scholarly journals ROLE OF DIFFERENT PARAMETERS IN THE QUANTIFICATION OF GENERATED SLUDGE IN THE OXYLATOR UNIT OF WATER TREATMENT PLANTS, USING ARTIFICIAL NEURAL NETWORK MODEL (CASE STUDY OF JALALIEH WATER TREATMENT PLANT, TEHRAN, IRAN)

2017 ◽  
Vol 15 (4) ◽  
pp. 129-142 ◽  
Author(s):  
N NAEAMIKHAH
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Treatment ◽  
Artificial Neural Network Model ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Model Case ◽  
Water Treatment Plants

scholarly journals River Water Salinity Impact on Drinking Water Treatment Plant Performance Using Artificial neural network

2019 ◽  
Vol 25 (8) ◽  
pp. 149-159
Author(s):  
Sabreen Hayder Abbas ◽  
Basim Hussein Khudair ◽  
Mahdi Shanshal Jaafar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Treatment ◽  
River Water ◽  
Artificial Neural Network Model ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Water Salinity ◽  
Tigris River ◽  
Artificial Neural

The river water salinity is a major concern in many countries, and salinity can be expressed as total dissolved solids. So, the water salinity impact of the river is one of the major factors effects of water quality. Tigris river water salinity increase with streamline and time due to the decrease in the river flow and dam construction from neighboring countries. The major objective of this research to developed salinity model to study the change of salinity and its impact on the Al-Karkh, Sharq Dijla, Al-Karama, Al-Wathba, Al-Dora, and Al-Wihda water treatment plant along Tigris River in Baghdad city using artificial neural network model (ANN). The parameter used in a model built is (Turbidity, Ec, T.s, S.s, and TDS in) to predict the salinity TDSout.  Results showed that the effectiveness of the artificial neural network model to predicting the salinity is a good agreement between observed and the predicted value of the TDS, through the determination coefficient of the model is (0.998, 0.966, 0.997, 0.998, 0.996, and 0.996) for Al. Karkh, Sharq Dijla, Al.Karama, Al.Wathba, Al.Dora and Al.Wihda respectively. From this value can be shown that ANN is a successful tool for predicting the nonlinear equation of the salinity under different and complicated environmental case along the river.  

Application of artificial neural network to control the coagulant dosing in water treatment plant

2000 ◽  
Vol 42 (3-4) ◽  
pp. 403-408 ◽  
Author(s):  
R.-F. Yu ◽  
S.-F. Kang ◽  
S.-L. Liaw ◽  
M.-c. Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Water Treatment ◽  
Regression Model ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Ann Model ◽  
Coagulant Dosage ◽  
Ann Models

Coagulant dosing is one of the major operation costs in water treatment plant, and conventional control of this process for most plants is generally determined by the jar test. However, this method can only provide periodic information and is difficult to apply to automatic control. This paper presents the feasibility of applying artificial neural network (ANN) to automatically control the coagulant dosing in water treatment plant. Five on-line monitoring variables including turbidity (NTUin), pH (pHin) and conductivity (Conin) in raw water, effluent turbidity (NTUout) of settling tank, and alum dosage (Dos) were used to build the coagulant dosing prediction model. Three methods including regression model, time series model and ANN models were used to predict alum dosage. According to the result of this study, the regression model performed a poor prediction on coagulant dosage. Both time-series and ANN models performed precise prediction results of dosage. The ANN model with ahead coagulant dosage performed the best prediction of alum dosage with a R2 of 0.97 (RMS=0.016), very low average predicted error of 0.75 mg/L of alum were also found in the ANN model. Consequently, the application of ANN model to control the coagulant dosing is feasible in water treatment.

Prediction of Clarified Water Turbidity of Moyog Water Treatment Plant Using Artificial Neural Network

2007 ◽  
Vol 7 (15) ◽  
pp. 2006-2010 ◽  
Author(s):  
Duduku Krishnaiah ◽  
Siva Kumar Kumaresan . ◽  
Matthew Isidore . ◽  
Rosalam Sarbatly .
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Treatment ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Water Turbidity ◽  
Artificial Neural ◽  
Clarified Water

scholarly journals Using Lean Principles in Water Maintenance Operations with Application to Water Treatment Plant

2020 ◽  
Vol 8 (4) ◽  
pp. 1345-1351
Keyword(s):  
Water Treatment ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Maintenance Management ◽  
Water Filtration ◽  
Lean Principles ◽  
Maintenance System ◽  
Water Treatment Plants ◽  
The World

Water maintenance operations and management are so expensive, and costs too much money, Lean principles can serve maintenance sector and any sector like manufacturing and services, since it will reduce costs and efforts of workers. Lean principles first used in Toyota and then distributed in all over the world, it aims to minimize costs and eliminate all wastes in any operation or processes. Costs of water treatment plants water maintenance operations are so high and contains a lot of wastes. This paper aims to apply Lean principles in water maintenance operations in all sectors and then apply them in a water treatment plant as a case study. Also the paper discussing the capability of applying Lean tools like 5S which can be applied on maintenance operation to measure Lean applicability. MTBF, MTTR, 5S audit scores, Availability and reliability are calculated here. The methodology followed in this study depending of reviewing related studies and collecting some data from water filtration station maintenance department, then analyze them and deriving some conclusions to prove the presented claims. Availability and reliability are calculated for some given data from literature and collecting data from special water treatment plant in Kuwait. It is found that applying lean principles and tools like 5’S and others will enhance both availability and reliability of the maintenance system. Applying 5S audit scores shows the statues of the maintenance department and its availability and then reliability to be in good case. The costs and wastes of water maintenance operations in like stations after applying lean are reduced. Applying lean principles like 5’S tool on maintenance management system and water maintenance operations will improve the maintenance quality, MTTR, MTBF, availability and reliability and 5S audit scores of the systems are improved and so number of failures and downtime of the maintained component will be decreased

Sign in / Sign up

Export Citation Format

Share Document