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- 1Bendrich, Michelle
- 1Ghafoor Mohseni, Padideh
- 1Hassanzadeh, Bardia
- 1Li, Shuning
- 1Mohammadi, Leily
- 1Moon, Dana R
- 2Distributed Model Predictive Control
- 1Ammonia Dosing
- 1Ammonia Slip Catalyst
- 1Back-off Solution
- 1Bayesian methods
Results for "supervisors_tesim:"Forbes, Fraser (Chemical and Materials Engineering)""
Industrial chemical plants are complex, highly integrated systems composed of geographically distributed processing units, linked together by material and energy streams. To ensure efficient operation in such integrated plants, multivariable optimal control methods like MPC are required. Although...
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...
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...
Catalytic reactors have widespread applications in chemical and petrochemical industries. The most well known type of catalytic reactors are fixed-bed or packed-bed reactors where the reaction takes place on the surface of the catalyst. One of the most important phenomena that takes place in a...
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,...
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...
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...
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...
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...
Price-Driven Coordination of Distributed Model Predictive Controllers: A Bi-Level Optimization ApproachDownload
Chemical and petrochemical plants typically integrate a number of geographically distributed operating units, which are physically linked through energy and material streams or inherently coupled via plant-wide constraints. The main drawback of the current decentralized control system is that it...