Prediction Model for Evaluating the Raw Water Quality Parameters and Its Significance in Pipe Failures of Nuclear Power Plant

2021 ◽  
pp. 335-345
Author(s):  
P. Suganya ◽  
G. Swaminathan ◽  
B. Anoop ◽  
S. P. Sathiya Prabhakaran ◽  
M. Kavitha
Keyword(s):  
Water Quality ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Prediction Model ◽  
Nuclear Power ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Raw Water ◽  
Raw Water Quality

The study of interrelationship between raw water quality parameters, chlorine demand and the formation of disinfection by-products

2009 ◽  
Vol 34 (13-16) ◽  
pp. 806-811 ◽  
Author(s):  
Md. Pauzi Abdullah ◽  
Lim Fang Yee ◽  
Sadia Ata ◽  
Abass Abdullah ◽  
Basar Ishak ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Raw Water ◽  
Chlorine Demand ◽  
Raw Water Quality ◽  
By Products

Development of automatic coagulant dosage control technology for rapid change of raw water quality parameters

2012 ◽  
Vol 12 (6) ◽  
pp. 918-925 ◽  
Author(s):  
Y. Sangu ◽  
H. Yokoi ◽  
H. Tadokoro ◽  
T. Tachi
Keyword(s):  
Water Quality ◽  
Pilot Scale ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Control Technology ◽  
Raw Water ◽  
Control Logic ◽  
Water Turbidity ◽  
Coagulant Dosage ◽  
Raw Water Quality

An automatic coagulant dosage control technology for water purification plants was developed to deal with rapid changes of raw water quality parameters. Control logic was developed to decide coagulant dosage based on aluminum concentration in rapid mixing tank water based on results of semi-pilot scale experiments. This logic enabled quick feedback on the excess or lack of coagulant. It was found that the aluminum residual rate, which was proposed as an indicator of coagulation reactions, could be given as a function of coagulant dosage and turbidity. The effectiveness of the control logic was verified in semi-pilot scale experiments. Settled water turbidity was within ±0.5 NTU of target value even when raw water turbidity increased rapidly up to 100 NTU.

scholarly journals A Prediction Model Based on Deep Belief Network and Least Squares SVR Applied to Cross-Section Water Quality

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1929
Author(s):  
Jianzhuo Yan ◽  
Ya Gao ◽  
Yongchuan Yu ◽  
Hongxia Xu ◽  
Zongbao Xu
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Prediction Model ◽  
Least Squares ◽  
Quality Parameters ◽  
Deep Belief Network ◽  
Water Quality Parameters ◽  
Series Data ◽  
Belief Network ◽  
The Mean

Recently, the quality of fresh water resources is threatened by numerous pollutants. Prediction of water quality is an important tool for controlling and reducing water pollution. By employing superior big data processing ability of deep learning it is possible to improve the accuracy of prediction. This paper proposes a method for predicting water quality based on the deep belief network (DBN) model. First, the particle swarm optimization (PSO) algorithm is used to optimize the network parameters of the deep belief network, which is to extract feature vectors of water quality time series data at multiple scales. Then, combined with the least squares support vector regression (LSSVR) machine which is taken as the top prediction layer of the model, a new water quality prediction model referred to as PSO-DBN-LSSVR is put forward. The developed model is valued in terms of the mean absolute error (MAE), the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination ( R 2 ). Results illustrate that the model proposed in this paper can accurately predict water quality parameters and better robustness of water quality parameters compared with the traditional back propagation (BP) neural network, LSSVR, the DBN neural network, and the DBN-LSSVR combined model.

