@article{Borghesan:2019:10.1016/j.compchemeng.2019.05.009,
author = {Borghesan, F and Chioua, M and Thornhill, NF},
doi = {10.1016/j.compchemeng.2019.05.009},
journal = {Computers and Chemical Engineering},
pages = {188--200},
title = {Forecasting of process disturbances using k-nearest neighbours, with an application in process control},
url = {http://dx.doi.org/10.1016/j.compchemeng.2019.05.009},
volume = {128},
year = {2019}
}

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TY  - JOUR
AB  - This paper examines the prediction of disturbances based on their past measurements using k-nearest neighbours. The aim is to provide a prediction of a measured disturbance to a controller, in order to improve the feed-forward action. This prediction method works in an unsupervised way, it is robust against changes of the characteristics of the disturbance, and its functioning is simple and transparent. The method is tested on data from industrial process plants and compared with predictions from an autoregressive model. A qualitative as well as a quantitative method for analysing the predictability of the time series is provided. As an example, the method is implemented in an MPC framework to control a simple benchmark model.
AU  - Borghesan,F
AU  - Chioua,M
AU  - Thornhill,NF
DO  - 10.1016/j.compchemeng.2019.05.009
EP  - 200
PY  - 2019///
SN  - 1873-4375
SP  - 188
TI  - Forecasting of process disturbances using k-nearest neighbours, with an application in process control
T2  - Computers and Chemical Engineering
UR  - http://dx.doi.org/10.1016/j.compchemeng.2019.05.009
UR  - https://doi.org/10.1016/j.compchemeng.2019.05.009
UR  - http://hdl.handle.net/10044/1/70458
VL  - 128
ER  - 

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