Spartan Controls

Edmonton, Canada

Spartan Controls

Edmonton, Canada
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Jiang H.,Spartan Controls | Shah S.L.,University of Alberta | Huang B.,University of Alberta | Wilson B.,Suncor Energy | And 2 more authors.
Control Engineering Practice | Year: 2012

This paper presents two case studies on the performance evaluation and model validation of two industrial multivariate model predictive control (MPC) based controllers: (1) a 7-output, 3-input MPC with three measured disturbance variables for controlling a part of kerosene hydrotreating unit (KHU) and (2) a 8-output, 4-input MPC with five measured disturbances for controlling a part of naphtha hydrotreating unit (NHU). The first case study focuses on potential limits to control performance due to constraints and limits set at the time of controller commissioning. The root causes of sub-optimal performance of KHU are successfully isolated. Data from the NHU unit with MPC 'on' and with MPC 'off' are analyzed to obtain and compare several different measures of multivariate controller performance. Model quality assessment for the two MPCs are performed. A new model index is proposed to have a measure of simulation ability and prediction ability of a model. Closed-loop identification of KHU and closed-loop identification of NHU are conducted using the asymptotic method (ASYM) proposed by Zhu (1998). © 2011 Elsevier Ltd.

Tsai Y.,University of British Columbia | Gopaluni R.B.,University of British Columbia | Marshman D.,Spartan Controls | Chmelyk T.,Spartan Controls
IFAC-PapersOnLine | Year: 2015

For Model Predictive Controlled (MPC) applications, the quality of the plant model determines the quality of performance of the controller. Model Plant Mismatch (MPM), the discrepancies between the plant model and actual plant transfer matrix, can both improve or degrade performance, depending on the context in which performance is measured. In this paper, we do not use performance metrics or "yes-no"-type tests to merely diagnose the presence or absence of MPM in the plant matrix. Rather, we achieve the further goal of locating the exact MPM-affected elements within the plant matrix. Our proposed detection algorithm consists of two system identification experiments: the first experiment diagnoses the presence of MPM, and the second experiment pinpoints the exact MPM-affected elements. We then exercise the algorithm on artificial 3x3 and 5x5 plants suffering from sparse MPM, and demonstrate algorithm's capability of correctly locating the MPM-affected entries. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Mcclure K.S.,University of British Columbia | Nagamune R.,University of British Columbia | Marshman D.,Spartan Controls | Chmelyk T.,Spartan Controls | Gopaluni B.,University of British Columbia
Canadian Journal of Chemical Engineering | Year: 2014

This paper presents a new method for controlling the height of a mineral processing dry-surge ore pile between specified upper and lower levels by manipulating the outlet ore flow. The upper level should not be exceeded for safety, while the height should always be higher than the lowest allowable level to maintain a reserve for continuous operation downstream of the ore pile. However, this is a non-trivial control problem because the disturbance of the unpredictable discontinuous ore input must be attenuated in the continuous outlet ore flow. In addition, ore is mined from multiple sites and causes the physical properties of the ore to be uncertain. To deal with these issues, a control problem is formulated to attenuate the disturbance from the discontinuous feed and guarantee stability given the inaccurate process model. To solve the formulated control problem, a robust H∞ height controller is developed using the linear matrix inequality technique. It is shown that the robust H∞ controller attenuates four times more variability in outlet ore flow than a gain-scheduled PI controller and guarantees robust closed loop stability. © 2013 Canadian Society for Chemical Engineering.

Siddha R.,Spartan Controls | Gilbert A.F.,Lakehead University | Natarajan K.,Lakehead University
Canadian Journal of Chemical Engineering | Year: 2012

The tuning of lead-lag compensators to be used as feedforward controllers for measured disturbances is performed in the frequency domain. The identification of process G u and disturbance dynamics G d uses extended recursive least squares, and the frequency responses are calculated from the least squares coefficients. A lead-lag compensator G ll is designed which minimises the function G ll(jω) + (G d(jω))/(G u(jω)) over a finite number of frequencies, using the Nelder-Mead simplex method. The effectiveness of the frequency domain tuning strategy is compared by simulation to established tuning rules for first-order plus delay processes. The tuning method is experimentally verified on a pilot scale methanol-water distillation column. © 2011 Canadian Society for Chemical Engineering © 2011 Canadian Society for Chemical Engineering.

McClure K.S.,University of British Columbia | Gopaluni R.B.,University of British Columbia | Chmelyk T.,Spartan Controls | Marshman D.,Spartan Controls | Shah S.L.,University of Alberta
Industrial and Engineering Chemistry Research | Year: 2014

The chemical and mineral processing industries need a nonlinear process monitoring method to improve the stability and economy of their processes. Techniques that are currently available to these industries are often too computationally intensive for an industrial control system, or they are too complex to commission. In this paper, we propose using supervised locally linear embedding for projection (SLLEP) as a new nonlinear process monitoring technique to solve these issues. In addition, we suggest using a commonly available tool in modern industrial control systems, a model predictive control, to solve the quadratic program of SLLEP in real-time and with minimal effort to commission. As a case study, we demonstrate that process monitoring with SLLEP can detect and diagnose the early onset of a semiautogenous grinding (SAG) mill overload. A SAG mill overload is a highly nonlinear operating situation, and we show that principal component analysis, the best-in-class technique currently used by the industry for monitoring an overload, is unable to detect the early onset of an overload. © 2013 American Chemical Society.

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