A simulation was needed to simulate generating energy from Jatropha plants’ solid wastes in combined cycle power plants. In this study, a simulation was built to simulate energy generation from gasified Jatropha solid wastes. Solid wastes from Jatropha plants' were generated from biodiesel production to produce 1.53 kg/s biodiesel. An assumption was made these solid wastes were gasified to generate energy. Energy production was simulated using DWSIM software. Solid wastes were classified into three categories; cake, shell, and husk. The amount of cake was calculated to be 1.62 kg/s, the shell calculated to be 3.6 kg/s, and the husk was calculated to be 2.16 kg/s. Therefore, the producer gas for each Jatropha solid waste was estimated according to the literature to be 3.65 m³/s for the cake, 4.86 m³/s for husk, and 8.1 m³/s for the shell. The simulation of generating energy from the syngas was validated using experimental data from the literature. Sensitivity analysis was conducted to find the optimum conditions. The results showed that the energy produced from Jatropha waste was higher than that produced from biodiesel. From the simulation, the net energy generated in the gas turbine section was 21.34 MW and from HRSG 12.472 MW. The water flow rate in the compression section of syngas was found to be 10.18 kg/s, which was converted to steam and added more power to the power plant.

This study aims to determine the optimal location for a biodiesel power plant in the municipality of Jazan based on selection criteria by estimating energy consumption and using spatial analysis within the framework of GIS and Structured Query Language (SQL). Additionally, the study will calculate how much electricity each town needs. After Vision 2030, the user can combine the information from remote sensing (RS) and GIS mapping with electricity demand estimation to determine an appropriate location and evaluate the power plant's efficiency. A range of sources, including the Jazan city base map, satellite images, aerial photographs, landuse map, and demographic data, were used to collect descriptive and geographic data as part of the paper's methodology. This data was first entered into the digital model using the ArcGIS 10.4 program, and it was subsequently categorized according to the model's accuracy. Additionally, the data underwent a number of procedures, including scanning printed maps, georeferencing, and on-screen digitizing, to complete the logical design of the digital model. Additionally, the model was spatially assessed in compliance with the requirements for selecting the optimal location for the biodiesel power plant. The criteria and analysis were arranged using Structured Query Language (SQL) and spatial analysis, which determined the best location for a biodiesel power plant station with the greatest ability to model population increase. Mathematical model equations were used to estimate population size, growth rate, and electrical demands (Brauer and Castillo-Chavez in Mathematical models in population biology and epidemiology: Texts in Applied Mathematics, Springer-Verlag, New York USA., 2012). With a total electricity production capacity of 250,167,600 Kilowatt-hour (KWh), the study and investigation efforts concluded that the optimal site for the biodiesel power plant would be in the Dammed municipality. This study is interesting since it is the first to use GIS to determine the ideal location for a power plant station and produce electricity from biodiesel in Jazan Municipalities, KSA.

This research investigates the development of a mesoporous silica–Prussian blue (HOM-SiO ₂ -PB) composite sorbent, synthesized using a one-pot in situ sol–gel lyotropic liquid crystal method.