Thermal management of an independent steam reformer for a solid oxide fuel cell with constrained generalized predictive control

Jiang, Jianhua; Li, Xi; Deng, Zongquan; Yang, Jie; Zhang, Yisheng; Li, Jian

doi:10.1016/j.ijhydene.2012.05.119

Cited by 38 publications

(10 citation statements)

References 25 publications

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“…All things considered, the full-order discrete-time model is defined by (20), (21), (22), (23), (24), (25), (26), (27), and (28). The order of model dynamics is defined by the integer numbers: H2 = 4, O2 = 3, and…”

Section: Model Discretisationmentioning

confidence: 99%

“…Firstly, the firstprinciple model based on technological laws may be used [13,16]. Secondly, empirical (black-box) models are possible, for example, neural networks [14,19] or fuzzy systems [20,21]. Although it may be easier to use empirical models than rigorous first-principle ones, it is necessary to point out three important disadvantages of black-box structures.…”

Section: Introductionmentioning

confidence: 99%

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Towards Reduced‐Order Models of Solid Oxide Fuel Cell

Ławryńczuk

2018

Complexity

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The objective of this work is to find precise reduced-order discrete-time models of a solid oxide fuel cell, which is a multiple-input multiple-output dynamic process. At first, the full-order discrete-time model is found from the continuous-time first-principle description. Next, the discrete-time submodels of hydrogen, oxygen, and water pressures (intermediate variables) are reduced. Two model reduction methods based on observability and controllability Grammians are compared: the state truncation method and reduction by residualisation. In all comparisons, the second method gives better results in terms of dynamic and steady-state errors as well as Nyquist plots. Next, the influence of the order of the pressure models on the errors of the process outputs (the voltage and the pressure difference) is studied. It is found that the number of pressure model parameters may be reduced from 25 to 19 without any deterioration of model accuracy. Two suboptimal reduced models are also discussed with only 14 and 11 pressure parameters, which give dynamic trajectories and steady-state characteristics that are very similar to those obtained from the full-order structure.

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Section: Model Discretisationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Reduced‐Order Models of Solid Oxide Fuel Cell

Ławryńczuk

2018

Complexity

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“…As a heuristic and intelligent optimization algorithm, TSA has its privilege in searching for the optimal solution to a nonlinear programming problem. But the heavy calculation burden is the main obstruction for the practical application of NMPC, especially for the nonlinear systems with fast 14 dynamic characteristics like the control system of a HGU. For the sake of designing a MPC strategy in which the control input is updated frequently for a strong output tracking capability, we make a trade-off to the optimality of the solution to the optimization problem and turn to obtain a feasible approach instead.…”

Section: Tsa Based Rolling Optimization In Mpcmentioning

confidence: 99%

“…With the development trend of power system control now concentrating on using nonlinear control theories to solve large systems described by higher dimensional nonlinear models with multiple state variables, NMPC theory which is regarded as an advanced control technique and is able to deal with complex control problems involved with multivariable process interactions, non-minimum phase behavior and systems represented by a nonlinear model [13,14] has been applied in turbine or generator control for power system in recent years [4,15]. It is a powerful control strategy that uses the model of the system to pre-calculate the behavior of the system for a finite prediction 3 horizon with an open-loop rolling optimization.…”

Section: Introductionmentioning

confidence: 99%

Design of a multi-mode intelligent model predictive control strategy for hydroelectric generating unit

et al. 2016

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“…[6] for the water management subsystem and a generalized predictive control is studied in Ref. [7] for the thermal management subsystem of the fuel cells. Besides, other nonlinear control strategies like the sliding-model control, the genetic algorithm, etc.…”

Section: Introductionmentioning

confidence: 99%