Modeling of a sorption heat storage reactor using nonlinear autoregressive neural networks

Luca Scapino, Herbert Zondag, Jan Diriken, Camilo Rindt, Adriano Sciacovelli

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Uittreksel

Sorption thermal energy storage has the potential to store thermal energy over a long time with a higher energy density and less thermal losses compared to other technologies. In terms of modeling, sorption reactors are commonly described by physics-based models encompassing complex nonlinear phenomena occurring in the reactor. From a system modeling perspective, the use of data-driven models can be beneficial in cases where experimental data or high-fidelity data from more complex models are available, and a low computational cost with an acceptable accuracy is desired. The aim of this work is to investigate the capabilities of data-driven models based on two neural networks for modeling an open sorption reactor. The model takes as inputs the inlet temperature and sorbate concentration, and gives as outputs the reactor state of charge (SOC) and outlet temperature (TOUT). To account also for the thermal inertia of heat taking place in the reactor, both outputs are estimated with nonlinear autoregressive neural networks with exogenous inputs (NARXn), which account for the past n model outputs to determine the next output. Three neural network models are analyzed and several test cases are investigated to compare the performance of these neural network models with a high-fidelity CFD model. The results show that, for the SOC estimation, the NARX10 mean squared error (MSE) with respect to the high-fidelity CFD model was approximately two orders of magnitude smaller compared to the NARX1 MSE, resulting in a higher prediction accuracy. On the other hand, using the NARX10 architecture also for the TOUT estimation decreased the accuracy of TOUT estimations compared to a simpler FFNN neural network architecture considered in this work.
TaalEngels
TitelEurotherm Seminar #112
SubtitelAdvances in Thermal Energy Storage
Plaats van productieLleida
Pagina's849-859
Aantal pagina's11
ISBN van elektronische versie978-84-9144-155-7
StatusGepubliceerd - 17 mei 2019
EvenementEurotherm Seminar #112: Advances in Thermal Energy Storage - University of Lleida, Lleida, Spanje
Duur: 15 mei 201917 mei 2019
http://eurotherm.udl.cat/#Home

Congres

CongresEurotherm Seminar #112
LandSpanje
StadLleida
Periode15/05/1917/05/19
Internet adres

Vingerafdruk

Heat storage
Sorption
Neural networks
Thermal energy
Computational fluid dynamics
Network architecture
Energy storage
Physics

Trefwoorden

    Citeer dit

    Scapino, L., Zondag, H., Diriken, J., Rindt, C., & Sciacovelli, A. (2019). Modeling of a sorption heat storage reactor using nonlinear autoregressive neural networks. In Eurotherm Seminar #112: Advances in Thermal Energy Storage (blz. 849-859). Lleida.
    Scapino, Luca ; Zondag, Herbert ; Diriken, Jan ; Rindt, Camilo ; Sciacovelli, Adriano. / Modeling of a sorption heat storage reactor using nonlinear autoregressive neural networks. Eurotherm Seminar #112: Advances in Thermal Energy Storage. Lleida, 2019. blz. 849-859
    @inproceedings{fc7de264d69a4ff19bb3c8af880b1aa6,
    title = "Modeling of a sorption heat storage reactor using nonlinear autoregressive neural networks",
    abstract = "Sorption thermal energy storage has the potential to store thermal energy over a long time with a higher energy density and less thermal losses compared to other technologies. In terms of modeling, sorption reactors are commonly described by physics-based models encompassing complex nonlinear phenomena occurring in the reactor. From a system modeling perspective, the use of data-driven models can be beneficial in cases where experimental data or high-fidelity data from more complex models are available, and a low computational cost with an acceptable accuracy is desired. The aim of this work is to investigate the capabilities of data-driven models based on two neural networks for modeling an open sorption reactor. The model takes as inputs the inlet temperature and sorbate concentration, and gives as outputs the reactor state of charge (SOC) and outlet temperature (TOUT). To account also for the thermal inertia of heat taking place in the reactor, both outputs are estimated with nonlinear autoregressive neural networks with exogenous inputs (NARXn), which account for the past n model outputs to determine the next output. Three neural network models are analyzed and several test cases are investigated to compare the performance of these neural network models with a high-fidelity CFD model. The results show that, for the SOC estimation, the NARX10 mean squared error (MSE) with respect to the high-fidelity CFD model was approximately two orders of magnitude smaller compared to the NARX1 MSE, resulting in a higher prediction accuracy. On the other hand, using the NARX10 architecture also for the TOUT estimation decreased the accuracy of TOUT estimations compared to a simpler FFNN neural network architecture considered in this work.",
    keywords = "Sorption heat storage; Artificial Neural Networks; Energy Efficiency",
    author = "Luca Scapino and Herbert Zondag and Jan Diriken and Camilo Rindt and Adriano Sciacovelli",
    year = "2019",
    month = "5",
    day = "17",
    language = "English",
    isbn = "978-84-9144-155-7",
    pages = "849--859",
    booktitle = "Eurotherm Seminar #112",

    }

    Scapino, L, Zondag, H, Diriken, J, Rindt, C & Sciacovelli, A 2019, Modeling of a sorption heat storage reactor using nonlinear autoregressive neural networks. in Eurotherm Seminar #112: Advances in Thermal Energy Storage. Lleida, blz. 849-859, Lleida, Spanje, 15/05/19.

