Parametric Sensitivity of a Dynamic Model for a Pilot Single-Stage Wastewater Treatment Plant

Plazl, Igor; Pipus, G.; Koloini, T.

doi:10.1007/978-94-017-0932-3_2

Cited by 4 publications

(5 citation statements)

References 2 publications

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“…In addition, the variability of the influent characteristics, in terms of composition, strength and flow rates, might influence model parameters, and consequently operational control, significantly (Hamoda et al, 1999). Therefore, modeling a WWTP is a difficult task and most of the available models are just approximations based on, probably severe, assumptions (Lee and Park, 1999;Cote et al, 1995;Hamoda et al, 1999;Plazl et al, 1999;Bhat et al, 1990).…”

Section: Introductionmentioning

confidence: 97%

“…Wastewater treatment plants involve several complex physical, biological and chemical processes. Often these processes exhibit non-linear behaviors which are difficult to describe by linear mathematical models (Plazl et al, 1999). In addition, the variability of the influent characteristics, in terms of composition, strength and flow rates, might influence model parameters, and consequently operational control, significantly (Hamoda et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

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Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance

Mjalli

Al‐Asheh

Alfadala

2007

Journal of Environmental Management

253

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance

Mjalli

Al‐Asheh

Alfadala

2007

Journal of Environmental Management

253

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“…BOD 5 requires 5-days incubation, and this makes it difficult to find and solve the problematic situation in time. Therefore, modeling a WWTP is a difficult task and most of the available models are just approximate ones based on, probably severe, assumptions (Lee and Park, 1999;Cote et al, 1995;Hamoda et al, 1999;Plazl et al, 1999;Bhat et al, 1990). These features make it difficult to achieve optimum performance of the WWTP using conventional modeling techniques.…”

Section: Introductionmentioning

confidence: 97%

Forecasting Influent-Effluent Wastewater Treatment Plant Using Time Series Analysis and Artificial Neural Network Techniques

Al‐Asheh

Mjalli

Alfadala

2007

Chemical Product and Process Modeling

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We consider the problem of predicting the future behavior of wastewater treatment plant quality indicators by creating prediction models using historical plant data. One of the main aims of this work is to be able to predict plant operational situations in advance so that corrective actions can be taken in time. Sets of historical plant data, such as BOD, COD and TSS were collected for a local wastewater treatment plant in Doha, the capital of the State of Qatar. These variables characterize the performance of any wastewater treatment plant and can be considered as quality indicators of the plant performance. Data were collected over a period of 4 years for the influent and effluent streams of the station. The plant influent and effluent predictions were performed using different techniques. These include time-series analysis, where the ARIMA (Autoregressive Integrated Moving Average) model was implemented in this case, and two Artificial Neural Networks (ANN) algorithms, namely Adaptive Linear Neuron networks (ADALINE) and Multi-layer Feedforward (ML-FF) neural networks. The predictions from the three techniques were presented and compared. The ML-FF model predictions proved to be more reliable than that of the equivalent ARIMA predictions followed by the ADALINE predictions, particularly for the finial effluent stream variables.

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“…WWTPs involve several complex physical, biological and chemical processes. Often these processes exhibit nonlinear behaviors which are difficult to describe by linear mathematical methods [2].…”

Section: Introductionmentioning

confidence: 99%

Biochemical oxygen demand prediction in wastewater treatment plant by using different regression analysis models

BAKİ¹,

Akdemir²,

Aras³

et al. 2019

Desalination and Water Treatment

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The management and operation of the wastewater treatment plants (WWTP) have an important role in the controlling and monitoring of the plants' operations. Various performance data are taken into account in the controlling of the WWTP. The irregularities between operating parameters often lead to management problems that cannot be overcome. The aim of this study is to provide a simple and reliable prediction model to estimate the biochemical oxygen demand (BOD) with specific water quality parameters like wastewater temperature, pH, chemical oxygen demand, suspended sediment, total nitrogen, total phosphorus, electrical conductivity, and input discharge. The data records in this study were measured between June 2015 and May 2016 and obtained from the laboratory of Antalya Hurma WWTP. In the creation of the model, classical regression analysis, multivariate adaptive regression splines (MARS), artificial bee colony, and teaching-learning based optimization were used. The root mean square error and the mean absolute error were used to evaluate performance criteria for each model. When the results of the analyses were compared with each other, it was observed that the MARS method gave better estimation results than the other methods used in the study. As a result, it was evinced that the MARS method produces acceptable results in the BOD estimation.

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Parametric Sensitivity of a Dynamic Model for a Pilot Single-Stage Wastewater Treatment Plant

Cited by 4 publications

References 2 publications

Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance

Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance

Forecasting Influent-Effluent Wastewater Treatment Plant Using Time Series Analysis and Artificial Neural Network Techniques

Biochemical oxygen demand prediction in wastewater treatment plant by using different regression analysis models

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