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- 1Ammonia Dosing
- 1Ammonia Slip Catalyst
- 1Back-off Solution
- 1Bayesian methods
- 1Chance Constrained Model Predictive Control
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Fall 2014
In today’s vehicle applications, Selective Catalytic Reduction (SCR) ammonia dosing is completed using complex control algorithms that need to be parameterized for the individual catalytic converter technology. The parameterization of these control strategies is not always completed during the...
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Spring 2017
Model Predictive Control (MPC) is widely applied in the process industry nowadays. Chemical processes are corrupted by all kinds of uncertainties, such as measurement noises, disturbances and parameter uncertainties. Without consideration of uncertainties, conventional MPC will cause various...
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EM Algorithm for Electricity Pool Price Prediction and Errors-in-variables Process Identification
DownloadSpring 2016
In this thesis, under the EM algorithm framework, a multiple model approach is developed towards electricity price prediction, and the identification problem for errors-in-variables (EIV) systems is studied. Alberta's electricity price, which shows high volatility and erratic nature, is...
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Spring 2013
In the steam methane reforming process, improvement of the reformed gas outlet temperature control performance can lead to a larger hydrogen production rate, while ensuring safe process operation. In this work, a side fired primary gas reformer is investigated. The three objectives of this work...
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Spring 2015
Heterogeneous slurry pipelines are found in mining, chemical, and solid transportation (such as coal pipelines) industries worldwide. One of the most important factors in the operation and design of these pipelines is bulk velocity. Solids settle when the bulk velocity is below the deposition...
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Spring 2014
Energy and utilities costs often represent one of the largest operating costs at manufacturing plants and they are areas where companies can reduce cost if optimal operating strategy is applied for efficient steam distribution and electricity generation. In addition to the financial incentive,...
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Fall 2013
State inference and identification of discrete-time, non-linear, stochastic state-space models (SSMs) are considered here. A novel sequential Monte Carlo (SMC) based Bayesian method for simultaneous on-line state inference and identification of non-linear SSMs is proposed. Extension of the method...