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Sains Malaysiana 52(1)(2023):
http://doi.org/10.17576/jsm-2023-5201-20
Penaksiran Risiko Sel Bahan Api Oksida Pepejal (SOFC) (Risk Assessment of Solid Oxide Fuel Cells (SOFCs)-Using
Bayesian Networks)
MUTHIEAH MULLIYATDI1, DARMAN NORDIN2,*,
NURZAILYN SHAMSUDDIN2, MASLI IRWAN ROSLI2, ZAMIRA HASANAH
ZAMZURI3
1Fakulti
Sains dan Teknologi, Universiti Kebangsaan Malaysia, 43000 UKM Bangi, Selangor
Darul Ehsan, Malaysia
2Fakulti
Kejuruteraan dan Alam Bina, Universiti Kebangsaan Malaysia, 43000 UKM Bangi,
Selangor Darul Ehsan, Malaysia
3Fakulti
Sains Matematik, Universiti Kebangsaan Malaysia, 43000 UKM Bangi, Selangor
Darul Ehsan, Malaysia
Recieved: 10 June 2022/Accepted:
26 September 2022
Abstrak
Tenaga boleh baharu menjadi
tumpuan dunia sebagai sumber tenaga alternatif bagi penggantian penggunaan
tenaga petroleum. Tenaga boleh baharu yang mendapat perhatian adalah penggunaan
sel bahan api oksida pepejal (SOFC) kerana tenaga ini mempunyai pelepasan
karbon yang minimum di samping kelestarian tenaga yang bersih. Walau
bagaimanapun, penggunaan SOFC dalam penjanaan tenaga elektrik adalah terhad
kerana terdapat risiko dari segi suhu, pelepasan bahan kimia, serta kaedah
penyimpanan tenaga penjanaan yang mampu memberi kesan kemusnahan serta
kemalangan yang membawa kepada gangguan
terhadap pengendalian operasi dan keadaan sekeliling. Oleh itu, penaksiran
risiko memainkan peranan penting dalam memastikan pengoperasian keseluruhan
sistem berada dalam keadaan selamat melalui pendekatan keselamatan berdasarkan
kepada data yang didapati secara langsung atau data kemalangan terdahulu.
Penyelidikan ini mengkaji senario yang membawa kepada kemusnahan melalui kaedah
pemetaan Bayesan menggunakan program perisian sumber terbuka, GeNie dalam
mengenal pasti risiko terhadap operasi. Keadaan operasi pada suhu tinggi
dikenal pasti sebagai punca utama yang menyumbang kepada risiko. Kemerosotan
sebanyak 76% sehingga 90% boleh berlaku sekiranya suhu operasi melebihi suhu
optimum bersama kemerosotan parameter utama operasi.
Kata kunci: Degradasi; kerosakan; penaksiran risiko; sel
bahan api oksida pepejal
Abstract
Renewable energy becomes the
world’s attention as an alternative energy resource to replace petroleum energy
usage. Renewable energy that is getting attention is solid oxide fuels cell
(SOFCs) because they produce minimum carbon emission with clean sustainable
energy. However, SOFCs usage in electricity generation is limited due to the
risks of temperature, chemical substance emission, and generation power storage
method that is able to cause destruction and accidents that will bring
disruption to the handling operation and the surrounding. Therefore, risk
assessment plays an important role to ensure the overall operation system in
safe condition through safety approach based on the direct data or previous
accidents data. This study investigates the consequences scenario due to high
operating temperature through Bayesian mapping method using open resource
software programme, GeNie in
identifying risks towards operation. The operation condition in high
temperature is identified as the main cause that contributes to the risk. The
degeneration of 76% to 90% can happen if the operation temperature exceeds the
optimum temperature with the degradation of the main operation parameter.
Keywords:
Degradation; fault; risk assessment; solid oxide fuel cells
REFERENCES
Abdul Hamid,
H.H., Zakaria, I. & Othman, M.S. 2019. Tenaga boleh diperbaharui bagi penjanaan tenaga elektrik di Malaysia: Satu kajian literatur. Journal on Technical and Vocational
Education (JTVE) 4(3) Special Edition NASCO: 129-140.
