Identification of Wiener models for dynamic and steady‐state performance with application to solid oxide fuel cell

Ławryńczuk, Maciej

doi:10.1002/asjc.2038

Cited by 21 publications

(9 citation statements)

References 29 publications

(58 reference statements)

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“…16 Unlike black-box modeling, the block-oriented modeling approach is more transparent due to its straightforward physical interpretation, based on a combination of block gains. 17 In addition, the identification method for the block-oriented models is straightforward, requires low computational effort, and is able to incorporate priori process knowledge. 18 The block-oriented model class comprises a wide range of model configurations, which involved a linear dynamic and nonlinear static element.…”

Section: Introductionmentioning

confidence: 99%

Low‐density polyethylene tubular reactor control using neural Wiener model predictive control

Muhammad

Rohman

Ahmad

et al. 2021

Asia-Pacific J Chem Eng

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Low-density polyethylene (LDPE) is a valued commodity plastic with versatile applications. However, controlling the LDPE polymerization reactor is a challenging task due to its nonlinear behavior and multivariable process and operates in a board operating region. This work aims to develop and explore the neural Wiener MPC (NWMPC) application in controlling the LDPE tubular reactor process. The motivation for NWMPC development originates from its advantage in terms of lower development effort, time, resource, and computational costs compared to nonlinear MPC (NMPC) using the first principle model (FPM). In order to develop the LDPE tubular reactor model, the Aspen Plus and Aspen Dynamic software are used to simulate the process in steady-state and dynamic form. The neural Wiener (NW) model identification is performed using state space and neural network model identification using Matlab software. The identification result shows the ability of the NW model to identify the nonlinear LDPE tubular reactor process successfully. Furthermore, the NWMPC has outperformed the state space MPC (SSMPC) in controlling grade transition, feed pressure loss, feed impurity disturbance, and heat of polymerization change during the online closed-loop performance tests. These results signify the ability of NWMPC to handle practical LDPE tubular reactor control scenarios.

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Section: Introductionmentioning

confidence: 99%

Low‐density polyethylene tubular reactor control using neural Wiener model predictive control

Muhammad

Rohman

Ahmad

et al. 2021

Asia-Pacific J Chem Eng

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“…Despite great advances in the field of time-delay systems involving constant delays, the parameter estimation of this class of systems is still an essential issue in the context of control theory. Recently, much efforts and progress have been made in this field, and some excellent methods have been proposed in the literature [10][11][12][13][14][15][16]. The situation becomes much more complicated when time-delay is a piecewise constant function.…”

Section: Introductionmentioning

confidence: 99%

Parameter identification of a class of nonlinear multidelay systems with piecewise constant delays

Marzban

2020

Asian Journal of Control

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This paper deals with the parameter estimation of an important class of nonlinear time-delay systems with a piecewise constant delay function. The developed procedure is based on a generalization of the hybrid of block-pulse functions and Taylor polynomials. The operational matrix of integration related to the generalized hybrid functions is constructed. Furthermore, an upper bound with respect to the L 2 -norm for the generalized hybrid functions is acquired, and convergence of the generalized hybrid functions is demonstrated. The merits of the hybrid basis are implemented to convert the problem under examination into an algebraic system. The methodology of least squares is adopted for solving the acquired algebraic system. An illustrative example is included in order to validate the efficiency and accuracy of the designed method. It is shown that the proposed approach provides accurate estimates for the desired parameters. The current approach reduces the computation complexity of the identification process.

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“…Several SOFC models have been developed for process system applications, ranging from empirical/input-output/black-box models (Guida et al , 2015; Huo et al , 2006; Lawrynczuk, 2019; Pohjoranta et al , 2014; Stepancic et al , 2019), zero-dimensional physical models (Bhattacharyya and Rengaswamy, 2009; Wang et al , 2011; Zhu and Kee, 2006) to high-fidelity multiphysics-based models (Bhattacharyya and Rengaswamy, 2009; DeCaluwe et al , 2018; Bove et al , 2005; Kakac et al , 2007; Wang et al , 2011; Wu et al , 2020). A case in point where technological decisions have a direct and strong impact on the complexity of the model is the fuel being used for the system (Faro et al , 2012; Lanzini et al , 2017a; Pugliese et al , 2017).…”

Section: Introductionmentioning

confidence: 99%

Reliability analysis for a multi-stack solid oxide fuel cell system subject to operation condition-dependent degradation

Colombo

Schütz

Хартон

2020

JQME

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PurposeA reliability analysis of a solid oxide fuel cell (SOFC) system is presented for applications with strict constant power supply requirements, such as data centers. The purpose is to demonstrate the effect when moving from a module-level to a system-level in terms of reliability, also considering effects during start-up and degradation.Design/methodology/approachIn-house experimental data on a system-level are used to capture the behavior during start-up and normal operation, including drifts of the operation point due to degradation. The system is assumed to allow replacement of stacks during operation, but a minimum number of stacks in operation is needed to avoid complete shutdown. Experimental data are used in conjunction with a physics-based performance model to construct the failure probability function. A dynamic program then solves the optimization problem in terms of time and replacement requirements to minimize the total negative deviation from a given target reliability.FindingsResults show that multi-stack SOFC systems face challenges which are only revealed on a system- and not on a module-level. The main finding is that the reliability of multi-stack SOFC systems is not sufficient to serve as sole power source for critical applications such as data center.Practical implicationsThe principal methodology may be applicable to other modular systems which include multiple critical components (of the same kind). These systems comprise other electrochemical systems such as further fuel cell types.Originality/valueThe novelty of this work is the combination of mathematical modeling to solve a real-world problem, rather than assuming idealized input which lead to more benign system conditions. Furthermore, the necessity to use a mathematical model, which captures sufficient physics of the SOFC system as well as stochasticity elements of its environment, is of critical importance. Some simplifications are, however, necessary because the use of a detailed model directly in the dynamic program would have led to a combinatorial explosion of the numerical solution space.

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Identification of Wiener models for dynamic and steady‐state performance with application to solid oxide fuel cell

Cited by 21 publications

References 29 publications

Low‐density polyethylene tubular reactor control using neural Wiener model predictive control

Low‐density polyethylene tubular reactor control using neural Wiener model predictive control

Parameter identification of a class of nonlinear multidelay systems with piecewise constant delays

Reliability analysis for a multi-stack solid oxide fuel cell system subject to operation condition-dependent degradation

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