The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
H C Hu - One of the best experts on this subject based on the ideXlab platform.
-
predicting performance of grey and neural network in industrial Effluent using online monitoring parameters
Process Biochemistry, 2008Co-Authors: Shunhsing Chuang, H H Ho, L F Yu, H C Su, H C HuAbstract:Abstract Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids (SSeff), chemical oxygen demand (CODeff) and Pheff in the Effluent from conventional activated process of an industrial wastewater treatment plant using simple online monitoring parameters (Ph in the equalization pond Effluent; Ph, temperature, and dissolved oxygen in the aeration tank). The results indicated that the minimum mean absolute percentage errors of 20.79, 6.09 and 0.71% for SSeff, CODeff and Pheff, respectively, could be achieved using different types of GMs. GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. According to the results, the online monitoring parameters could be applied on the prediction of Effluent quality. It also revealed that GM could predict the industrial Effluent variation as its Effluent data was insufficient.
P K Tewari - One of the best experts on this subject based on the ideXlab platform.
-
recovery and pre concentration of uranium from secondary Effluent using novel resin
International Journal of Nuclear Desalination, 2010Co-Authors: S K Satpati, K N Hareendran, Sanjukta A Kumar, K L Thalor, P K TewariAbstract:The act of enrichment or improving the quality of product concentration, i.e. 'pre-concentration', has been studied with respect to uranium plant Effluent, which contains uranium in 10?30 ppm level intermingled with a huge number of interfering ions, such as magnesium, in percent level. The effects of different operating conditions, such as concentration of uranium in the Effluent, Ph, the time required for uptake of uranium and the effect of the presence of other elements in the Effluent, in batch experiments have been investigated. Using this in-house novel resin for preferential uranium uptake, the sorbed matrix has been eluted with different eluant concentration for further enrichment of radionuclides in the elute. High uptake values for uranium ions prove its selectivity, and fractional elution ensures further reuse of sorbent and significant improvement in uranium enrichment.
Kun Liu - One of the best experts on this subject based on the ideXlab platform.
-
Polymer-coated composite anodes for efficient and stable capacitive deionization
Desalination, 2016Co-Authors: Xinbo Gao, N. Holubowitch, K. Ruh, Ayokunle Omosebi, John Landon, Kun LiuAbstract:In the contemporary literature, diminished salt removal in a CDI device is primarily due to carbon oxidation at the anode in aqueous solutions. Therefore, an anion exchange polymer is used to prepare a composite carbon as a CDI anode. Results from repetitive CDI testing shows that more efficient and consistent long-term salt removal is achieved when a flow-through CDI stack is configured with composite anodes compared to polymer-free anodes. Analysis of the Effluent Ph and steady-state current indicates that this performance improvement may be due to the minimization of parasitic reactions by shielding of the carbon electrodes with the selective polymer layer coated at the anode.
Enrong Xiao - One of the best experts on this subject based on the ideXlab platform.
-
PhosPhorus removal by laboratory-scale unvegetated vertical-flow constructed wetland systems using anthracite, steel slag and related blends as substrate.
Water Science and Technology, 2011Co-Authors: Wu Junmei, Xu Dong, Rong Wang, Xiangling Zhang, Enrong XiaoAbstract:This research aimed to investigate the PhosPhorus (P) removal of a series of laboratory-scale unvegetated vertical-flow constructed wetland systems using anthracite, steel slag and related blends as substrate in treatment of low concentration domestic sewage. The long-term performance of P removal was firstly studied by using single substrate of anthracite or steel slag, and three systems applying various combined substrates were investigated when the average P loading rate varied between 0.9 and 1.5 g TP/m(2).d. The results demonstrated that both anthracite and steel slag systems were highly effective in removing total P (TP, 77.17 +/- 23.34% and 90.26 +/- 4.48%) and soluble reactive P (SRP, 92.14 +/- 12.56% and 96.20 +/- 2.58%). The system filled with anthracite, vermiculite and steel slag from the top down removed 82.45 +/- 9.52% and 87.83 +/- 8.58% of TP and SRP, respectively. However, other combined substrate systems showed comparative low and fluctuant P removal. The Effluent Ph was maintained at 7-9, which met environmental requirements of China. Therefore, anthracite provides a long-term high efficiency of P removal and may be a promising substrate from the standpoint of the Effluent Ph, and the arrangement of combined substrate has a prominent effect on P removal.
Taner Alkay - One of the best experts on this subject based on the ideXlab platform.
-
ARTIFICIAL NEURAL NETWORK APPROACH FOR THE PREDICTION OF EffluentS STREAMS FROM A WASTEWATER TREATMENT PLANT: A CASE STUDY IN KOCAELI (TURKEY)
Muğla Journal of Science and Technology, 2020Co-Authors: Esra Bilgin Şimşek, Taner AlkayAbstract:A three-layer Artificial Neural Network (ANN) model was employed to develop and estimate the Effluent stream parameters of two different wastewater treatment plants (WWTP) in Kocaeli, Turkey. The chemical oxygen demand (COD), suspended solid (SS), Ph and temperature as the output parameters were estimated by five input parameters such as flow rate, COD, Ph, SS and temperature. The ANN model was developed with 400 data sets for prediction of Effluent Ph, temperature, COD and SS. The benchmark tests were employed to achieve an optimum network algorithm. The network model with optimum functions at hidden and output layers were applied for the forecasts of Effluent streams of both WWTPs. The regression values of training, validation and test using this function were found as 0.94, 0.96 and 0.95, respectively. The optimum neuron numbers were determined according to the minimum mean square error values. ANN testing outputs revealed that the model exhibited well performance in forecasting the Effluent Ph, temperature, SS and COD values.
