Abstract: NEOM City in Saudi Arabia is planned to be the first environmentally friendly city in the world that is powered by renewable energy sources minimizing CO2 emissions to reduce the eect of global warming according to Saudi Arabia’s Vision 2030. In recent years, Saudi Arabia has had a problem with water scarcity. The main factors aecting water security are unequal water distribution, wrong use of water resources and using bad or less ecient irrigation techniques. This paper is aimed to provide a detailed feasibility and techno-economic evaluation of using several scenarios of a stand-alone hybrid renewable energy system to satisfy the electrical energy needs for an environmentally friendly seawater desalination plant which feeds 150 m

This research aims to experimentally improve the overall eciency of solar photovoltaic (PV) panels by coating them with hydrophobic SiO2 nanomaterial. Also, an accurate mathematical model was used to estimate the parameters of the PV panel, which is a non-linear optimization problem. Based on the experimental data and using the particle swarm optimization (PSO) algorithm, the optimal five parameters of a single diode model of a PV panel were determined in this study. This experimental work was conducted and carried out in the Renewable Energy Laboratory of Assiut University, Egypt. A comparative analysis was completed for three identical solar PV panels; the first panel was coated with hydrophobic SiO2 nanomaterial, so it was considered to be a self-cleaning panel; the second panel was uncoated and cleaned manually on a daily basis; and the third panel was kept dusty all the time through the experimental investigation, and was used as a reference. Experimentally, the output power of the PV panels was monitored for each panel in this study. Also, the anti-static and anti-reflection eects of coating solar PV panels with hydrophobic SiO2 nanomaterial were investigated experimentally. According to the obtained experimental results, it was found that the use of SiO2 coating for PV panels results in the better performance of the PV panels. The overall eciency of the coated panel increased by 15% and 5%, compared to the dusty panel and the uncoated panel which was manually cleaned daily, respectively.

This research aims to experimentally improve the overall eciency of solar photovoltaic (PV) panels by coating them with hydrophobic SiO2 nanomaterial. Also, an accurate mathematical model was used to estimate the parameters of the PV panel, which is a non-linear optimization problem. Based on the experimental data and using the particle swarm optimization (PSO) algorithm, the optimal five parameters of a single diode model of a PV panel were determined in this study. This experimental work was conducted and carried out in the Renewable Energy Laboratory of Assiut University, Egypt. A comparative analysis was completed for three identical solar PV panels; the first panel was coated with hydrophobic SiO2 nanomaterial, so it was considered to be a self-cleaning panel; the second panel was uncoated and cleaned manually on a daily basis; and the third panel was kept dusty all the time through the experimental investigation, and was used as a reference. Experimentally, the output power of the PV panels was monitored for each panel in this study. Also, the anti-static and anti-reflection eects of coating solar PV panels with hydrophobic SiO2 nanomaterial were investigated experimentally. According to the obtained experimental results, it was found that the use of SiO2 coating for PV panels results in the better performance of the PV panels. The overall eciency of the coated panel increased by 15% and 5%, compared to the dusty panel and the uncoated panel which was manually cleaned daily, respectively.

Flexible manufacturing systems (FMSs) have widely expanded in industry sectors all over the world. Scheduling is one of the problems that face the life cycle of FMSs. This paper introduces the simultaneous scheduling problem of automated guided vehicles (AGVs) and machines in FMSs. The study is based on discrete event simulation (DES) to solve that problem using benchmark data used by previous studies. The objective of this study is to determine the starting and completion times of each job to minimise the makespan. The results validate the robustness of the simulation model, especially for large-scale problems. Furthermore, they are compared against the other approaches and show consistency.

Flexible manufacturing systems (FMSs) have widely expanded in industry sectors all over the world. Scheduling is one of the problems that face the life cycle of FMSs. This paper introduces the simultaneous scheduling problem of automated guided vehicles (AGVs) and machines in FMSs. The study is based on discrete event simulation (DES) to solve that problem using benchmark data used by previous studies. The objective of this study is to determine the starting and completion times of each job to minimise the makespan. The results validate the robustness of the simulation model, especially for large-scale problems. Furthermore, they are compared against the other approaches and show consistency.

Flexible manufacturing systems (FMSs) have widely expanded in industry sectors all over the world. Scheduling is one of the problems that face the life cycle of FMSs. This paper introduces the simultaneous scheduling problem of automated guided vehicles (AGVs) and machines in FMSs. The study is based on discrete event simulation (DES) to solve that problem using benchmark data used by previous studies. The objective of this study is to determine the starting and completion times of each job to minimise the makespan. The results validate the robustness of the simulation model, especially for large-scale problems. Furthermore, they are compared against the other approaches and show consistency.

Flexible manufacturing systems (FMSs) have widely expanded in industry sectors all over the world. Scheduling is one of the problems that face the life cycle of FMSs. This paper introduces the simultaneous scheduling problem of automated guided vehicles (AGVs) and machines in FMSs. The study is based on discrete event simulation (DES) to solve that problem using benchmark data used by previous studies. The objective of this study is to determine the starting and completion times of each job to minimise the makespan. The results validate the robustness of the simulation model, especially for large-scale problems. Furthermore, they are compared against the other approaches and show consistency.

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