Review of soft sensor methods for regression applications
Soft sensors for regression applications (SSR) are inferential models that use online
available sensors (eg temperature, pressure, flow rate, etc.) to predict quality variables …
available sensors (eg temperature, pressure, flow rate, etc.) to predict quality variables …
Input selection methods for soft sensor design: A survey
Soft Sensors (SSs) are inferential models used in many industrial fields. They allow for real-
time estimation of hard-to-measure variables as a function of available data obtained from …
time estimation of hard-to-measure variables as a function of available data obtained from …
Input selection methods for data-driven Soft sensors design: Application to an industrial process
Abstract Soft Sensors (SSs) are inferential models which are widely used in industry. They
are generally built through data-driven approaches that exploit industry historical databases …
are generally built through data-driven approaches that exploit industry historical databases …
[HTML][HTML] Variable time delay estimation in continuous industrial processes
Digital sensors and machine learning enable efficiency improvements in production
processes, through process monitoring, anomaly detection, soft sensing, and process …
processes, through process monitoring, anomaly detection, soft sensing, and process …
A soft sensor modeling method with dynamic time-delay estimation and its application in wastewater treatment plant
Time-delay estimation is an important step for soft sensor modeling. In practical industrial
process, the transportation time of materials and the transmission time of signals are …
process, the transportation time of materials and the transmission time of signals are …
基于联合互信息的动态软测量方法
阮宏镁， 田学民， 王平 - 化工学报, 2014 - hgxb.cip.com.cn
针对工业过程中普遍存在的时延特性和动态特性, 提出一种基于联合互信息的动态软测量方法.
以联合互信息最大化作为准则, 从各辅助变量的历史输入数据矩阵中选取一个连续子变量集 …
以联合互信息最大化作为准则, 从各辅助变量的历史输入数据矩阵中选取一个连续子变量集 …
[HTML][HTML] Process PLS: Incorporating substantive knowledge into the predictive modelling of multiblock, multistep, multidimensional and multicollinear process data
Chemical production processes benefit from intelligent data analysis. Previous work showed
how process knowledge can be included in a structural equation modelling framework …
how process knowledge can be included in a structural equation modelling framework …
Feature selection and regularization of interpretable soft sensors using evolutionary multi-objective optimization design procedures
VHA Ribeiro, G Reynoso-Meza - Chemometrics and Intelligent Laboratory …, 2021 - Elsevier
Soft sensors are mathematical models that estimate hard-to-measure variables given easy-
to-measure ones. This field of study has given the industry a valuable tool to enable a better …
to-measure ones. This field of study has given the industry a valuable tool to enable a better …
Improving flood forecasting through feature selection by a genetic algorithm–experiments based on real data from an amazon rainforest river
This paper addresses the problem of feature selection aiming to improve a flood forecasting
model. The proposed model is carried out through a case study that uses 18 different time …
model. The proposed model is carried out through a case study that uses 18 different time …
[HTML][HTML] Prediction of flooding in distillation columns using machine learning
LM Ochoa-Estopier, S Gourvénec, R Cahors… - Digital Chemical …, 2023 - Elsevier
This work presents a new data-driven approach for early detection of anomalies, namely
flooding, in distillation columns. The main contribution of this approach is that it does not rely …
flooding, in distillation columns. The main contribution of this approach is that it does not rely …