Impact of a nuclear power plant on water quality of Embalse del Río Tercero Reservoir, (Córdoba, Argentina)

Hydrobiologia ◽  
1992 ◽  
Vol 246 (2) ◽  
pp. 129-140 ◽  
Author(s):  
Aldo A. Mariazzi ◽  
Jorge L. Donadelli ◽  
Patricia Arenas ◽  
Miguel A. Di Siervi ◽  
Carlos Bonetto
Keyword(s):  
Water Quality ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Del Rio

scholarly journals Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations

2011 ◽  
Vol 15 (8) ◽  
pp. 2693-2708 ◽  
Author(s):  
A. Najah ◽  
A. El-Shafie ◽  
O. A. Karim ◽  
O. Jaafar
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Prediction Model ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Effective Parameters ◽  
Data Set ◽  
Proposed Model ◽  
Input Parameters ◽  
Predicted Values

Abstract. This study examined the potential of Multi-layer Perceptron Neural Network (MLP-NN) in predicting dissolved oxygen (DO) at Johor River Basin. The river water quality parameters were monitored regularly each month at four different stations by the Department of Environment (DOE) over a period of ten years, i.e. from 1998 to 2007. The following five water quality parameters were selected for the proposed MLP-NN modelling, namely; temperature (Temp), water pH, electrical conductivity (COND), nitrate (NO3) and ammonical nitrogen (NH3-NL). In this study, two scenarios were introduced; the first scenario (Scenario 1) was to establish the prediction model for DO at each station based on five input parameters, while the second scenario (Scenario 2) was to establish the prediction model for DO based on the five input parameters and DO predicted at previous station (upstream). The model needs to verify when output results and the observed values are close enough to satisfy the verification criteria. Therefore, in order to investigate the efficiency of the proposed model, the verification of MLP-NN based on collection of field data within duration 2009–2010 is presented. To evaluate the effect of input parameters on the model, the sensitivity analysis was adopted. It was found that the most effective inputs were oxygen-containing (NO3) and oxygen demand (NH3-NL). On the other hand, Temp and pH were found to be the least effective parameters, whereas COND contributed the lowest to the proposed model. In addition, 17 neurons were selected as the best number of neurons in the hidden layer for the MLP-NN architecture. To evaluate the performance of the proposed model, three statistical indexes were used, namely; Coefficient of Efficiency (CE), Mean Square Error (MSE) and Coefficient of Correlation (CC). A relatively low correlation between the observed and predicted values in the testing data set was obtained in Scenario 1. In contrast, high coefficients of correlation were obtained between the observed and predicted values for the test sets of 0.98, 0.96 and 0.97 for all stations after adopting Scenario 2. It appeared that the results for Scenario 2 were more adequate than Scenario 1, with a significant improvement for all stations ranging from 4 % to 8 %.

scholarly journals Limnology of lake Surinsar, Jammu, J&K: Part II – Water chemistry

2009 ◽  
Vol 1 (2) ◽  
pp. 159-165
Author(s):  
D. Slathia ◽  
S. P. S. Dutta
Keyword(s):  
Water Quality ◽  
Electrical Conductivity ◽  
Drinking Water ◽  
Quality Parameters ◽  
International Standards ◽  
Water Quality Parameters ◽  
Total Hardness ◽  
Human Consumption ◽  
Raw Water ◽  
Detectable Limit