Abdul Rosyid, O. & Oktaufik, M.A.M.
(2009). Infrastruktur hidrogen untuk aplikasi fuel cell dalam era ekonomi hidrogen. J. Ilm. Tek. Energi 1(9): 1-14.
Akbarizan, Kurniawan, R., Nazri, M.Z.A.,
Abdullah, S.N.H.S., Murhayati, S. & Nurcahaya. 2018. Using Bayesian Network for determining the
recipient of Zakat in BAZNAS Pekanbaru. 2nd
International Conference on Electrical Engineering and Informatics (ICon EEI).
https://doi.org/10.1109/ICon-EEI.2018.8784142
Aman,
N.A.M.N., Muchtar, A., Rosli,
M.I., Baharuddin, N.A., Somalu,
M.R. & Kalib, N.S. 2020. Influence of thermal
conductivity on the thermal behavior of intermediate-Temperature solid oxide
fuel cells. Journal of Electrochemical Science and Technology 11(2):
132-139. https://doi.org/10.33961/jecst.2019.00276
Amin, M.T.,
Khan, F. & Imtiaz, S. 2019. Fault detection and pathway analysis using a
dynamic Bayesian network. Chemical Engineering Science 195: 777-790.
https://doi.org/10.1016/j.ces.2018.10.024
Arsie, I., Filippi, A. di Marra, D., Pianese, C. &
Sorrentino, M. 2010. Fault tree analysis aimed to design and implement on-field
fault detection and isolation schemes for SOFC systems. ASME 2010 8th
International Conference on Fuel Cell Science, Engineering and Technology
Conference.http://dx.doi.org/10.1115/FuelCell2010-33344
Bahoo Toroody, A., de Carlo, F., Paltrinieri, N., Tucci, M. & van Gelder, P.H.A.J.M. 2020. Bayesian regression based condition monitoring approach for effective reliability prediction of random processes in autonomous energy supply operation. Reliability Engineering & System Safety 201: 106966. https://doi.org/10.1016/j.ress.2020.106966 Behravan, A., Kiamanesh, B. & Obermaisser, R. 2021. Fault diagnosis of DCV and heating systems based on causal relation in fuzzy Bayesian belief networks using relation direction probabilities. Energies 14(20): 6607. https://doi.org/10.3390/en14206607
Castillo,
E., Gutierrez, J.M. & Hadi, A.S. 1997.
Sensitivity analysis in discrete Bayesian networks. IEEE Transactions on
Systems, Man, and Cybernetics - Part A: Systems and Humans, 27(4): 412-423.
https://doi.org/10.1109/3468.594909
Costamagna, P., de Giorgi, A., Moser, G., Serpico, S.B. & Trucco,
A. 2019. Data-driven techniques for fault diagnosis in power generation plants
based on solid oxide fuel cells. Energy Conversion and Management 180(1336): 281-291. https://doi.org/10.1016/j.enconman.2018.10.107
Deshpande,
G., Shrikhande, S., Sawarkar,
A.N. & Patle, D.S. 2022. Multiobjective optimization of ultrasound intensified and ionic liquid catalyzed in situ algal biodiesel production considering economic, environmental and safety
indicators. Chemical Engineering Research and Design 180: 134-152.
https://doi.org/10.1016/j.cherd.2022.02.011
Ericson,
C.A. 2005. Hazard Analysis Techniques for System Safety. New York: John
Wiley & Sons.
Feng, X.,
Jiang, J. & Wang, W. 2020. Gas pipeline failure evaluation method based on
a Noisy-OR gate bayesian network. Journal of Loss
Prevention in the Process Industries 66: 104175.
https://doi.org/10.1016/j.jlp.2020.104175
Fenton, N., Lagnado, D., Dahlman, C. &
Neil, M. 2019. The opportunity prior: A proof-based prior for criminal cases. Law,
Probability and Risk 18(4): 237-253. https://doi.org/10.1093/lpr/mgz007
Ferreira,
O., Viavattene, C., Jiménez, J.A., Bolle, A., das Neves, L., Plomaritis,
T.A., McCall, R. & van Dongeren, A.R. 2018.
