Renewable energy has garnered attention due to the need for sustainable energy sources.Wind power has emerged as an alternative that has contributed to the transition towards cleaner energy.As the importance of wind e...Renewable energy has garnered attention due to the need for sustainable energy sources.Wind power has emerged as an alternative that has contributed to the transition towards cleaner energy.As the importance of wind energy grows,it can be crucial to provide forecasts that optimize its performance potential.Artificial intelligence(AI)methods have risen in prominence due to how well they can handle complicated systems while enhancing the accuracy of prediction.This study explored the area of AI to predict wind-energy production at a wind farm in Yalova,Turkey,using four different AI approaches:support vector machines(SVMs),decision trees,adaptive neuro-fuzzy inference systems(ANFIS)and artificial neural networks(ANNs).Wind speed and direction were considered as essential input parameters,with wind energy as the target parameter,and models are thoroughly evaluated using metrics such as the mean absolute percentage error(MAPE),coefficient of determination(R~2),and mean absolute error(MAE).The findings accentuate the superior performance of the SVM,which delivered the lowest MAPE(2.42%),the highest R~2(0.95),and the lowest MAE(71.21%)compared with actual values,while ANFIS was less effective in this context.The main aim of this comparative analysis was to rank the models to move to the next step in improving the least efficient methods by combining them with optimization algorithms,such as metaheuristic algorithms.展开更多
Morocco produces annually large quantities of phosphogypsum(PG),which permanent stacking rises technical and environmental constraints.However,the valorization of this coproduct in civil engineering and especially in ...Morocco produces annually large quantities of phosphogypsum(PG),which permanent stacking rises technical and environmental constraints.However,the valorization of this coproduct in civil engineering and especially in roads building,is a promising solution within a circular economy frame.A first experimental one-kilometer-long pilot,incorporating four different PG based formulations with a 7%cement addition,built at Safi in 2017,allowed positive mechanical assessment(by deflector)and environmental one(by leaching test).In order to further evaluate PG use as road material,our experimental approach focuses here on optimizing material mixtures-made of phosphogypsum(maximum content desired)treated with cement(to be minimized so as to reduce the cost)and sand or steel slag as granular corrector-to meet mechanical requirements of a road base material.We first identified and characterized phosphogypsum produced at the Jorf Lasfar plant and other materials used.Design of experiment is used for modeling desired physical and mechanical responses and to establish domains meeting the required criteria for using the mixture material either as road subgrade layer or foundation layer.In addition,through a parametric study,we evaluated the effects of traffic level,soil bearing capacity and mechanical performance of treated phosphogypsum mixtures on pavement design for three different pavement structures(mixed,reverse and structure with treated sub-base)and determined that the best to adopt for maximizing PG recycling is the pavement structure with subbase treated with hydraulic binder.展开更多
文摘Renewable energy has garnered attention due to the need for sustainable energy sources.Wind power has emerged as an alternative that has contributed to the transition towards cleaner energy.As the importance of wind energy grows,it can be crucial to provide forecasts that optimize its performance potential.Artificial intelligence(AI)methods have risen in prominence due to how well they can handle complicated systems while enhancing the accuracy of prediction.This study explored the area of AI to predict wind-energy production at a wind farm in Yalova,Turkey,using four different AI approaches:support vector machines(SVMs),decision trees,adaptive neuro-fuzzy inference systems(ANFIS)and artificial neural networks(ANNs).Wind speed and direction were considered as essential input parameters,with wind energy as the target parameter,and models are thoroughly evaluated using metrics such as the mean absolute percentage error(MAPE),coefficient of determination(R~2),and mean absolute error(MAE).The findings accentuate the superior performance of the SVM,which delivered the lowest MAPE(2.42%),the highest R~2(0.95),and the lowest MAE(71.21%)compared with actual values,while ANFIS was less effective in this context.The main aim of this comparative analysis was to rank the models to move to the next step in improving the least efficient methods by combining them with optimization algorithms,such as metaheuristic algorithms.
文摘Morocco produces annually large quantities of phosphogypsum(PG),which permanent stacking rises technical and environmental constraints.However,the valorization of this coproduct in civil engineering and especially in roads building,is a promising solution within a circular economy frame.A first experimental one-kilometer-long pilot,incorporating four different PG based formulations with a 7%cement addition,built at Safi in 2017,allowed positive mechanical assessment(by deflector)and environmental one(by leaching test).In order to further evaluate PG use as road material,our experimental approach focuses here on optimizing material mixtures-made of phosphogypsum(maximum content desired)treated with cement(to be minimized so as to reduce the cost)and sand or steel slag as granular corrector-to meet mechanical requirements of a road base material.We first identified and characterized phosphogypsum produced at the Jorf Lasfar plant and other materials used.Design of experiment is used for modeling desired physical and mechanical responses and to establish domains meeting the required criteria for using the mixture material either as road subgrade layer or foundation layer.In addition,through a parametric study,we evaluated the effects of traffic level,soil bearing capacity and mechanical performance of treated phosphogypsum mixtures on pavement design for three different pavement structures(mixed,reverse and structure with treated sub-base)and determined that the best to adopt for maximizing PG recycling is the pavement structure with subbase treated with hydraulic binder.
