Skip to main content
SpringerLink
Log in

A Novel AGPSO3-based ANN Prediction Approach: Application to the RO Desalination Plant

  • Research Article-Chemical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

One of the critical issues faced by the desalination plants is an accurate analysis of their real-time performance. Soft computing techniques are efficient in overcoming these issues and predicting accurate outcomes. In this paper, models based on the third version of the modified particle swarm optimization algorithm called autonomous groups particles swarm optimization—based artificial neural network (AGPSO3-ANN) have been proposed for accurate prediction of permeate flux of reverse osmosis (RO) desalination plant. It employs remarkable optimization strategy that demonstrates superiority than the conventional PSO-ANN techniques, to update acceleration factors (c1 and c2) and to screen the global best solution. Here, four input parameters: evaporator inlet temperature, feedwater salt concentration, condenser inlet temperature, and feed flow rate have been considered for the modeling, and models' performance evaluated in terms of the regression coefficient (R2) and mean square errors (MSE). The results show an impressive agreement between simulated and experimental datasets indicating that the proposed approach is strongly capable of finding optimal solutions to predict accurate results with minimum errors (R2 = 99.2%, MSE = 0.005) compared to the existing ANN approaches. This demonstrates that the proposed models based on such soft computing tools like AGPSO3-ANN are perfect for analyzing and predicting real-time desalination plant performance that would present an effective way for improved process control and efficiency for plant engineers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Availability of Data and Material

References are given to the data as and when it is used.

Code availability

Custom code.

References

  1. Ibrahim, Y.; Ismail, R.A.; Ogungbenro, A.; Pankratz, T.; Banat, F.; Arafat, H.A.: The sociopolitical factors impacting the adoption and proliferation of desalination: a critical review. Desalination 498, 114798 (2021)

    Article  Google Scholar 

  2. Eke, J.; Yusuf, A.; Giwa, A.; Sodiq, A.: The global status of desalination: an assessment of current desalination technologies, plants and capacity. Desalination 495, 114633 (2020)

    Article  Google Scholar 

  3. WHO, UNICEF, Joint Monitoring Programme Progress on Drinking Water, Sanitation and Hygiene., (2017)

  4. Shannon, M.A.; Bohn, P.W.; Elimelech, M.; Georgiadis, J.G.; Marĩas, B.J.; Mayes, A.M.: Science and technology for water purification in the coming decades. Nature 452, 301–310 (2008)

    Article  Google Scholar 

  5. Altmann, T.; Das, R.: Process improvement of sea water reverse osmosis (SWRO) and subsequent decarbonization. Desalination 499, 1–13 (2021)

    Article  Google Scholar 

  6. Elimelech, M.; Phillip, W.A.: The future of seawater desalination: Energy, technology, and the environment. Science 333, 712–717 (2011)

    Article  Google Scholar 

  7. Mahadeva, R.; Manik, G.; Verma, O.P.; Sinha, S.: Modelling and simulation of desalination process using artificial neural network: a review. Desalin. Water Treat. 122, 351–364 (2018)

    Article  Google Scholar 

  8. Mahadeva, R.; Manik, G.; Goel, A.; Dhakal, N.: A review of the artificial neural network based modelling and simulation approaches applied to optimize reverse osmosis desalination techniques. Desalin. Water Treat. 156, 245–256 (2019)

    Article  Google Scholar 

  9. Padhy, N.P.; Simon, S.P.: Soft Computing with MATLAB Programming. Oxford University Press, Oxford (2015)

    Google Scholar 

  10. Goel, A.; Manik, G.; Mahadeva, R.: A review of parabolic trough collector and its modeling. Adv. Intell. Syst. Comput. 1053, 803–813 (2020)

    Article  Google Scholar 

  11. Mahadeva, R.; Kumar, M.; Manik, G.; Patole, S.P.: Modeling, simulation and optimization of the membrane performance of seawater reverse osmosis desalination plant using neural network and fuzzy based soft computing techniques. Desalin. Water Treat. 229, 17–30 (2021)

    Article  Google Scholar 

  12. Mahadeva, R.; Kumar, M.; Patole, S.P.; Manik, G.: Desalination plant performance prediction model using grey wolf optimizer based ANN approach. IEEE Access. 10, 34550–34561 (2022)

    Article  Google Scholar 

  13. Kumar, R.; Mahadeva, R.: An experimental measurement and control of human body stomach using electrical impedance tomography. J. Circuits Syst. Comput. 30, 1–17 (2021)

    Article  Google Scholar 

  14. Mahadeva, R.; Kumar, M.; Patole, S.P.; Manik, G.: Employing artificial neural network for accurate modeling, simulation and performance analysis of an RO-based desalination process. Sustain. Comput. Inf. Syst. 35, 100735 (2022)

    Google Scholar 

  15. Mirjalili, S.; Lewis, A.; Sadiq, A.S.: Autonomous particles groups for particle swarm optimization. Arab. J. Sci. Eng. 39, 4683–4697 (2014)

    Article  MATH  Google Scholar 

  16. Mahadeva, R.; Manik, G.; Verma, O.P.; Goel, A.; Kumar, S.: Modelling and simulation of reverse osmosis system using PSO-ANN prediction technique. Adv. Intell. Syst. Comput. 1053, 1209–1219 (2020)

    Article  Google Scholar 

  17. Chau, K.W.: Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River. J. Hydrol. 329, 363–367 (2006)

    Article  Google Scholar 

  18. Khajeh, M.; Kaykhaii, M.; Sharafi, A.: Application of PSO-artificial neural network and response surface methodology for removal of methylene blue using silver nanoparticles from water samples. J. Ind. Eng. Chem. 19, 1624–1630 (2013)