Water quality parameters viz. air temperature (15.21 0 C -36 0 C/16.71 0 C - 39.42 0 C), water temperature (13 0 C-32.42 0 C/15 0 C-32.8 0 C), depth (42cm-69.08cm/ 25cm-121.92cm), turbidity (3.88-46.27NTU/3.67-69.39 NTU), salinity (0.10-0.31ppt/ 0.10-0.37ppt), electrical conductivity (0.101-0.172mS/cm/0.114-0.279mS/cm), TDS (49.63-111.78 mg/l/57.64-177.01mg/l), pH (7.92-9.82/7.80-9.09), free CO2 (0-19.22mg/l/0-15.32mg/l), DO (6.82-9.90mg/l/4.65-9.40mg/l), carbonate (0-18.38mg/l/0-20.63mg/l), bicarbonate (60.99-170.70mg/l/77.62-168.70mg/l, chloride (7.41-12.35mg/l/9.59-19.60mg/l), calcium (6.85-38.50mg/l/11.81-140.49mg/l), magnesium (4.62-7.22mg/l/3.86-39.05mg/l), total hardness (40.29-125.50 mg/l/56.61-511.05mg/l), BOD (3.12-5.79mg/l/1.31-16.21 mg/l), COD (17.74-75.42 mg/l/ 26.57-73.03mg/l), sodium (14.2-22.5mg/l/12.2-30.9mg/l), potassium (1.83-4.17mg/l/2.25-6.21mg/l), phosphate (0.048-0.233mg/l/0.008-0.603mg/l), nitrate (0.13-1.3mg/l/0.11-4.08mg/l), sulphate (1.60-19.19mg/l/1.36-15.70mg/l), silicate (0.14-4.23mg/l/0.27-7.05mg/l), iron (0-0.65/0-0.40mg/l), copper (below detectable limit) and zinc (below detectable limit), of lake Surinsar-the only source of drinking water to the inhabitants of the Surinsar village, have been reported monthly, during the year 2002-03/2003-04. WQI range falls from poor (70.45, December; 73.55, October; 74.4, November and 74.56, September/ 74.52, January and 75.36, September), very poor(82.54, February; 89.25, May; 80.76, August and 78.86, January/ 80.89, February; 98.25, April; 80.03, June; 82.26, July; 86.55, October and 83.03, November) to unfit (100.44, June; 101.9, July; 103.86, April and 119.5, March/ 103.73, May; 108.28, March; 122.56, August and 103.72, December). Comparison of range of various water quality parameters of Surinsar lake water, with national and international standards has also revealed that most of these parameters are beyond permissible limits. This clearly indicates the unsuitability of raw water, generally consumed by local inhabitants, for human consumption.

The Chemistry Experience Summary During the Engineering Commissioning Period of Fujian Ningde Nuclear Power Plant

2017 ◽  
Author(s):  
Heng Liu ◽  
Yue Chen ◽  
Fuping Li ◽  
Hui Liu ◽  
Shunlong Yang
Keyword(s):  
Water Quality ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Water Chemistry ◽  
Nuclear Power ◽  
Functional Test ◽  
Fuel Loading ◽  
Line System ◽  
Good Practices ◽  
Commercial Operation

In chemical researchers view, hot functional test is a verification of the Nuclear Power Plant before first fuel loading and commercial operation, which is the preparation for staffs, documents, instruments and sampling systems. So, chemistry department should use its own language, knowledge and experience to express their thoughts and what they have seen during the engineering commissioning period. As the first commercial operation nuclear power plant after Fukushima nuclear accident, during the four units commissioning period, chemical researchers accumulated a lot of good experience and feedbacks in the aspect of construction and commissioning for new nuclear power plant. For example, in order to ensure the personnel skill level, we must make special plans which include staff training, laboratory construction, instrument and on-line system commissioning, and all of these should be timely adjusted and changed in order to be consistent with the engineering progress. In order to ensure the water quality of pipe flushing in different stages, such as cold functional test, hot functional test, we should set a strictly water chemical standard which based on the HAF103, and the standard should have some differences in different stage for one unit. In order to ensure the water chemistry in good performance especially after the unit going into commercial operation, the maintenance plan for equipment and system must be formulated, and then, a detailed monitoring plan must be executed. At the same time, a strict system flushing controlling mode can also provide a great benefits for water chemistry quality, especially in the period of start-up. In addition to these above experiences, chemistry researchers of Ningde nuclear power plant also accumulated a lot of good practices and feedbacks about dealing with some abnormal water quality activities, which can’t be founded in commercial operation unit. For example, the aluminum (Al) content in the primary increased rapidly and beyond the specification limits in hot functional test and so on. This article will share the good practices and feedbacks of the first phase of Fujian Ningde nuclear power plant. We hope these good practices and experience feedbacks can provide good reference for the other new nuclear plants in the stage of design, construction, operation and maintenance in the future.

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