Best battery storage technologies of solar photovoltaic systems for desalination plant using the results of multi optimization algorithms and sustainable development goals

Salameh, Tareq; Kumar, Polamarasetty P.; Olabi, Abdul Ghani; Obaideen, Khaled; Sayed, Enas Taha; Maghrabie, Hussein M.; Abdelkareem, Mohammad Ali

doi:10.1016/j.est.2022.105312

Cited by 45 publications

(13 citation statements)

References 48 publications

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“…For example, optimization algorithms can be used to optimize the use of resources, such as energy and water, reduce waste, improve efficiency, protect the environment, and many more. The integration of optimization algorithms with SDGs is a promising area of research (Salameh et al, 2022). By using optimization algorithms, we can find solutions that are both efficient and sustainable, and that help us to achieve the SDGs.…”

Section: Sdgs and Their Integration With Optimization Algorithmsmentioning

confidence: 99%

“…The enhanced SOS algorithm and the hybridized SOS with SA algorithm demonstrated their effectiveness in solving the non-preemptive (UPMSP), showcasing improvements in solution quality and computational efficiency (Ezugwu, 2019). Salimifard et al (2021) tackled the problem of Parallel Machine Scheduling Problems (PMSPs) by formulating it as a bi-objective integer linear programming model. The model aimed to minimize two objectives: total tardiness and the number of waste.…”

Section: Swarm Intelligence-based Optimization Algorithms For Upmspmentioning

confidence: 99%

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Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A Concise Overview With a Proof-of-Concept Study

Ezugwu

2024

IEEE Access

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Sustainable development has emerged as a global priority, and industries are increasingly striving to align their operations with sustainable practices. Parallel machine scheduling (PMS) is a critical aspect of production planning that directly impacts resource utilization and operational efficiency. In this paper, we investigate the application of metaheuristic optimization algorithms to address the unrelated parallel machine scheduling problem (UPMSP) through the lens of sustainable development goals (SDGs). The primary objective of this study is to explore how metaheuristic optimization algorithms can contribute to achieving sustainable development goals in the context of UPMSP. We examine a range of metaheuristic algorithms, including genetic algorithms, particle swarm optimization, ant colony optimization, and more, and assess their effectiveness in optimizing the scheduling problem. The evaluation of the algorithms focuses on their ability to improve the optimization of job-to-machine assignments, enabling industries to efficiently minimize the overall makespan of scheduling tasks. This, in turn, leads to waste reduction and enhanced energy efficiency. To conduct a comprehensive analysis, we consider UPMSP instances that incorporate sustainability-related constraints and objectives. We assess the algorithms' performance in terms of solution quality, convergence speed, robustness, and scalability, while also examining their implications for sustainable resource allocation and environmental stewardship. The findings of this study provide insights into the efficacy of metaheuristic optimization algorithms for addressing UPMSP with a focus on sustainable development goals. By leveraging these algorithms, industries can optimize scheduling decisions to minimize waste and enhance energy efficiency. The practical implications of this research are valuable for decision-makers, production planners, and researchers seeking to achieve sustainable development goals in the context of unrelated parallel machine scheduling. By embracing metaheuristic optimization algorithms, businesses can optimize their scheduling processes while aligning with sustainable principles, leading to improved operational efficiency, cost savings, and a positive contribution to the global sustainability agenda.INDEX TERMS sustainable development goals, metaheuristic optimization algorithms, unrelated parallel machine scheduling, resource utilization, energy consumption, environmental impact.

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Section: Sdgs and Their Integration With Optimization Algorithmsmentioning

confidence: 99%

Section: Swarm Intelligence-based Optimization Algorithms For Upmspmentioning

confidence: 99%

Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A Concise Overview With a Proof-of-Concept Study

Ezugwu

2024

IEEE Access

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“…In September 2015, the United Nations General Assembly (UNGA) identified seventeen SDGs. The main aims were to overcome hunger and poverty, protect the planet on land and under water, secure resources, ensure fair access to potable water and hygiene san-itation, provide renewable energy for all generations around the world [85][86][87][88][89], and ensure prosperity and peace by 2030. Numerous countries are aiming to achieve 100% renewable energy utilisation by 2050.…”

Section: Contribution Of Stss To Sdgsmentioning

confidence: 99%

Progress in Solar Thermal Systems and Their Role in Achieving the Sustainable Development Goals

et al. 2022

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The use of solar thermal systems (STSs) has recently reached a significant edge. The increasing research on developing an alternative power supply for limiting fossil fuel usage and climate change are the driving forces of STSs. The current work explores the recent progress in STSs’ applications, including PV/T or “photovoltaic/thermal” systems, zero-energy buildings, greenhouse solar thermal applications, solar thermal for pumping water, solar thermal refrigerators, solar chimneys, water desalination, and solar collectors, along with the benefits and challenges of these applications. Then, the potential contribution of STSs in achieving the various SDGs or “Sustainable development goals”, including barriers and research gaps, are elaborated. In brief, STSs significantly contribute to the seventeen SDGs’ achievement directly and indirectly. Recent developments in the engineering applications of STSs are strongly based on the materials of construction, as well as their design, process optimisation, and integration with multidisciplinary sciences and technologies such as modelling, nanoscience/nanotechnology, and artificial intelligence.

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“…Since waste heat recovery has already proven useful in various other contexts [20][21][22][23], it may be possible to implement it in CSP operations. The energy produced by the different renewable energy sources might be stored using a variety of energy storage systems, including; supercapacitors [24], fuel cells [25], Battery Energy Storage Systems (BESS) [26], thermal energy storage [27], compressed air systems [28] and hydropower dams [29,30].…”

Section: Introductionmentioning

confidence: 99%

Concentrating solar power (CSP) technologies: Status and analysis

Alami¹,

Olabi²,

Rezk³

et al. 2023

International Journal of Thermofluids

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Best battery storage technologies of solar photovoltaic systems for desalination plant using the results of multi optimization algorithms and sustainable development goals

Cited by 45 publications

References 48 publications

Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A Concise Overview With a Proof-of-Concept Study

Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A Concise Overview With a Proof-of-Concept Study

Progress in Solar Thermal Systems and Their Role in Achieving the Sustainable Development Goals

Concentrating solar power (CSP) technologies: Status and analysis

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