# Novel Hybrid Optimization Techniques to Enhance Reliability from Reverse Osmosis Desalination Process

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## Abstract

**:**

## 1. Introduction

- The optimum sizing of the hybrid solar and wind energy with storage and ROD is constructed using the proposed HCRS algorithm.
- The system’s reliability is estimated by using the loss of power supply probability (LPSP) factor along with the estimation of life cycle cost (LCC). The LCC is used to estimate the environmental impacts of the system. Even though solar based has less impact than fossil fuel, it is necessary to estimate the impact.
- Moreover, the continuous and integer decision variables are considered to describe the power required for RO desalination.
- Finally, the proposed approach can be used to analyze these parameters and optimize the results.
- Further, our proposed method mitigates the maintenance and repair cost using the least resources.
- The utilization of energy required for the conversion process is reduced with our proposed approach.

## 2. Literature Survey

## 3. Materials and Methods

- ➢
**Charge state for the Battery (BAT) system**

**During the charging state:**

**During the discharging state:**

- ➢
**The economic model of Reverse Osmosis Desalination (ROD)**

^{3}which includes the desalination unit, pumps energy recovery devices, and membranes. Moreover, the daily desalination water production can be estimated as,

_{T}) of the ROD system, it can be expressed as,

- ➢
**Objective function and limitations**

- ➢
**HCRS algorithm for the Hybrid system**

**Hybrid CRS algorithm**:

#### 3.1. Capuchin Search Algorithm

#### 3.2. Rat Swarm Optimizer (RSO) Algorithm

- (i).
**Prey chasing**:

- (ii).
**Fighting with prey**:

#### 3.3. Hybrid CRS Algorithm

- Initialize the capuchin search algorithm parameters with the maximum number of iterations.
- Capuchins’ movement from one tree to another resembles projectile motion.
- Update the acceleration and velocity.
- Rat’s trying to chase actions are based on their agonistic nature and on searching for prey upon learning its location.
- The process of selecting a location and upgrading is comparable to how rats compete for their prey.
- After calculating the ideal solution, update the search agent locations.

