Application of model predictive control to advanced combustion control

Havlena, Vladimı́r; Findejs, Jiří

doi:10.1016/j.conengprac.2004.01.005

Cited by 73 publications

(27 citation statements)

References 16 publications

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“…A typical MPC configuration for a boiler is derived from the work by [6], [7] and [10], and is presented in Table I. A primary objective of the MPC controller is to guarantee safe operation, that is guaranteeing that explosions and meltdowns are prevented.…”

Section: A Mpc Controller Designmentioning

confidence: 99%

“…The boiler dynamics have been extensively studied in for instance [13] and [1]. This knowledge has enabled [6], [7] and [10] to study the control of a steam delivery network using advanced control algorithms, 1 Norwegian University of Science and Technology, Trondheim, Norway torstein.t.k@gmail.com 2 Norwegian University of Science and Technology, Trondheim, Norway lars.imsland@itk.ntnu.no 3 Statoil ASA, Kårstø, Norway, {totk,dsna,mariusg}@statoil.com including using model predictive control. However, to our knowledge, economically optimal production of high pressure steam has received less attention in literature.…”

Section: Introductionmentioning

confidence: 99%

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Optimal and robust production of high pressure steam

Kristoffersen

Snarheim

Imsland

et al. 2014

2014 European Control Conference (ECC)

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Abstract-Steam delivery networks are large energyconsuming processes and an important part in many processing plants. Often, there is a potential for improved operation and reduction in energy consumption by application of advanced control algorithms and real time optimization. In this article, such a solution is investigated by implementing a model predictive controller to control the common header pressure of the steam delivery network. Furthermore, the energy consumption is reduced by incorporating a real time optimizer on top of the model predictive controller providing optimal operating points for the boilers. The potential reduction in energy consumption is examined using data for a real plant, and the application of the model predictive controller with the overlying real time optimization algorithm is examined using a rigorous process simulator.

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Section: A Mpc Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal and robust production of high pressure steam

Kristoffersen

Snarheim

Imsland

et al. 2014

2014 European Control Conference (ECC)

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“…In [12], model predictive control technology for boiler control was presented to enable the coordination of air and fuel flows during transients. It was shown that this approach can be used to increase the boiler efficiency, and also considerably reduce the production of nitrogen oxides (NO x ).…”

Section: Introductionmentioning

confidence: 99%

Data-Reconciliation Based Fault-Tolerant Model Predictive Control for a Biomass Boiler

et al. 2017

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This paper presents a novel, effective method to handle critical sensor faults affecting a control system devised to operate a biomass boiler. In particular, the proposed method consists of integrating a data reconciliation algorithm in a model predictive control loop, so as to annihilate the effects of faults occurring in the sensor of the flue gas oxygen concentration, by feeding the controller with the reconciled measurements. Indeed, the oxygen content in flue gas is a key variable in control of biomass boilers due its close connections with both combustion efficiency and polluting emissions. The main benefit of including the data reconciliation algorithm in the loop, as a fault tolerant component, with respect to applying standard fault tolerant methods, is that controller reconfiguration is not required anymore, since the original controller operates on the restored, reliable data. The integrated data reconciliation-model predictive control (MPC) strategy has been validated by running simulations on a specific type of biomass boiler-the KPA Unicon BioGrate boiler.

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“…Model-based predictive control has been used by Havlena and Findejs [3] to enable tight dynamical coordination between air and fuel intake to take into account variations in power levels. The results showed that this approach can be used to increase boiler efficiency while considerably reducing NOx emissions.…”

Section: Introductionmentioning

confidence: 99%

Fuel-quality soft sensor using the dynamic superheater model for control strategy improvement of the BioPower 5 CHP plant

Kortela

Jämsä‐Jounela

2012

International Journal of Electrical Power & Energy Systems

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Fuel-quality soft sensor using the dynamic superheater model for control strategy improvement of the Biopower 5 CHP plant AbstractThis paper presents an enhanced method for estimating fuel quality in a BioGrate combustion process and the method's use in control strategy improvement. This method is based on a dynamic model that makes use of combustion power estimates -which can be calculated based on the furnaces oxygen consumption -and that makes use of a nonlinear dynamic model of the secondary superheater. The paper focuses to estimate the most essential combustion parameters: fuel moisture and fuel flow. The time delay for detecting a change in fuel moisture and fuel flow is small enough for the method to be used for controlling both air and fuel feed, preventing any steam and pressure oscillations. The proposed control strategy is compared with the method currently used in the BioPower 5 CHP plant. Finally, the results are analyzed and discussed.

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Application of model predictive control to advanced combustion control

Cited by 73 publications

References 16 publications

Optimal and robust production of high pressure steam

Optimal and robust production of high pressure steam

Data-Reconciliation Based Fault-Tolerant Model Predictive Control for a Biomass Boiler

Fuel-quality soft sensor using the dynamic superheater model for control strategy improvement of the BioPower 5 CHP plant

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