Storm-induced risk assessment: Evaluation of two tools at the regional and
hotspot scale. Coastal Engineering 134: 241-253.
https://doi.org/10.1016/j.coastaleng.2017.10.005
Finkenrath, M., Lokurlu, A., Blum, L. & Stolten, D. 2004. Dynamic Simulation Model of a High
Temperature Fuel Cell System. 5th European Congress on Modelling and
Simulation Conference.
Forrai, A., Funato, H., Yanagita, Y. & Kato, Y. 2005. Fuel-cell parameter estimation and diagnostics. IEEE Transactions on Energy Conversion 20(3): 668-675. https://doi.org/10.1109/TEC.2005.845516 Gazzarri, J.I. & Kesler, O. 2007. Non-destructive delamination
detection in solid oxide fuel cells. Journal of Power Sources 167(2):
430-441.
Irshad, M.,
Siraj, K., Raza, R., Ali, A., Tiwari, P., Zhu, B., Rafique, A., Ali, A., Kaleem
Ullah, M., & Usman, A. 2016. A brief description of high temperature solid
oxide fuel cell’s operation, materials, design, fabrication technologies and
performance. Applied Sciences 6(3): 75.
https://doi.org/10.3390/app6030075
Jia, C.,
Han, M. & Chen, M. 2017. Analysis of gas leakage and current loss of solid
oxide fuel cells by screen printing. ECS Transactions 78(1): 1533-1540.
https://doi.org/10.1149/07801.1533ecst
Johnston,
B., Mayo, M.C. & Khare, A. 2005. Hydrogen: the
energy source for the 21st century. Technovation 25(6): 569-585. https://doi.org/10.1016/j.technovation.2003.11.005
Kabir, S.
& Papadopoulos, Y. 201). Applications of Bayesian networks and Petri nets
in safety, reliability, and risk assessments: A review. Safety Science 115: 154-175. https://doi.org/10.1016/j.ssci.2019.02.009
Kalib, N.S., Muchtar, A., Rao Somalu, M., Kamal, A., Mohd Ihsan, A., Ashikin, N., Aman, M.N. & Akidah Baharuddin, N. 2018.
Factors Influencing thermal stress development in solid oxide fuel cells. Journal
of Advanced Research in Fluid Mechanics and Thermal Sciences Journal Homepage 50: 146-152. www.akademiabaru.com/arfmts.html
Lackner,
M.A., Rogers, A.L. & Manwell, J.F. 2008.
Uncertainty analysis in MCP-based wind resource assessment and energy
production estimation. Journal of Solar Energy Engineering 130(3):
031006. https://doi.org/10.1115/1.2931499
Lee, Y.K., Mavris, D.N., Volovoi, V.V.,
Yuan, M. & Fisher, T. 2010. A fault diagnosis method for industrial gas
turbines using Bayesian data analysis. Journal of Engineering for Gas
Turbines and Power 132(4): 041602. https://doi.org/10.1115/1.3204508
Leimeister, M. & Kolios, A. 2018. A
review of reliability-based methods for risk analysis and their application in
the offshore wind industry. Renewable and Sustainable Energy Reviews 91:
1065-1076. https://doi.org/10.1016/j.rser.2018.04.004
Li, S., Cao, H. & Yang, Y. 2018. Data-driven simultaneous fault diagnosis for solid oxide fuel cell system using multi-label pattern identification. Journal of Power Sources 378: 646-659. https://doi.org/10.1016/j.jpowsour.2018.01.015 Mahato, N., Banerjee, A., Gupta, A., Omar, S. & Balani, K. 2015. Progress in material selection for solid oxide fuel cell technology: A review. Progress in Materials Science 72: 141-337. https://doi.org/10.1016/j.pmatsci.2015.01.00 Monica, I.,
Larisa, I., Serban, M. & Alina, R. 2012. Hazard
Identification and Risk Assessment in Sustainable Enterprise. 52(12):
58-61. http://www.ipedr.com/vol52/012-ICEME2012-C00027.pdf
Nanik Indayaningsih, Muljadi & Erfin Yundra Febrianto. 2008. Studi awal penggabungan antar komponen sel bahan bakar berbasis keramik. Jurnal Fisika Himpunan Fisika Indonesia 8(1): 12-23.