## 4. Results and Experimental Outcomes

**Total life cycle cost (TLCC)**

**CPU time**

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- Hlalele, T.G.; Naidoo, R.M.; Zhang, J.; Bansal, R.C. Dynamic Economic Dispatch With Maximal Renewable Penetration Under Renewable Obligation. IEEE Access
**2020**, 8, 38794–38808. [Google Scholar] [CrossRef] - Alam, S.; Al-Ismail, F.S.; Salem, A.; Abido, M.A. High-Level Penetration of Renewable Energy Sources Into Grid Utility: Challenges and Solutions. IEEE Access
**2020**, 8, 190277–190299. [Google Scholar] [CrossRef] - Rehman, W.U.; Bhatti, A.R.; Awan, A.B.; Sajjad, I.A.; Khan, A.A.; Bo, R.; Haroon, S.S.; Amin, S.; Tlili, I.; Oboreh-Snapps, O. The Penetration of Renewable and Sustainable Energy in Asia: A State-of-the-Art Review on Net-Metering. IEEE Access
**2020**, 8, 170364–170388. [Google Scholar] [CrossRef] - Schefer, H.; Fauth, L.; Kopp, T.H.; Mallwitz, R.; Friebe, J.; Kurrat, M. Discussion on Electric Power Supply Systems for All Electric Aircraft. IEEE Access
**2020**, 8, 84188–84216. [Google Scholar] [CrossRef] - Yu, S.S.; Guo, J.; Chau, T.K.; Fernando, T.L.; Iu, H.H.-C.; Trinh, H. An Unscented Particle Filtering Approach to Decentralized Dynamic State Estimation for DFIG Wind Turbines in Multi-Area Power Systems. IEEE Trans. Power Syst.
**2020**, 35, 2670–2682. [Google Scholar] [CrossRef] - Atif, A.; Khalid, M. Saviztky–Golay Filtering for Solar Power Smoothing and Ramp Rate Reduction Based on Controlled Battery Energy Storage. IEEE Access
**2020**, 8, 33806–33817. [Google Scholar] [CrossRef] - Irfan, M.; Zhao, Z.-Y.; Panjwani, M.K.; Mangi, F.H.; Li, H.; Jan, A.; Ahmad, M.; Rehman, A. Assessing the energy dynamics of Pakistan: Prospects of biomass energy. Energy Rep.
**2019**, 6, 80–93. [Google Scholar] [CrossRef] - Yu, L.; Sun, Y.; Xu, Z.; Shen, C.; Yue, D.; Jiang, T.; Guan, X. Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings. IEEE Trans. Smart Grid
**2020**, 12, 407–419. [Google Scholar] [CrossRef] - Chawda, G.S.; Shaik, A.G.; Mahela, O.P.; Padmanaban, S.; Holm-Nielsen, J.B. Comprehensive Review of Distributed FACTS Control Algorithms for Power Quality Enhancement in Utility Grid With Renewable Energy Penetration. IEEE Access
**2020**, 8, 107614–107634. [Google Scholar] [CrossRef] - Mostafa, M.; Abdullah, H.M.; Mohamed, M.A. Modeling and Experimental Investigation of Solar Stills for Enhancing Water Desalination Process. IEEE Access
**2020**, 8, 219457–219472. [Google Scholar] [CrossRef] - Oikonomou, K.; Parvania, M. Optimal Coordinated Operation of Interdependent Power and Water Distribution Systems. IEEE Trans. Smart Grid
**2020**, 11, 4784–4794. [Google Scholar] [CrossRef] - Subahi, A.F.; Bouazza, K.E. An intelligent IoT-based system design for controlling and monitoring greenhouse temperature. IEEE Access
**2020**, 8, 125488–125500. [Google Scholar] [CrossRef] - Mohamed, M.A.; Almalaq, A.; Awwad, E.M.; El-Meligy, M.A.; Sharaf, M.; Ali, Z.M. An Effective Energy Management Approach within a Smart Island Considering Water-Energy Hub. IEEE Trans. Ind. Appl.
**2020**, 1. [Google Scholar] [CrossRef] - Semshchikov, E.; Negnevitsky, M.; Hamilton, J.M.; Wang, X. Cost-Efficient Strategy for High Renewable Energy Penetration in Isolated Power Systems. IEEE Trans. Power Syst.
**2020**, 35, 3719–3728. [Google Scholar] [CrossRef] - Atallah, M.O.; Farahat, M.; Lotfy, M.E.; Senjyu, T. Operation of conventional and unconventional energy sources to drive a reverse osmosis desalination plant in Sinai Peninsula, Egypt. Renew. Energy
**2019**, 145, 141–152. [Google Scholar] [CrossRef] - Wang, Q.; Liu, J.; Hu, Y.; Zhang, X. Optimal Operation Strategy of Multi-Energy Complementary Distributed CCHP System and its Application on Commercial Building. IEEE Access
**2019**, 7, 127839–127849. [Google Scholar] [CrossRef] - Ouammi, A.; Achour, Y.; Zejli, D.; Dagdougui, H. Supervisory Model Predictive Control for Optimal Energy Management of Networked Smart Greenhouses Integrated Microgrid. IEEE Trans. Autom. Sci. Eng.
**2019**, 17, 117–128. [Google Scholar] [CrossRef] - Poudel, B.; Gokaraju, R. Optimal Operation of SMR-RES Hybrid Energy System for Electricity & District Heating. IEEE Trans. Energy Convers.
**2021**, 36, 3146–3155. [Google Scholar] - Baseer, M.A.; Alsaduni, I.; Zubair, M. A Novel Multi-Objective Based Reliability Assessment in Saudi Arabian Power System Arrangement. IEEE Access
**2021**, 9, 97822–97833. [Google Scholar] [CrossRef] - Baseer, M.A.; Alsaduni, I.; Zubair, M. Novel Hybrid Optimization Maximum Power Point Tracking and Normalized Intelligent Control Techniques for Smart Grid Linked Solar Photovoltaic System. Energy Technol.
