Design and Implementation of the Off-Line Robust Model Predictive Control for Solid Oxide Fuel Cells

Chatrattanawet, Narissara; Kheawhom, Soorathep; Chen, Yong-Song; Arpornwichanop, Amornchai

doi:10.3390/pr7120918

Cited by 8 publications

(4 citation statements)

References 46 publications

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“…Kupecki et al 6 proposed a direct internal reforming model verified by experimental data, which allows the calculation of the molar fractions of the outlet components and analyzes their impacts on the electrochemical reaction, heat balance, and mass balance. Chatrattanawet et al 19 designed a robust model predictive control, which can control the fuel and temperature of the SOFC system at the set-point in the presence of parameter uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Efficiency Maximization of a Direct Internal Reforming Solid Oxide Fuel Cell in a Two-Layer Self-Optimizing Control Structure

Shen

et al. 2023

ACS Omega

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Control system configuration is essential for the efficiency performance of a solid oxide fuel cell (SOFC). In this paper, we aim to report a novel two-layer self-optimizing control (SOC) system for the efficiency maximization of a direct internal reforming SOFC, where the efficiency index is defined as the profit of generated electricity penalized by carbon (CO 2 ) emission. Based on the lumped-parameter model of the SOFC, comprehensive evaluations are carried out to identify the optimal controlled variables (CVs), the control of which at constant set-points can optimize the efficiency, in spite of operating condition changes. In the lower SOC layer, we configure single variables as the CVs. The results show that the stack temperature is the active constraint which should be controlled to maintain the cell performance. In addition, the outlet hydrogen composition is identified as the optimal CV. This result differs from several previous proposals, such as methane composition. In the presence of operating condition changes, the set-point of hydrogen composition is further automatically adjusted by the upper SOC layer, where a linear combination of the SOFC measurements is configured as the CV, giving negligible efficiency losses. The cascaded two-layer SOC structure is able to maximize the SOFC efficiency and reduce carbon emission without using online optimization techniques; meanwhile, it allows for smooth and safe operations. The validity of the new scheme is verified through both static and dynamic evaluations.

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Section: Introductionmentioning

confidence: 99%

Efficiency Maximization of a Direct Internal Reforming Solid Oxide Fuel Cell in a Two-Layer Self-Optimizing Control Structure

Shen

et al. 2023

ACS Omega

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“…As a result, MPC methods have been used to numerous industrial processes [7], e.g., chemical reactors [8], distillation columns [9], waste water treatment plants [10], solar power stations [11], cement kilns [12], pasteurization plants [13] and pulp digesters [14]. In addition to that, MPC algorithms are more and more popular in other areas; example applications are: fuel cells [15], active vibration attenuation [16], combustion engines [17], robots [18], synchronous motors [19], mechanical systems [20], freeway traffic congestion control [21] and autonomous driving [22].…”

Section: Introductionmentioning

confidence: 99%

Tuning of Multivariable Model Predictive Control for Industrial Tasks

Nebeluk

Ławryńczuk

2021

Algorithms

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This work is concerned with the tuning of the parameters of Model Predictive Control (MPC) algorithms when used for industrial tasks, i.e., compensation of disturbances that affect the process (process uncontrolled inputs and measurement noises). The discussed simulation optimisation tuning procedure is quite computationally simple since the consecutive parameters are optimised separately, and it requires only a very limited number of simulations. It makes it possible to perform a multicriteria control assessment as a few control quality measures may be taken into account. The effectiveness of the tuning method is demonstrated for a multivariable distillation column. Two cases are considered: a perfect model case and a more practical case in which the model is characterised by some error. It is shown that the discussed tuning approach makes it possible to obtain very good control quality, much better than in the most common case in which all tuning parameters are constant.

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“…These controllers are robust and real‐time implementable, thus making them suitable for industrial applications. Among these controllers, H‐infinity, 29 Weiner‐type, 30 L1‐adaptive, 31,32 sliding mode, 29 and conventional model predictive 33‐35 do not give satisfactory results for the SOFC's fuel utilization factor, and the performance in the nonlinear region needs further investigations. ADR controller 36 is another system‐specific complex approach, which controls the SOFC in its nonlinear region but the fuel utilization factor during control violates the boundary limits, resulting in the reduced life of the fuel cell 20,37 …”

Section: Introductionmentioning

confidence: 99%

Efficient control of a nonlinear solid oxide fuel cell system using an adaptive model predictive controller

Gupta

Pahwa

Verma

2021

Int Trans Electr Energ Syst

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Summary Over the years, the solid oxide fuel cell (SOFC) is growing commercially due to its high fuel flexibility, lesser maintenance requirement, and environmental friendliness feature. However, its control is challenging due to its nonlinear behavior and simultaneous management of its operational constraints. Therefore, in this work, an adaptive model predictive control is designed systematically to handle the SOFC's behavior while optimizing its operational constraints such as fuel input, utilization factor, and change in pressure of hydrogen and oxygen. This model‐oriented control linearizes, augments, and discretizes the SOFC during run time while estimating the states using a time‐varying Kalman filter. Finally, it optimizes the control problem for predicting the internal states of the SOFC using successive linearization. The simulation results show that the proposed controller gives better performance of the SOFC in linear region, and significantly improved performance in nonlinear region in comparison to the conventional model predictive controller. The validation of the work has been done through real‐time simulation using OPAL‐RT's hardware setup. Using such control is beneficial as it enhances the efficiency of the SOFC significantly. Thus, this advanced controller is most suited for these kinds of nonlinear systems due to its superior performance.

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Design and Implementation of the Off-Line Robust Model Predictive Control for Solid Oxide Fuel Cells

Cited by 8 publications

References 46 publications

Efficiency Maximization of a Direct Internal Reforming Solid Oxide Fuel Cell in a Two-Layer Self-Optimizing Control Structure

Efficiency Maximization of a Direct Internal Reforming Solid Oxide Fuel Cell in a Two-Layer Self-Optimizing Control Structure

Tuning of Multivariable Model Predictive Control for Industrial Tasks

Efficient control of a nonlinear solid oxide fuel cell system using an adaptive model predictive controller

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