Estimation of biochemical oxygen demand in slurry and effluents using ultra-violet spectrophotometry

Brookman, S. K. E.

doi:10.1016/s0043-1354(96)00250-3

Cited by 38 publications

(29 citation statements)

References 5 publications

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“…This result emphasised the robustness of using multiple information sources, to indicate changes in COD over time as well as the need for a non-linear modelling tool for this particular application. This is because using a single wavelength may not effectively characterise the organic composition of raw sewage effluent over time [3]. Overall the proposed method worked well at predicting COD values, based on the spectral absorbance profile for two types of wastewater.…”

Section: Spectral Absorbance Profilingmentioning

confidence: 99%

“…The reason ANNs have been applied to this problem, instead of using traditional mathematical modelling techniques, is that they have enabled the entire spectral absorbance pattern to be processed to indirectly quantify COD and biochemical oxygen demand (BOD) values, instead of one or two selected wavelengths, as seen in past studies [3][4][5]. This was because one of the major limitations of the spectroscopic approach to COD and BOD measurements was that they were based on one specific wavelength to indicate the change.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of chemical oxygen demand by ultraviolet spectroscopic profiling and artificial neural networks

Fogelman

Blumenstein

Zhao

2005

Neural Comput & Applic

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A simple method based on the mathematical treatment of spectral absorbance profiles in conjunction with artificial neural networks (ANNs) is demonstrated for rapidly estimating chemical oxygen demand (COD) values of wastewater samples. In order to improve spectroscopic analysis and ANN training time as well as to reduce the storage space of the trained ANN algorithm, it is necessary to decrease the ANN input vector size by extracting unique characteristics from the raw input pattern. Key features from the spectral absorbance pattern were therefore selected to obtain the spectral absorbance profile, reducing the ANN input vector from 160 to 10 selected inputs. The results indicate that the COD values obtained from the selected absorbance profiles agreed well with those obtained from the entire absorbance pattern. The spectral absorbance profile technique was also compared to COD values estimated by a multiple linear regression (MLR) model to validate whether ANNs were better and more robust models for rapid COD analysis. It was found that the ANN model predicted COD values closer to standard COD values than the MLR model.

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Section: Spectral Absorbance Profilingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of chemical oxygen demand by ultraviolet spectroscopic profiling and artificial neural networks

Fogelman

Blumenstein

Zhao

2005

Neural Comput & Applic

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“…Sewage-impacted rivers were characterized by both high fulvic-like F int and tryptophan T int fluorescence intensity (Baker, 2001), and it was demonstrated that fluorescence quenching by metal ions was not important, due to the high concentration of organic matter in the wastewater. Farm wastes (sheep barn waste, silage liquor, pig and cattle slurry) also have distinctive fluorescence (Baker, 2002) and absorption properties (Brookman, 1997) that might also impact on river fluorescence properties in agricultural catchments.…”

mentioning

confidence: 99%

Spectrophotometric discrimination of river dissolved organic matter

Baker

2002

Hydrological Processes

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Abstract:There is a need to be able to differentiate the dissolved organic matter (DOM) fraction in river waters. Research in the 1970s and 1980s has attempted to utilize both absorbance and fluorescence to distinguish between DOM fractions in river waters, but both were limited by the available technology. Total organic carbon content has, therefore, been widely used as a standard method of measuring DOM concentration, although it has little power to differentiate DOM fractions. Recent advances in fluorescence spectrophotometry enable rapid and optically precise analysis of DOM. Here, we show how a combination of both fluorescence and absorbance can be used to discriminate statistically between spatial variations of DOM in tributaries in a small catchment of the Ouseburn, NE England. The results of the discriminant analysis suggest that about 70% of the samples can be correctly classified to its tributary. Discriminant function 1 explains 60Ð8% of the variance in the data and the fulvic-like fluorescence intensity has the largest absolute correlation within this function; discriminant function 2 explains a further 21Ð5% of the variance and the fulvic-like fluorescence emission wavelength has the largest absolute correlation within this function. The discriminant analysis does not correctly classify all tributaries every time, and successfully discriminates between the different tributaries 70% of the time. Occasions when the tributary waters are less well discriminated are due to either episodic pollution events (at two sites) or due to tributaries that have strong seasonal trends in spectrophotometric parameters, which allows the sites to be misclassified. Results suggest that spectrophotometric techniques have considerable potential in the discrimination of DOM in rivers.

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“…BOD gives the qualitative and quantitative concept of the status of organic matter activity in the river system or wastewater (Brookman, 1997 prescribed by the Royal Commission on Sewage Disposal shows that the standard way to report is the reading of BOD on the fifth day. Therefore, the five-day biochemical oxygen demand (BOD 5 ) gives the most effective outcome of dissolve oxygen activity of the polluted river or wastewater under consideration.…”

Section: Introductionmentioning

confidence: 99%