**2021**, 9, 2000980. [Google Scholar] [CrossRef] - Zubair, M.; Awan, A.B.; Praveen, R.P.; Baseer, M.A. Solar energy export prospects of the kingdom of saudi arabia. J. Renew. Sustain. Energy
**2019**, 11, 045902. [Google Scholar] [CrossRef] - Praveen, R.P.; Abdul Baseer, M.; Awan, A.; Zubair, M. Performance analysis and optimization of a parabolic trough solar power plant in the middle east region. Energies
**2018**, 11, 741. [Google Scholar] [CrossRef] [Green Version] - Baseer, M.; Praveen, R.P.; Zubair, M.; Khalil, A.G.A.; Al Saduni, I. Performance and Optimization of Commercial Solar PV and PTC Plants. Int. J. Recent Technol. Eng.
**2020**, 8, 1703–1714. [Google Scholar] [CrossRef] - Zubair, M.; Awan, A.B.; Baseer, M.A.; Khan, M.N.; Abbas, G. Optimization of parabolic trough based concentrated solarpower plant for energy export from Saudi Arabia. Energy Rep.
**2021**, 7, 4540–4554. [Google Scholar] [CrossRef] - Alhaj, M.; Al-Ghamdi, S.G. Integrating concentrated solar power with seawater desalination technologies: A multi-regional environmental assessment. Environ. Res. Lett.
**2019**, 14, 074014. [Google Scholar] [CrossRef] - Alqaed, S.; Mustafa, J.; Almehmadi, F. Design and Energy Requirements of a Photovoltaic-Thermal Powered Water Desalination Plant for the Middle East. Int. J. Environ. Res. Public Health
**2021**, 18, 1001. [Google Scholar] [CrossRef] - Ershad, A.M.; Brecha, R.J.; Hallinan, K. Analysis of solar photovoltaic and wind power potential in Afghanistan. Renew. Energy
**2016**, 85, 445–453. [Google Scholar] [CrossRef] - Al-Dousari, A.; Al-Nassar, W.; Al-Hemoud, A.; Alsaleh, A.; Ramadan, A.; Al-Dousari, N.; Ahmed, M. Solar and wind energy: Challenges and solutions in desert regions. Energy
**2019**, 176, 184–194. [Google Scholar] [CrossRef] - Hlal, M.I.; Ramachandaramurthy, V.K.; Sarhan, A.; Pouryekta, A.; Subramaniam, U. Optimum battery depth of discharge for off-grid solar PV/battery system. J. Energy Storage
**2019**, 26, 100999. [Google Scholar] [CrossRef] - Ortiz, X.; Rival, D.; Wood, D. Forces and Moments on Flat Plates of Small Aspect Ratio with Application to PV Wind Loads and Small Wind Turbine Blades. Energies
**2015**, 8, 2438–2453. [Google Scholar] [CrossRef] [Green Version] - Magnor, D.; Sauer, D.U. Optimization of PV Battery Systems Using Genetic Algorithms. Energy Procedia
**2016**, 99, 332–340. [Google Scholar] [CrossRef] - Krishna, H.J. Introduction to desalination technologies. Tex. Water Dev.
**2004**, 2, 1–7. [Google Scholar] - Spiegler, K.; El-Sayed, Y. The energetics of desalination processes. Desalination
**2001**, 134, 109–128. [Google Scholar] [CrossRef] - Ramu, S.; Ranganathan, R.; Ramamoorthy, R. Capuchin search algorithm based task scheduling in cloud computing environment. Yanbu J. Eng. Sci.
**2022**, 19, 18–29. [Google Scholar] [CrossRef] - Wu, Z.; Chen, T.; Wang, H.; Shi, H.; Li, M. Investigate aerodynamic performance of wind turbine blades with vortex generators at the transition area. Wind Eng.
**2022**, 46, 615–629. [Google Scholar] [CrossRef] - Braik, M.; Sheta, A.; Al-Hiary, H. A novel meta-heuristic search algorithm for solving optimization problems: Capuchin search algorithm. Neural Comput. Appl.
**2020**, 33, 2515–2547. [Google Scholar] [CrossRef] - Ma, Y.; Zhao, F.; Hao, H.; Liu, Z. Life-Cycle Cost Analysis of Low-Speed Electric Vehicles Using Different Kinds of Battery Technologies Based on Chinese Market. In Proceedings of the International Conference on Applied Energy, Västerås, Sweden, 12–15 August 2019; p. 4. [Google Scholar]
- Dhiman, G.; Garg, M.; Nagar, A.; Kumar, V.; Dehghani, M. A novel algorithm for global optimization: Rat Swarm Optimizer. J. Ambient. Intell. Humaniz. Comput.
**2020**, 12, 8457–8482. [Google Scholar] [CrossRef] - Tamilarasan, A.; Renugambal, A.; Vijayan, D. Parametric estimation for AWJ cutting of Ti-6Al-4V alloy using Rat swarm optimization algorithm. Mater. Manuf. Process.
**2022**, 37, 1871–1881. [Google Scholar] [CrossRef] - Mirjalili, S.; Lewis, A. The whale optimization algorithm. Adv. Eng. Softw.
**2016**, 95, 51–67. [Google Scholar] [CrossRef] - Bansal, J.C. Particle Swarm Optimization. In Evolutionary and Swarm Intelligence Algorithms; Springer: Cham, Switzerland, 2019; pp. 11–23. [Google Scholar]
- Mirjalili, S. Genetic Algorithm. In Evolutionary Algorithms and Neural Networks; Springer: Cham, Switzerland, 2019; pp. 43–55. [Google Scholar]

