DETERMINATION OF THE OPTIMAL RATE OF FEED OF THE SOLAR DESALINATION PLANTS BY DISTILLATION OF SMALL CAPACITIES PROVIDED WITH A SYSTEM OF REGULATION

Author(s):  
Ousmane Sow ◽  
Thierry Mare ◽  
Jacques Miriel
2020 ◽  
Vol 34 (6) ◽  
pp. 801-806
Author(s):  
Kshitij Khatri ◽  
Nathan S. Boyd

AbstractMetam potassium (metam-K) is a soil fumigant used commonly in Florida at the end of the tomato and pepper production season. The fumigant essentially cleans a field by killing the established weeds and crops after harvest. The goal of this project was to determine the optimal rate of metam-K for the effective termination of tomato, pepper, and established weeds such as purple nutsedge, goosegrass, and dogfennel. Tomato, pepper, and purple nutsedge at bed center were effectively terminated with the metam-K rate of 65 kg ha−1. Optimal rates required for the termination of goosegrass and dogfennel were 91 and 156 kg ha−1, respectively. In contrast, metam-K at 500 to 680 kg ha−1 was required to terminate purple nutsedge on bed edges. The reduced efficacy of metam-K at bed edge might be related to the limited movement of metam-K in soil.


Author(s):  
Ali Bagheri ◽  
Nadia Esfandiari ◽  
Bizhan Honarvar ◽  
Amin Azdarpour

Abstract This study investigated a novel method for increasing desalinated water mass in solar desalination plants. For this purpose, solar panels and a cylindrical parabolic collector (CPC) were used to raise basin water temperature. The effect of different components of basin solar still on freshwater mass was also investigated. The aluminum basin has been associated with maximum water desalination among the different materials constituting a basin. The effects of different colors (e.g. black, brown, and red) on the basin, as well as different water depths (5, 10, and 15 mm), were also explored. The highest amount of freshwater in the black aluminum basin at a 5-mm water depth was 2.97 kg/day. ANN modeling was employed to validate the experimental data, indicating good compliance of experimental data with ANN prediction. According to the results of the simulation with varying numbers of neurons (n = 2–25), the highest and lowest agreement between experimental data and ANN prediction data were related to 24 and 10 neurons, respectively. Under optimum conditions, R2 and %AAD error were 0.993 and 2.654, respectively.


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