    Modeling of a sorption heat storage reactor using nonlinear autoregressive neural networks. / Scapino, Luca; Zondag, Herbert; Diriken, Jan; Rindt, Camilo; Sciacovelli, Adriano.

    Eurotherm Seminar #112: Advances in Thermal Energy Storage. Lleida, 2019. blz. 849-859.

    Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

    TY - GEN

    T1 - Modeling of a sorption heat storage reactor using nonlinear autoregressive neural networks

    AU - Scapino,Luca

    AU - Zondag,Herbert

    AU - Diriken,Jan

    AU - Rindt,Camilo

    AU - Sciacovelli,Adriano

    PY - 2019/5/17

    Y1 - 2019/5/17

    N2 - Sorption thermal energy storage has the potential to store thermal energy over a long time with a higher energy density and less thermal losses compared to other technologies. In terms of modeling, sorption reactors are commonly described by physics-based models encompassing complex nonlinear phenomena occurring in the reactor. From a system modeling perspective, the use of data-driven models can be beneficial in cases where experimental data or high-fidelity data from more complex models are available, and a low computational cost with an acceptable accuracy is desired. The aim of this work is to investigate the capabilities of data-driven models based on two neural networks for modeling an open sorption reactor. The model takes as inputs the inlet temperature and sorbate concentration, and gives as outputs the reactor state of charge (SOC) and outlet temperature (TOUT). To account also for the thermal inertia of heat taking place in the reactor, both outputs are estimated with nonlinear autoregressive neural networks with exogenous inputs (NARXn), which account for the past n model outputs to determine the next output. Three neural network models are analyzed and several test cases are investigated to compare the performance of these neural network models with a high-fidelity CFD model. The results show that, for the SOC estimation, the NARX10 mean squared error (MSE) with respect to the high-fidelity CFD model was approximately two orders of magnitude smaller compared to the NARX1 MSE, resulting in a higher prediction accuracy. On the other hand, using the NARX10 architecture also for the TOUT estimation decreased the accuracy of TOUT estimations compared to a simpler FFNN neural network architecture considered in this work.

    AB - Sorption thermal energy storage has the potential to store thermal energy over a long time with a higher energy density and less thermal losses compared to other technologies. In terms of modeling, sorption reactors are commonly described by physics-based models encompassing complex nonlinear phenomena occurring in the reactor. From a system modeling perspective, the use of data-driven models can be beneficial in cases where experimental data or high-fidelity data from more complex models are available, and a low computational cost with an acceptable accuracy is desired. The aim of this work is to investigate the capabilities of data-driven models based on two neural networks for modeling an open sorption reactor. The model takes as inputs the inlet temperature and sorbate concentration, and gives as outputs the reactor state of charge (SOC) and outlet temperature (TOUT). To account also for the thermal inertia of heat taking place in the reactor, both outputs are estimated with nonlinear autoregressive neural networks with exogenous inputs (NARXn), which account for the past n model outputs to determine the next output. Three neural network models are analyzed and several test cases are investigated to compare the performance of these neural network models with a high-fidelity CFD model. The results show that, for the SOC estimation, the NARX10 mean squared error (MSE) with respect to the high-fidelity CFD model was approximately two orders of magnitude smaller compared to the NARX1 MSE, resulting in a higher prediction accuracy. On the other hand, using the NARX10 architecture also for the TOUT estimation decreased the accuracy of TOUT estimations compared to a simpler FFNN neural network architecture considered in this work.

    KW - Sorption heat storage; Artificial Neural Networks; Energy Efficiency

    M3 - Conference contribution

    SN - 978-84-9144-155-7

    SP - 849

    EP - 859

    BT - Eurotherm Seminar #112

    CY - Lleida

    ER -

    Scapino L, Zondag H, Diriken J, Rindt C, Sciacovelli A. Modeling of a sorption heat storage reactor using nonlinear autoregressive neural networks. In Eurotherm Seminar #112: Advances in Thermal Energy Storage. Lleida. 2019. blz. 849-859.