Nikolić, B. & Ružić-Dimitrijević,
L. 2009. Risk assessment of information technology systems. Issues in
Informing Science and Information Technology (IISIT) 6:
595-615.
Peng, J.,
Huang, J., Wu, X., Xu, Y., Chen, H. & Li, X. 2021. Solid oxide fuel cell
(SOFC) performance evaluation, fault diagnosis and health control: A review. Journal
of Power Sources 505: 230058.
https://doi.org/10.1016/j.jpowsour.2021.230058
Pijolat, C. 2012. Screen-printing for the fabrication of solid
oxide fuel cells (SOFC). In Printed Films: Materials Science and
Applications in Sensors, Electronics and Photonics, edited by Prudenziati, M. & Hormadaly,
J. Wood Publishing: Elsevier. hlm. 469-495. https://doi.org/10.1533/9780857096210.2.469
Plomaritis, T.A., Costas, S. & Ferreira, Ó. 2018. Use of a
Bayesian Network for coastal hazards, impact and disaster risk reduction
assessment at a coastal barrier (Ria Formosa, Portugal). Coastal Engineering 134: 134-147. https://doi.org/10.1016/j.coastaleng.2017.07.003
Ploner, A., Hagen, A. & Hauch, A.
2018. Carbon deposition diagnostics for reliability and state-of-health
assessment of SOFC. ECS Transactions 85(4): 25-32.
https://doi.org/10.1149/08504.0025ecst
Riascos, L.A.M., Simoes, M.G. &
Miyagi, P.E. 2007. A Bayesian network fault diagnostic system for proton
exchange membrane fuel cells. Journal of Power Sources 165(1): 267-278.
https://doi.org/10.1016/j.jpowsour.2006.12.003
Romessis, C. & Mathioudakis, K. 2006. Bayesian network approach for gas path fault diagnosis. Journal of Engineering for Gas Turbines and Power 128(1): 64-72. https://doi.org/10.1115/1.1924536 Sanuy, M., Jiménez, J.A. & Plant, N. 2020. A Bayesian network methodology for coastal hazard assessments on a regional scale: The BN-craf. Coastal Engineering 157: 103627.
https://doi.org/10.1016/j.coastaleng.2019.103627
Sayed Ali,
S. 2018. Testing and evaluation the new safety job analysis guideline. Master’s
Thesis in Risk Management, University of Stavanger, Norway (Tidak diterbitkan).
Sharif,
A.M., Basri, S. & Ali, H. 2014. Strength and
weakness of software risk assessment tools. International Journal of Software
Engineering & Its Applications 8(3): 389-398.
Sharifzadeh, M. & Rashtchian, D. 2020.
Fault detection, loss prevention, hazard mitigation, and safe operation of
solid oxide fuel cell systems. In Design and Operation of Solid Oxide Fuel
Cells: The Systems Engineering Vision for Industrial Application,
edited by Sharifzadeh, M. hlm.
255-274. Elsevier. https://doi.org/10.1016/B978-0-12-815253-9.00008-2
Sreedhar,
I., Agarwal, B., Goyal, P. & Agarwal, A. 2020. An overview of degradation
in solid oxide fuel cells-potential clean power sources. Journal of Solid
State Electrochemistry 24(6): 1239-1270.
