2篇
您的检索式：作者名="Arjun Tyagi"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Artificial neural network based modeling of liquid membranes for separation of dysprosium显示文摘In recent years,the liquid membrane process has been widely investigated to remove rare earth metals.However,transport modeling of this process requires the accurate values of several parameters,which are difficult to measure.Thus,the accurate simulation of this process is a challenging task.In this study,the artificial neural network(ANN)based approach is used to model the liquid membrane process for removing dysprosium.Experimental results from a previous study were used to train the ANN.Initially,the number of neurons in the hidden layer was optimized.The minimum mean squared error between experimental results and model predictions is found with ten neurons.Model predictions were successfully validated with experimental results with correlation factor(R)of 0.9987,which confirms the authenticity of the trained network.Trained ANN was then used to study the effects of different operating parameters on transport rate.The higher volume ratio of membrane solution to feed solution(3-4)with 50-60 min of operation,higher feed pH(5),HCl concentration in stripping solution of 2 mol/L,and moderate concentration of carrier species(0.5 mol/L)with 0.5×10^(-4) mol/L dysprosium initial concentration are found to be optimum values of operating conditions for maximizing the transport rate. | Jawad Iqbal Arjun Tyagi Manish Jain | 2023 | Journal of Rare Earths2023，41，3： | 0 |
| 2 | Loadability Assessment and Enhancement in Unbalanced Distribution Systems显示文摘Voltage stability is a major concern in heavily loaded distribution networks.Careful determination of control parameters for loadability enhancement may maximize the utilization of distribution networks.In applicable studies,most of the approaches optimize real/reactive power losses for the current operating conditions of the distribution network.Although,these types of approaches increase the stability margin,such an increase may not be sufficient.The most important factor in loadability enhancement is representation of future load scenarios in addressing the optimization problem.In this paper a look ahead approach is developed for loadability enhancement of an unbalanced distribution system.The determination of the critical loading point is conventionally done using continuation power flow,which is computationally very demanding,and also complex for implementation in unbalanced distribution networks.Therefore,a new,computationally very efficient voltage stability indicator is developed here for determination of the loadability limit.The proposed methodology is demonstrated on IEEE 4 bus and 25 bus unbalanced distribution systems with different transformer connections. | Arjun Tyagi Ashu Verma Pradeep R.Bijwe | 2018 | CSEE Journal of Power and Energy Systems2018，4，4： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员