Prediction of coagulation and flocculation processes using ANN models and fuzzy regression

Zangooei, Hossein; Delnavaz, Mohammad; Asadollahfardi, Gholamreza

doi:10.2166/wst.2016.315

Cited by 35 publications

(7 citation statements)

References 37 publications

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“…Ten years later, Maier et al (2009) used the same data as Van Leeuwen to predict treated water quality (turbidity, color, pH, UV-254, residual alum) and optimal alum dose using a DNN (two-layer ANN) and was able to reduce the standard deviation of the prediction error by 37%. Zangooei et al (2016) used historical jar test data to predict turbidity using pH, initial turbidity, temperature, type of coagulant (e.g., solid or liquid poly aluminum chloride from different vendors), and concentration of coagulant. An MLP with two hidden layers outperformed an RBF ANN and FL regression model and required less time to train.…”

Section: Integration Of Thermal Energy Recovery From Wastewater With ...mentioning

confidence: 99%

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

2022

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The water sector is in the middle of a paradigm shift from focusing on treatment and meeting discharge permit limits to integrated operation that also enables a circular water economy via water reuse, resource recovery, and system level planning and operation. While the sector has gone through different stages of such revolution, from improving energy efficiency to recovering renewable energy and resources, when it comes to the next step of achieving carbon neutrality or negative emission, it falls behind other infrastructure sectors such as energy and transportation. The water sector carries tremendous potential to decarbonize, from technological advancements, to operational optimization, to policy and behavioural changes. This book aims to fill an important gap for different stakeholders to gain knowledge and skills in this area and equip the water community to further decarbonize the industry and build a carbon-free society and economy. The book goes beyond technology overviews, rather it aims to provide a system level blueprint for decarbonization. It can be a reference book and textbook for graduate students, researchers, practitioners, consultants and policy makers, and it will provide practical guidance for stakeholders to analyse and implement decarbonization measures in their professions. ISBN: 9781789061789 (Paperback) ISBN: 9781789061796 (eBook) ISBN: 9781789061802 (ePUB)

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Section: Integration Of Thermal Energy Recovery From Wastewater With ...mentioning

confidence: 99%

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

2022

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“…An advantage of ANNs is that they can correlate large and complex datasets [16,17]. An ANN was previously used to develop and assess a drinking water quality model, and a multilayer perceptron ANN was required in the hydrological modelling [18].…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Factor Analysis and Estimation Model of Water Consumption of Government Institutions in Taiwan

Huang

Lee²,

Lin

et al. 2017

Water

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Models for adequately estimating water consumption in Taiwanese government institutions were developed to assist the government to more accurately predict and account for their water needs. A correlation coefficient matrix of associated factors was constructed based on records per unit of water consumption, describing the impact of various water consumption factors. To understand and quantify the effect of the impact factors, linear and nonlinear regression models, as well as an artificial neural network model were adopted. To account for data variability, the data used for modelling were either fully or partially adopted. For partial adoption, the quartile method was employed to remove any outliers. Analysis of the factors affecting water consumption revealed that the building floor area and number of personnel in an organization had the largest impact on estimated consumption, followed by the number of residential personnel. As the coefficient of variation for the green irrigated area and number of consulting personnel was low, the total area and the total number personnel of water consumption decreased the effectiveness of the model.

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“…The RBF neural networks have an easy architecture. Their structure includes an input layer, a single hidden layer, and an output layer, which, at each output node, makes available a linear combination of the outputs of the hidden‐layer nodes (Zangooei, Delnavaz, & Asadollahfardi, ). Training an RBF comprises two steps.…”

Section: Radial Basis Functionmentioning

confidence: 99%

Application of Artificial Neural Networks to Predict Total Dissolved Solids at the Karaj Dam

Asadollahfardi

Zangooei

Aria

et al. 2017

Environmental Quality Management

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We applied multilayer perceptron (MLP) and radial basis function (RBF) neural networks using data from two water quality monitoring stations at the Karaj Dam in Iran. Input data were calcium ions (Ca2+), magnesium ions (Mg2+), sodium ions (Na+), chloride ions (Cl−), sulfate ( SO 42−), and pH, and the output data were total dissolved solids (TDS). An MLP with one hidden layer containing eight neurons was selected for the upstream water quality station using normalized input data. We developed a second MLP neural network for the downstream station with one hidden layer containing 10 neurons in the hidden layer using normalized input data. Considering applying normalized input data and one hidden layer, the coefficient of determination (R2) and index of agreement (IA) between the observed and the predicted data for the upstream and downstream monitoring stations using the MLP neural networks were 0.985, 0.84, 0.99, and 0.92, respectively. The RBF neural network with 100 neurons in its hidden layer reached the minimum errors between the observed and the predicted results in upstream and downstream stations. The R2 between observed and predicted data for upstream and downstream monitoring stations for the RBF was 0.999 and 0.998, respectively. Data normalization improved the performance of the MLP neural networks. Sensitivity analysis indicated that magnesium is the most effective water quality parameter for predicting TDS, and sulfate is the second most effective water quality parameter affecting TDS prediction at the Karaj Dam.

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Prediction of coagulation and flocculation processes using ANN models and fuzzy regression

Cited by 35 publications

References 37 publications

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

Factor Analysis and Estimation Model of Water Consumption of Government Institutions in Taiwan

Application of Artificial Neural Networks to Predict Total Dissolved Solids at the Karaj Dam

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