scholarly journals Stagnation leads to short-term fluctuations in the effluent water quality of biofilters: A problem for greywater reuse?

2021 ◽  
Vol 13 ◽  
pp. 100120 ◽  
Author(s):  
Angelika Hess ◽  
Chiara Baum ◽  
Konstanze Schiessl ◽  
Michael D. Besmer ◽  
Frederik Hammes ◽  
...  
Keyword(s):  
Water Quality ◽  
Effluent Water ◽  
Short Term ◽  
Greywater Reuse

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

scholarly journals Implementation of Long-Short Term Memory Neural Network (LSTM) for Predicting The Water Quality Parameters in Sungai Selangor

2021 ◽  
Vol 6 (4) ◽  
pp. 40-49
Author(s):  
Nur Natasya Mohd Anuar ◽  
Nur Fatihah Fauzi ◽  
Huda Zuhrah Ab Halim ◽  
Nur Izzati Khairudin ◽  
Nurizatul Syarfinas Ahmad Bakhtiar ◽  
...  
Keyword(s):  
Water Quality ◽  
Short Term Memory ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Short Term ◽  
Term Memory ◽  
Quality Of Water ◽  
Long Short Term Memory

Predictions of future events must be factored into decision-making. Predictions of water quality are critical to assist authorities in making operational, management, and strategic decisions to keep the quality of water supply monitored under specific criteria. Taking advantage of the good performance of long short-term memory (LSTM) deep neural networks in time-series prediction, the purpose of this paper is to develop and train a Long-Short Term Memory (LSTM) Neural Network to predict water quality parameters in the Selangor River. The primary goal of this study is to predict five (5) water quality parameters in the Selangor River, namely Biochemical Oxygen Demand (BOD), Ammonia Nitrogen (NH3-N), Chemical Oxygen Demand (COD), pH, and Dissolved Oxygen (DO), using secondary data from different monitoring stations along the river basin. The accuracy of this method was then measured using RMSE as the forecast measure. The results show that by using the Power of Hydrogen (pH), the dataset yielded the lowest RMSE value, with a minimum of 0.2106 at station 004 and a maximum of 1.2587 at station 001. The results of the study indicate that the predicted values of the model and the actual values were in good agreement and revealed the future developing trend of water quality parameters, showing the feasibility and effectiveness of using LSTM deep neural networks to predict the quality of water parameters.

scholarly journals Modeling and monitoring of treated wastewater based on water quality assurance parameters

2019 ◽  
Author(s):  
CI Chemistry International ◽  
Elias Barsenga Hassen ◽  
Abraham M. Asmare
Keyword(s):  
Water Quality ◽  
Wastewater Treatment Plants ◽  
Back Propagation ◽  
Predictive Performance ◽  
Quality Parameters ◽  
Treated Wastewater ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Feed Forward Back Propagation

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

scholarly journals Research of the possibility of using Ceriodaphnia affinis Lilljeborg (Crustacea) in a short-term test while setting eological quality standards in Ukraine

2019 ◽  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Quality Standards ◽  
Environmental Water ◽  
Chemical Substance ◽  
Water Quality Standards ◽  
Short Term ◽  
Test Organisms ◽  
Ceriodaphnia Affinis

Formulation of the problem. Regulation of hazardous chemicals admission to surface water is carried out by means of appropriate water quality standards. Researching the systems of surface water quality regulation in different countries, it has been determined that there is a tendency to use standards for the protection of the aquatic ecosystem and for meeting the needs of society and industries - environmental quality standards. Such standards are based on obtaining ecotoxicological information of a dangerous chemical substance on the representatives of the aquatic ecosystem. Among them, some of the most sensitive test organisms are crustaceans from the Daphniidae family. They are used to establish water quality standards for chemicals, to assess the quality of wastewater and surface water using a toxicological indicator. It is recommended to use a standardized international methodology to assess effects of chemicals on Daphnia magna Straus (OECD No. 202) to establish environmental water quality standards in EU countries. However, in Ukraine, in water protection practice, the most popular test organism is Ceriodaphnia affinis Lilljeborg (Daphnia sp.). The purpose of the article. In order to set ecological standards for water quality of chemicals in Ukraine, the authors proposed to test the OECD No. 202 methodology using Ceriodaphnia affinis test organisms and to establish metrological characteristics for it. Presentation of the main research material. The authors tested the OECD methodology No. 202 [21] on the crustacean culture Ceriodaphnia affinis from the culture collection of the Laboratory of Ecological and Toxicological Research, the V. N. Karazin KhNU. The coefficient of variation of EC50-24 and EC50-48 K2Cr2O7 was 16,8 % and 15,9 % respectively. Based on the data obtained, the metrological characteristics of the tested method were established: the response range of the test organisms Ceriodaphnia affinis is the following – 1,45<EC50-24 <2,91 (mg/dm3); reproducibility of the results of determining the toxicity of a chemical substance – 0,18 mg/dm3 (16,1%); the error in the results of determining the toxicity of a chemical substance – 0,34 mg/dm3 (31.6 %); standard of operational control – 0,49 mg/dm3. Scientific novelty and practical significance. The findings confirm the possibility of using Ceriodaphnia affinis in a short-term test in setting environmental water quality standards in Ukraine.

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