Instrumental Determination of Energy Oxygen and BOD5

Ciaccio, Leonard L.

doi:10.2166/wst.1992.0577

Cited by 2 publications

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“…In the literature, biochemical oxygen demand (BOD), chemical oxygen demand (COD), potential of hydrogen (pH), and total suspended solids (TSS) are widely used indicators of wastewater quality[48][49][50][51].In practice, the influent water quality is not measured with online sensors[52,53].CBOD, pH, and TSS are usually measured 2 or 3 times a week. This time span is too long for real-time control purposes[54,55]. Monitoring the waste concentration has been considered in the literature as a way to address the influent quality issue.…”

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Modeling and optimization of wastewater treatment process with a data-driven approach

Wei¹

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The primary objective of this research is to model and optimize wastewater treatment process in a wastewater treatment plant (WWTP). As the treatment process is complex, its operations pose challenges. Traditional physics-based and mathematicalmodels have limitations in predicting the behavior of the wastewater process and optimization of its operations. Automated control and information technology enables continuous collection of data. The collected data contains process information allowing to predict and optimize the process. Although the data offered by the WWTP is plentiful, it has not been fully used to extract meaningful information to improve performance of the plant. A data-driven approach is promising in identifying useful patterns and models using algorithms versed in statistics and computational intelligence. Successful data-mining applications have been reported in business, manufacturing, science, and engineering. The focus of this research is to model and optimize the wastewater treatment process and ultimately improve efficiency of WWTPs. To maintain the effluent quality, the influent flow rate, the influent pollutants including the total suspended solids (TSS) and CBOD, are predicted in short-term and long-term to provide information to efficiently operate the treatment process. To reduce energy consumption and improve energy efficiency, the process of biogas production, activated sludge process and pumping station are modeled and optimized with evolutionary computation algorithms. Modeling and optimization of wastewater treatment processes faces three major challenges. The first one is related to the data. As wastewater treatment includes physical, chemical, and biological processes, and instruments collecting large volumes of data. Many variables in the dataset are strongly coupled. The data is noisy, uncertain, and incomplete. Therefore, several preprocessing algorithms should be used to preprocess the v ABSTRACT The primary objective of this research is to model and optimize wastewater treatment process in a wastewater treatment plant (WWTP). As the treatment process is complex, its operations pose challenges. Traditional physics-based and mathematicalmodels have limitations in predicting the behavior of the wastewater process and optimization of its operations. Automated control and information technology enables continuous collection of data. The collected data contains process information allowing to predict and optimize the process. Although the data offered by the WWTP is plentiful, it has not been fully used to extract meaningful information to improve performance of the plant. A data-driven approach is promising in identifying useful patterns and models using algorithms versed in statistics and computational intelligence. Successful data-mining applications have been reported in business, manufacturing, science, and engineering. The focus of this research is to model and optimize the wastewater treatment process and ultimately improve efficiency of WWTPs. To maintain the effluent quali...

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Modeling and optimization of wastewater treatment process with a data-driven approach

Wei¹

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A data-mining approach to predict influent quality

Kusiak

Verma

Wei

2012

Environ Monit Assess

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In wastewater treatment plants, predicting influent water quality is important for energy management. The influent water quality is measured by metrics such as carbonaceous biochemical oxygen demand (CBOD), potential of hydrogen, and total suspended solid. In this paper, a data-driven approach for time-ahead prediction of CBOD is presented. Due to limitations in the industrial data acquisition system, CBOD is not recorded at regular time intervals, which causes gaps in the time-series data. Numerous experiments have been performed to approximate the functional relationship between the input and output parameters and thereby fill in the missing CBOD data. Models incorporating seasonality effects are investigated. Four data-mining algorithms-multilayered perceptron, classification and regression tree, multivariate adaptive regression spline, and random forest-are employed to construct prediction models with the maximum prediction horizon of 5 days.

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Instrumental Determination of Energy Oxygen and BOD5

Cited by 2 publications

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Modeling and optimization of wastewater treatment process with a data-driven approach

Modeling and optimization of wastewater treatment process with a data-driven approach

A data-mining approach to predict influent quality

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