TY  - CONF
ID  - scholars10978
SN  - 17578981
TI  - Adaptive PLS inferential soft sensor for continuous online estimation of NOx emission in industrial water-tube boiler
Y1  - 2019///
A1  - Hasnen, S.H.
A1  - Zabiri, H.
A1  - Prakash, K.K.
A1  - Mat, T.T.
N1  - cited By 3; Conference of 1st Process Systems Engineering and Safety Symposium 2019, ProSES 2019 ; Conference Date: 4 September 2019 Through 4 September 2019; Conference Code:156611
KW  - Boilers; Forecasting; Nitric oxide; Nitrogen oxides
KW  -  Adaptive soft-sensor; Continuous on-line estimations; Industrial boilers; Industrial water; Online prediction; Operating condition; Prediction accuracy; Process dynamics
KW  -  Industrial emissions
N2  - In common industrial application, the use of a linear and static PLS soft sensor for online prediction and monitoring of industrial boiler is often preferred due to its simple and intuitive framework. However, process dynamics and time-variant factors can negatively affect the accuracy and reliability of PLS soft sensor over its long-term application in process industries. In this paper, development of adaptive soft sensor based on dynamic PLS method has been applied to an industrial water-tube boiler for continuous online prediction of Nitric Oxides emission. In the case study, it is found that the adaptive PLS soft sensor which includes lagged measurements of NOx emission in the model input can significantly improve the prediction accuracy and reliability by 72.7 relative to the performance of linear and static PLS soft sensor when tested on online dataset containing gradual and abrupt changes in the process operating conditions. © Published under licence by IOP Publishing Ltd.
PB  - IOP Publishing Ltd
VL  - 702
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078204954&doi=10.1088%2f1757-899X%2f702%2f1%2f012019&partnerID=40&md5=07e76412e29b9a0775c379dcf22bf8ef
AV  - none
ER  -