    Article  Google Scholar 

  19. Buyukyildiz, M.; Tezel, G.; Yilmaz, V.: Estimation of the change in lake water level by artificial intelligence methods. Water Resour. Manag. 28, 4747–4763 (2014)

    Article  Google Scholar 

  20. Khajeh, M.; Sarafraz-Yazdi, A.; Moghadam, A.F.: Modeling of solid-phase tea waste extraction for the removal of manganese and cobalt from water samples by using PSO-artificial neural network and response surface methodology. Arab. J. Chem. 10, S1663–S1673 (2017)

    Article  Google Scholar 

  21. Alizamir, M.; Sobhanardakani, S.: An artificial neural network - particle swarm optimization (ANN- PSO) approach to predict heavy metals contamination in groundwater resources. Jundishapur J. Heal. Sci. 10, e67544 (2018)

    Google Scholar 

  22. Zubaidi, S.L.; Dooley, J.; Alkhaddar, R.M.; Abdellatif, M.; Al-Bugharbee, H.; Ortega-Martorell, S.: A Novel approach for predicting monthly water demand by combining singular spectrum analysis with neural networks. J. Hydrol. 561, 136–145 (2018)

    Article  Google Scholar 

  23. Aryafar, A.; Mikaeil, R.; DoulatiArdejani, F.; ShaffieeHaghshenas, S.; Jafarpour, A.: Application of non-linear regression and soft computing techniques for modeling process of pollutant adsorption from industrial wastewaters. J. Min. Environ. 10, 327–337 (2019)

    Google Scholar 

  24. Sulugodu, B.; Deka, P.C.: Evaluating the performance of CHIRPS satellite rainfall data for streamflow forecasting. Water Resour. Manag. 33, 3913–3927 (2019)

    Article  Google Scholar 

  25. Mei, Y.; Yang, J.; Lu, Y.; Hao, F.; Xu, D.; Pan, H.; Wang, J.: BP–ann model coupled with particle swarm optimization for the efficient prediction of 2-chlorophenol removal in an electro-oxidation system. Int. J. Environ. Res. Public Health. 16, 2454 (2019)

    Article  Google Scholar 

  26. Armaghani, D.J.; Koopialipoor, M.; Marto, A.; Yagiz, S.: Application of several optimization techniques for estimating TBM advance rate in granitic rocks. J. Rock Mech. Geotech. Eng. 11, 779–789 (2019)

    Article  Google Scholar 

  27. Mahadeva, R.; Mehta, R.; Manik, G.; Bhattacharya, A.: An experimental and computational investigation of poly(piperizinamide) thin film composite membrane for salts separation from water using Artificial Neural Network. Desalin. Water Treat. 224, 106–121 (2021)

    Article  Google Scholar 

  28. Gil, J.D.; Ruiz-Aguirre, A.; Roca, L.; Zaragoza, G.; Berenguel, M.: Prediction models to analyse the performance of a commercial-scale membrane distillation unit for desalting brines from RO plants. Desalination 445, 15–28 (2018)

    Article  Google Scholar 

  29. Gao, L.; Zhang, J.; Gray, S.; De Li, J.: Influence of PGMD module design on the water productivity and energy efficiency in desalination. Desalination 452, 29–39 (2019)

    Article  Google Scholar 

  30. Beale, M.H.; Hagan, M.T.; Demuth, H.B.: Neural Network Toolbox, User’s Guide MathWorks. Apple Hill Drive Natick, MA (2018)

    Google Scholar 

  31. Kennedy, J.; Russell, E.: Particle swarm optimization, Proc. . IEEE Int. Conf. (1995) 1942–(1948)

  32. Clerc, M.; Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6, 58–73 (2002)

    Article  Google Scholar 

  33. Ahmadi, M.A.; Soleimani, R.; Bahadori, A.: A computational intelligence scheme for prediction equilibrium water dew point of natural gas in TEG dehydration systems. Fuel 137, 145–154 (2014)

    Article  Google Scholar 

  34. Mahadeva, R.; Kumar, M.; Patole, S.P.; Manik, G.: An optimized PSO-ANN model for improved prediction of water treatment desalination plant performance. Water Supply. 22, 2874–2882 (2022)

    Article  Google Scholar 

Download references

Acknowledgements

The first author wishes to express gratitude to the Ministry of Education (MoE), Government of India, for providing a research scholarship, as well as the Modeling and Simulation Lab, Department of Polymer and Process Engineering, Indian Institute of Technology, Roorkee, India, for carrying out the research.

Funding

The first author receives a monthly stipend from MoE, Govt. of India. Besides, no other financial support is received to conduct this study.

Author information

Authors and Affiliations

  1. Department of Polymer and Process Engineering, Indian Institute of Technology, Roorkee, Uttarakhand, 247667, India

    Rajesh Mahadeva, Anubhav Goel & Gaurav Manik

  2. Department of Electrical Engineering, Indian Institute of Technology, Roorkee, Uttarakhand, 247667, India

    Mahendra Kumar

  3. Department of Physics, Khalifa University of Science and Technology, 127788, Abu Dhabi, United Arab Emirates

    Shashikant P. Patole

Authors
  1. Rajesh Mahadeva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mahendra Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anubhav Goel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shashikant P. Patole
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gaurav Manik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gaurav Manik.

Ethics declarations

Conflict of interest

The authors have declared that they have no conflicts of interest that are relevant to the content of this work.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 20 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mahadeva, R., Kumar, M., Goel, A. et al. A Novel AGPSO3-based ANN Prediction Approach: Application to the RO Desalination Plant. Arab J Sci Eng 48, 15793–15804 (2023). https://doi.org/10.1007/s13369-023-07631-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-023-07631-0

Keywords

Search

Navigation