https://doi.org/10.1007/s10008-020-04584-4
Uusitalo, L. 2007. Advantages and challenges of Bayesian networks in environmental modelling. Ecological Modelling 203(3-4): 312-318. https://doi.org/10.1016/j.ecolmodel.2006.11.033 Vijay, P., Tadé, M.O. & Shao, Z. 2019. Adaptive observer based approach for the fault diagnosis in solid oxide fuel cells. Journal of Process Control 84: 101-114. https://doi. org/10.1016/j.jprocont.2019.09.010 Vilela, J., Castro, J., Martins, L.E.G. & Gorschek,
T. 2017. Integration between requirements engineering and safety analysis: A
systematic literature review. Journal of Systems and Software 125:
68-92. https://doi.org/10.1016/j.jss.2016.11.031
Wang, B., Albarracín-Suazo, S., Pagán-Torres,
Y. & Nikolla, E. 2017. Advances in methane
conversion processes. Catalysis Today 285: 147-158.
https://doi.org/10.1016/j.cattod.2017.01.023
Wang, K.,
Marion-Péra, M-C., Hissel,
D., Yousfi-Steiner, N., Pohjoranta,
A. & Pofahl, S. 2012. SOFC modelling based on
discrete Bayesian network for system diagnosis use. IFAC Proceedings Volumes (IFAC-PapersOnline) 8: 675-680.
William, B.
2008. Selection of Hazard Evaluation Techniques.
https://process-improvement-institute.com/_downloads/Selection_of_Hazard_Evaluation_Techniques.pdf
Wu, X., Xu, Y-W., Zhao, D., Li, Z., Zhong, X., Chen, M., Jiang, J., Deng, Z., Fu, X. & Li, X. 2019. Fault detection and assessment for solid oxide fuel cell system gas supply unit based on novel principal component analysis. Journal of Power Sources 436: 226864. https://doi.org/10.1016/j.jpowsour.2019.226864 Wu, X.J. & Ye, Q. 2016. Fault diagnosis and prognostic of solid oxide fuel cells. Journal of Power Sources 321: 47-56. https://doi.org/10.1016/j.jpowsour.2016.04.080 Wu, X. & Gao, D. 2017. Fault tolerance control of SOFC systems based on nonlinear model predictive control. International Journal of Hydrogen Energy 42(4): 2288-2308. https://doi.org/10.1016/j.ijhydene.2016.09.203 Wu, Z.,
Shen, Y., Wang, H. & Wu, M. 2020. Urban flood disaster risk evaluation
based on ontology and Bayesian Network. Journal of Hydrology 583:
124596. https://doi.org/10.1016/j.jhydrol.2020.124596
Xin, P-W.
2017. Risk-based design tools for process facilities. Master’s Thesis. Memorial
University of Newfoundland (Tidak diterbitkan).
Xin, P.,
Khan, F. & Ahmed, S. 2017. Dynamic hazard identification and scenario
mapping using Bayesian network. Process Safety and Environmental Protection 105: 143-155. https://doi.org/10.1016/j.psep.2016.11.003
Yang, B.,
Guo, Z., Wang, J., Wang, J., Zhu, T., Shu, H., Qiu,
G., Chen, J. & Zhang, J. 2021. Solid oxide fuel cell systems fault
diagnosis: Critical summarization, classification, and perspectives. Journal
of Energy Storage 34: 102153. https://doi.org/10.1016/j.est.2020.102153
Yazdi, M. & Kabir, S. 2017. A fuzzy Bayesian network approach for risk analysis in process industries. Process Safety and Environmental Protection 111: 507-519. https://doi.org/10.1016/j.psep.2017.08.015 Zaccaria, V., Tucker, D. & Traverso, A. 2016. A distributed realtime model of degradation in a solid oxide fuel cell, part I: Model characterization. Journal of Power Sources 311: 175-181. https://doi.org/10.1016/j.jpowsour.2016.02.040 Zaccaria, V., Tucker, D. & Traverso, A. 2016. A distributed real-time model of degradation in a solid oxide fuel cell, part II: Analysis of fuel cell performance and potential failures. Journal of Power Sources 327: 736-742. https://doi.org/10.1016/j.jpowsour.2016.01.027 Zhang, Z.,
Li, S., Xiao, Y. & Yang, Y. 2019. Intelligent simultaneous fault diagnosis
for solid oxide fuel cell system based on deep learning. Applied Energy 233-234: 930-942. https://doi.org/10.1016/j.apenergy.2018.10.113
*Corresponding author; email: darman@ukm.edu.my
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