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Skip to Search Results- 6Soft sensor
- 4Ensemble Kalman filter
- 2Inequality constraints
- 2Particle filters
- 1Adaptive
- 1Bayesian inference
- 1Balakrishnapillai Chitralekha, Saneej
- 1Khosbayar, Anudari
- 1Li, Ruoxia
- 1Ma, Ming
- 1Raghu, Abhinandhan
- 1Salehi, Yousef
- 6Huang, Biao (Chemical and Materials Engineering)
- 2Huang, Biao (Department of Chemical and Materials Engineering)
- 1Li, Zukui (Chemical and Materials Engineering)
- 1Prasad, Vinay (Chemical and Materials Engineering)
- 1Prasad, Vinay (Department of Chemical and Materials Engineering)
- 1Prasad, Vinay(Chemical and Materials Engineering)
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Spring 2011
Balakrishnapillai Chitralekha, Saneej
The development of fast and efficient computer hardware technology has resulted in the rapid development of numerous computational software tools for making statistical inferences. The computational algorithms, which are the backbone of these tools, originate from distinct areas in science,...
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Data Quality Assessment for Closed-Loop System Identification and Forecasting with Application to Soft Sensors
DownloadFall 2012
In many chemical plants, data historians store thousands of variables at fast sampling rates. Much of this collected data is routine operating data that could easily be used for system identification and forecasting, especially in the design of soft sensors. Currently, there is no framework for...
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Spring 2013
Parameter estimation of a dynamic system is an important task in process systems engineering. The utilization of an augmented system offers the approach of estimating process states and parameters simultaneously. In practice, the parameters often satisfy certain constraints which should be...
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Spring 2015
In many industrial processes, critical variables cannot be easily measured on-line: they are either obtained from hardware analyzers which are often expensive and difficult to maintain, or carried out off-line through laboratory analysis which cannot be used in real time control. These...
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RESERVOIR HISTORY MATCHING USING CONSTRAINED ENSEMBLE KALMAN FILTER AND PARTICLE FILTER METHODS
DownloadSpring 2015
The high heterogeneity of petroleum reservoirs, represented by their spatially varying rock properties (porosity and permeability), greatly dictates the quantity of recoverable oil. In this work, the estimation of these rock properties, which is crucial for the future performance prediction of a...
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Spring 2016
In data-driven modelling, model accuracy relies heavily on the data set collected from target process. However, various types of measurement noise exist extensively in industrial processes and the data obtained are usually contaminated. If the influence of measurement noise is neglected, both the...
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Fall 2018
The objective of this work is to study the problems that arise in state estimation for severely nonlinear systems. In practice, many processes are nonlinear, accompanied by uncertain parameters. The complexity of the model causes the probability density function (PDF) of the states to deviate...
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Spring 2021
For efficient process control and monitoring, accurate real-time information of quality variables is essential. To predict these quality (or slow-rate) variables at a fast-rate, in the industry, inferential/soft sensors are often used. However, most of the conventional methods for soft sensors do...
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Spring 2024
In the process industry, certain quality variables cannot be measured regularly due to technical limitations or economic constraints. Consequently, the industry relies on laboratory analysis to measure such quality variables. However, laboratory analysis introduces long time-delays in obtaining...