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The aim of this work is to show how partial least squares (PLS) regression when combined with two other techniques Karhunen-Loeve (KL) expansion and Markov chain Monte Carlo (MCMC) can be efficient and effective at addressing parameter uncertainties that affect the predictive ability of a model for critical applications such as monitoring and control. We introduce a combination of PLS regression and...
The objective of this work is to manage water flooding of a reservoir to achieve optimal oil production by employing an optimal model-based control framework that uses uncertain parameter updating and a particular reduced-order model. A Markov chain Monte Carlo method is used to update the proposed distributions of the uncertain parameters. To avoid excessive simulations of the complex reservoir model,...
Spray dryers represent an important unit operation in many industries such as pharmaceuticals and food. However, the approach to the design and control of spray dryers has been based primarily on heuristics. The traditional, serial approach to process design and control ignores the effect that the process design has on the control structure synthesis. Thus, it is not surprising that the achievable...
This paper presents the design of a model predictive controller (MPC) for a set of integrated biological reactors, which is a part of NASA's advanced regenerative wastewater treatment system. A first principles model developed by [1] is used to design the a centralized control system to regulate the performance of the integrated biological reactors for on-supply & on-demand operations simultaneously...
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