**Figure 4.**The comparative result of an optimized hybrid ROD system/battery/solar to various loss of power supply probability.

**Figure 7.**Hybrid energy system-based total life cycle cost (USD) for loss of power supply probability variations.

Methods | Total Life Cycle Cost (USD) | |||
---|---|---|---|---|

Minimum | Maximum | Standard Deviation (SD) | Mean | |

WO | 325,343 | 1,423,423 | 241,881 | 1,231,910 |

PSO | 302,123 | 1,532,452 | 202,321 | 1,613,808 |

GA | 337,163 | 1,614,552 | 210,532 | 1,432,723 |

Proposed | 202,302 | 1,523,134 | 123,454 | 1,213,823 |

Methods | TLCC (USD) | |
---|---|---|

Minimum | Standard Deviation | |

WO | 1,527,834 | 1,854,372 |

PSO | 1,423,124 | 1,943,032 |

GA | 1,232,121 | 1,696,384 |

Proposed | 1,141,010 | 1,563,900 |

Methods | TLCC (USD) | |
---|---|---|

Minimum | Standard Deviation | |

WO | 650,804 | 854,372 |

PSO | 823,184 | 943,032 |

GA | 446,171 | 106,384 |

Proposed | 353,210 | 663,900 |

Methods | CPU Time in Seconds | ||
---|---|---|---|

Minimum | Maximum | Mean | |

WO | 35.45 | 50.12 | 42.34 |

PSO | 39.32 | 52.67 | 40.26 |

GA | 37.32 | 48.23 | 45.32 |

Proposed | 34.21 | 40.23 | 38.10 |

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**MDPI and ACS Style**

Baseer, M.A.; Vinoth Kumar, V.; Izonin, I.; Dronyuk, I.; Velmurugan, A.K.; Swapna, B.
Novel Hybrid Optimization Techniques to Enhance Reliability from Reverse Osmosis Desalination Process. *Energies* **2023**, *16*, 713.
https://doi.org/10.3390/en16020713

**AMA Style**

Baseer MA, Vinoth Kumar V, Izonin I, Dronyuk I, Velmurugan AK, Swapna B.
Novel Hybrid Optimization Techniques to Enhance Reliability from Reverse Osmosis Desalination Process. *Energies*. 2023; 16(2):713.
https://doi.org/10.3390/en16020713

**Chicago/Turabian Style**

Baseer, Mohammad Abdul, Venkatesan Vinoth Kumar, Ivan Izonin, Ivanna Dronyuk, Athyoor Kannan Velmurugan, and Babu Swapna.
2023. "Novel Hybrid Optimization Techniques to Enhance Reliability from Reverse Osmosis Desalination Process" *Energies* 16, no. 2: 713.
https://doi.org/10.3390/en16020713