Biodiesel has emerged as a viable alternative to fuel, offering a more sustainable and environmentally friendly energy option. The current study explores the modeling and optimization of biodiesel production from wast...
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Biodiesel has emerged as a viable alternative to fuel, offering a more sustainable and environmentally friendly energy option. The current study explores the modeling and optimization of biodiesel production from waste cooking oil using artificial intelligence and geneticalgorithms. The study focuses on enhancing five process parameters: methanol-to-oil molar ratio, catalyst concentration, reaction temperature, reaction time, and stirring speed. The optimization of these parameters is complemented by a life cycle assessment to reduce environmental impact. The approach considers biodiesel yield, high heating value, and energy consumption as output variables, thereby advancing sustainable biodiesel production. The findings indicated that, under optimal conditions (methanol-to-oil ratio of 1:6.9, stirring rate of 500 rpm, reaction duration of 20 s, reaction temperature of 30 degrees C and catalyst concentration of 1), the transesterification process achieved the maximum biodiesel yield of 97.76 %. The optimization reached a low environmental impact in the production of biodiesel in an efficient way. Additionally, SWOT analysis helps to develop strategic methods that can enhance efficiency and increase competitiveness. The research suggests that, by optimizing the chemical process in biodiesel production, it is possible to achieve a high yield and high heating value of the biofuel, along with feasible environmental mitigation strategies.
The efficiency of Solar Water Collector (SWH) is low and in order to increase its thermal performances, various optimization techniques were used. In this paper, a stochastic method (genetic Algorithm (GA)) was adopte...
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The efficiency of Solar Water Collector (SWH) is low and in order to increase its thermal performances, various optimization techniques were used. In this paper, a stochastic method (genetic Algorithm (GA)) was adopted to increase the efficiency of the active SWH under various climatic conditions and for various operating parameters. The optimization model was introducing a lot of objectives in order to evaluate the optimality of the SWH. For the dynamic Reynolds number, solar radiation intensity and ambient temperature, different values of plate emissivity, a different number of the glass cover and velocity of air, the thermal performance were obtained and it is compared with the experimental results. It is established by the studies carried out based upon this algorithm that the maximum thermal efficiency comes out to be 75, 28 %. The application of our results was applied in such application as coupling the SWH with the anaerobic digester. The goal is to support decision makers in the definition of the optimal thermal performance and of the optimal collector flat area in order to give a good compromise between the collector efficiency and the output water temperature in the country.
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