The application of artificial neural network in wastewater treatment

Hu, Weixuan

doi:10.1109/iccsn.2011.6013606

Cited by 8 publications

(6 citation statements)

References 2 publications

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“…We have to consider that collected data come from a context where the wastewater is highly polluted, and are relative to different processes, each characterised by the usage of different chemical agents and the presence of suspended solids. The concentration of the chemical species affects the absorbance: higher concentrations result in stronger absorbance signals [15,26,27]. Indeed, involving the process in the estimation of COD allows us to obtain a more reliable estimation, since each process exploits different chemicals, even if the samples for each process are poor (a mean of around 10 samples for each process).…”

Section: Resultsmentioning

confidence: 99%

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Estimation of COD from UV-Vis Spectrometer Exploiting Machine Learning in Leather Industries Wastewater

Cardia¹,

Chessa²,

Franceschi³

et al. 2023

World Congress on Civil, Structural, and Environmental Engineering

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We present a method for the analysis of wastewater in the context of the leather industry. In this context, the determination of the Chemical Oxygen Demand parameter is essential for the determination of the degree of water pollution. Conventional methods for measuring it require time-consuming laboratory analysis, sample preparation and the usage of toxic chemicals. The proposed method is based on machine learning and soft sensing, employing nonspecific sensors to derive the quality indicators of wastewater. In particular, we leverage ultraviolet and visible spectroscopy measurements, that provide wastewater absorbance, that is the quantity of light absorbed by a solution, to estimate Chemical Oxygen Demand. We stress that, after deployment, our approach does not require any (timeexpensive) laboratory analyses, and hence it can be used to implement systems of real-time monitoring of wastewater in a leather production context.

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Section: Resultsmentioning

confidence: 99%

“…Indeed, the effluents often contain mixed chemicals, and it is difficult to detect all the components simultaneously through the absorption spectrum. For this reason, machine learning models, able to detect complex non-linear relationships, are largely adopted [15].…”

Section: Related Workmentioning

confidence: 99%

Estimation of COD from UV-Vis Spectrometer Exploiting Machine Learning in Leather Industries Wastewater

Cardia¹,

Chessa²,

Franceschi³

et al. 2023

World Congress on Civil, Structural, and Environmental Engineering

View full text Add to dashboard Cite

show abstract

Section: Related Workmentioning

confidence: 99%

Machine Learning for the Estimation of COD from UV-Vis Spectrometer in Leather Industries Wastewater

Cardia¹,

Chessa²,

Franceschi³

et al. 2023

IJEPR

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In this paper, we introduce a method for analysing wastewater from the leather industry with a specific focus on determining the Chemical Oxygen Demand parameter, which plays a vital role in evaluating water pollution levels. Conventional methods for measuring it involve extensive laboratory analysis, sample preparation, and the usage of hazardous substances. To overcome these limitations, we propose a machine learning-based approach that employs nonspecific sensors and soft sensing techniques to derive indicators of wastewater quality. Our method leverages ultraviolet and visible spectroscopy measurements, which provide valuable insights into the light absorption characteristics of the wastewater sample, enabling us to estimate Chemical Oxygen Demand. Importantly, our approach includes an analysis of the input wavelengths, allowing us to identify the spectra for accurate Chemical Oxygen Demand estimation. Once deployed, our method offers the potential for real-time monitoring systems of wastewater in leather production contexts, by eliminating the need for timeconsuming laboratory analyses.

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“…Nowadays, many machine learning techniques can be used in the prediction process. Perhaps the most widespread of them is the Neural Networks [6]. In the process of forming the architecture of the wastewater pumping decision support system (WPDSS), CBR was selected as the base technique.…”

Section: Introductionmentioning

confidence: 99%

A Decision Support System for the Prediction of Wastewater Pumping Station Failures Based on CBR Continuous Learning Model

Trstenjak

Palasek

Trstenjak

2019

Eng. Technol. Appl. Sci. Res.

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Nowadays the communities are facing the problem of waste and wastewater. While wastewater systems have become more complex, the need for development of sustainable solution for wastewater management emerged. Therefore, the development of a Decision Support System (DSS) for wastewater disposal management became necessary. This paper presents a new DSS for predicting the failure of wastewater pumping stations, the system architecture and its implementation. The prediction model is based on the Case Based Reasoning (CBR) classification method. The standard CBR classification technique has been upgraded with an algorithm for continuous learning. The paper describes the system structure, its connection to the wastewater system, the internal processes involved in the prediction process and the implemented algorithm for continuous learning. Furthermore, the features used in the prediction are indicated along with the achieved results and the method of results evaluation. The test and obtained results indicate that the proposed DSS is efficient and capable of providing very good results in the prediction process.

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The application of artificial neural network in wastewater treatment

Cited by 8 publications

References 2 publications

Estimation of COD from UV-Vis Spectrometer Exploiting Machine Learning in Leather Industries Wastewater

Estimation of COD from UV-Vis Spectrometer Exploiting Machine Learning in Leather Industries Wastewater

Machine Learning for the Estimation of COD from UV-Vis Spectrometer in Leather Industries Wastewater

A Decision Support System for the Prediction of Wastewater Pumping Station Failures Based on CBR Continuous Learning Model

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