015.

HARRAG, A.; MESSALTI, S. Three, Five and Seven PV Model Parameters Extraction Using PSO. Energy Procedia, v. 119, p. 767–74, 2017.

HOMOLA, T.; PONGRÁC, B.; ZEMÁNEK, M.; ŠIMEK, M. Efficiency of Ozone Production in Coplanar Dielectric Barrier Discharge. Plasma Chemistry and Plasma Processing, v. 39, p. 1227–42, 2019.

KENNEDY, J.; Eberhart, R. Particle Swarm Optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks, IEEE, v. 4, p. 1942–48, 1995.

MURBAT, H. H. Effects of Applied Voltage And Flow Rates of Ozone Generator Fed by Dry Air and O2 On The Coaxial Wire-Cylinder Reactor By Varying Various Electrodes Parameters, International Open Access. Journal of Modern Engineering Research (IJMER), v. 4, n. 92014, 2014.

NEMMICH, S.; TILMATINE, A.; HAMMADI, N. Set-point identification of an ozone water treatment process. Latin American Applied Research – An International Journal, v. 46, n. 1, p. 1–6, 2016. https://doi.org/10.52292/j.laar.2016.322.

NEMMICH, S.; Nassour, K.; Ramdani, N.; Bellebna, Y.; Boukhoulda, M.; Tilmatine, A. Development and optimization of an ozone food preservation system using response surface modelling (RSM). Carpathian Journal of Food Science and Technology, v. 13, p. 33–46, January 2022. https://doi.org/10.34302/


Predicting the Pile Static Load Test Using Backpropagation Neural Network and Generalized Regression Neural Network – a Comparative Study. n.d. Accessed January 12, 2024. https://www-tandfonline-com.sndl1.arn.dz/doi/epdf/10.1080/


QIN, Q.; CHENG, S.; ZHANG, Q.; LI, L.; SHI, Y. Particle Swarm Optimization With Interswarm Interactive Learning Strategy. IEEE Transactions on Cybernetics, v. 46, n. 10, p. 2238–51, 2016. https://doi.org/10.1109/TCYB.2015.2474153.

SANGRODY, R.; TAHERI, S.; CRETU, A.-M.; POURESMAEIL. E. An Improved PSO-Based MPPT Technique Using Stability and Steady State Analyses Under Partial Shading Conditions. IEEE Transactions on Sustainable Energy, v. 15, n. 1, p. 136–45, 2024. https://doi.org/10.1109/TSTE.2023.3274939.

SHRESTHA, R.; JOSHI, U. M.; SUBEDI, D. P. Experimental Study of Ozone Generation by Atmospheric Pressure Dielectric Barrier Discharge. International Journal of Recent Research and Review, v. 8, n. 4, p. 24–29, 2015.

ŠIMEK, M.; PEKÁREK, S.; PRUKNER, V. Influence of Power Modulation on Ozone Production Using an AC Surface Dielectric Barrier Discharge in Oxygen. Plasma Chemistry and Plasma Processing, v. 30, p. 607–17, 2010.

TAYEB MEHDI, L.; NASSOUR, K.; NEMMICH, S.; EL, N.; ZENAGUI, H.; JBILOU M.; TILMATINE, A. Experimental Study of the Gas Flow Path for a Dielectric Barrier Discharge Ozone Generator Using for Wastewater Fish Hatchery Depollution. International Journal of Plasma Environmental Science and Technology, v. 16, April. 2022. https://doi.org/10.34343/ijpest.2022.16.e01005.

TOUHAMI, G.; Halali, Y.; Laribi, S.; Ahmed, G. E.; Arama, F.; Abbas, M.; Koussa, K.; Arbaoui, I. Estimation of the Photovoltaic Organic Cells/Modules Parameters Using an Improved PSO Optimization Technique. 2023.

WANG, X.; SHAO, T.; QIN, J.; LI, Y.; LONG, X.; JIANG, D.; DING, J. Promotion Effect of Micro-Hole in Dielectric on Ozone Generation of Dielectric Barrier Discharge. Ozone: Science & Engineering, v. 0, n. 0, p. 1–10, 2024. https://doi.org/10.1080/01919512.2023.2301548.

XU, Z.; CHEN, X.; JIN, X.; HU, S.; LAN, Y.; XI, W.; HAN, W.; CHENG, C. Study on the Effective Removal of Chlorpyrifos from Water by Dielectric Barrier Discharge (DBD) Plasma: The Influence of Reactive Species and Different Water Components. Chemical Engineering Journal, v. 473, p. 144755, October 2023. https://doi.org/10.1016/j.cej.2023.144755.

ZHANG, J.; ZHU, X.; WANG, Y.; ZHOU, M. Dual-Environmental Particle Swarm Optimizer in Noisy and Noise-Free Environments. IEEE Transactions on Cybernetics, v. 49, n. 6, p. 2011–21, 2019. https://doi.org/10.1109/TCYB.2018.




How to Cite

Kamel, N., Nemmich, S., Ghaitaoui, T., Khalil, O. N. B. E., Bouroumeid, Y., Tilmatine, A., & Halali, Y. (2024). Optimization of the concentration of ozone generated by DBD using PSO algorithm for water treatment process. STUDIES IN ENGINEERING AND EXACT SCIENCES, 5(1), 1872–1887. https://doi.org/10.54021/seesv5n1-093