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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/m7t4p2keq5h2rgbfjgwqthgp4q" style="color: black;">International Journal of Education and Management Engineering</a>
Physical sensors are used mostly to detect sludge and odour in wastewater. Black box modelling or dataderived model using the correlation of input-output parameters is the preferred method as we have assessed. This is due to the non-complex approach of such models as opposed to model-driven, mechanistic models. The latter is hard to be adopted for soft-sensor development due to the inherent complexities and uncertainties. The commonest methods for soft sensor model development are ANN and<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5815/ijeme.2018.06.01">doi:10.5815/ijeme.2018.06.01</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/d5xb4nxfd5g5dexeuf2mdjc5gq">fatcat:d5xb4nxfd5g5dexeuf2mdjc5gq</a> </span>
more »... Many other improvements of these methods are achieved by combining with other techniques to enhance the prediction performance of the soft sensors. Accuracy and precision of data collected for soft sensor modelling has become a vital concern at present to ensure the reliability of wastewater quality indices predicted by the soft sensors. Reduction of the level of reliability of the sensor system in monitoring and controlling of WWTPs would lead to serious lapses in the wastewater quality management. In this backdrop we recommend SEVA soft sensor as one of the best potential solutions which could be offered by the existing technologies.
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