Artificial intelligence technique based performance estimation of solid oxide fuel cells

Subramanian, Yathavan; Veena, R.; Ali, Shamshad; Kumar, Avinash; Gubediran, Ramesh Kumar; Dhanasekaran, A.; Gurusamy, Dhanasekaran; Muniandi, Kalpana

doi:10.1016/j.matpr.2021.06.412

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…The key SOFC parameters, such as the temperature and the supply voltage, were input into the computational model and the values of current density and the MPD values were the output parameters, corresponding to 1160 mA cm −2 and 225 mW cm −2 at 800 • C, respectively. The prepared NiO-SDC|YSZ|LSCF single cell with hydrogen as fuel showed the values of peak current and power density equal to 1170 mA cm −2 and 227 mW cm −2 at 800 • C, respectively, which showed the closeness of the theoretically predicted and research data [461].…”

Section: Modeling Of the Electrode Performancesupporting

confidence: 68%

“…The prediction of the performance characteristics for the YSZ electrolyte-supported single cell with NiO-SDC as the composite anode and LSCF as the cathode was provided in [461] using a Support Vector Machine (SVM) machine learning technique. The key SOFC parameters, such as the temperature and the supply voltage, were input into the computational model and the values of current density and the MPD values were the output parameters, corresponding to 1160 mA cm −2 and 225 mW cm −2 at 800 • C, respectively.…”

Section: Modeling Of the Electrode Performancementioning

confidence: 99%

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Overview of Approaches to Increase the Electrochemical Activity of Conventional Perovskite Air Electrodes

Филонова

Pikalova

2023

Materials

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The progressive research trends in the development of low-cost, commercially competitive solid oxide fuel cells with reduced operating temperatures are closely linked to the search for new functional materials as well as technologies to improve the properties of established materials traditionally used in high-temperature devices. Significant efforts are being made to improve air electrodes, which significantly contribute to the degradation of cell performance due to low oxygen reduction reaction kinetics at reduced temperatures. The present review summarizes the basic information on the methods to improve the electrochemical performance of conventional air electrodes with perovskite structure, such as lanthanum strontium manganite (LSM) and lanthanum strontium cobaltite ferrite (LSCF), to make them suitable for application in second generation electrochemical cells operating at medium and low temperatures. In addition, the information presented in this review may serve as a background for further implementation of developed electrode modification technologies involving novel, recently investigated electrode materials.

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Section: Modeling Of the Electrode Performancesupporting

confidence: 68%

Section: Modeling Of the Electrode Performancementioning

confidence: 99%

Overview of Approaches to Increase the Electrochemical Activity of Conventional Perovskite Air Electrodes

Филонова

Pikalova

2023

Materials

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“…Machine learning methods have been used in many engineering aspects including fuel cell stack and system prognostics. [ 10d,14 ] The potential advantage of using machine learning with multiple cells lies in the ability to generalize and capture broader trends and variations across different cells and deterioration mechanisms. When considering a single cell, machine learning can still yield valuable insights into degradation behavior and enable accurate prognostics.…”

Section: Introductionmentioning

confidence: 99%

Root Cause Analysis of Degradation in Protonic Ceramic Electrochemical Cell with Interfacial Electrical Sensors Using Data‐Driven Machine Learning

Wu,

Wang,

Bian

et al. 2023

Advanced Science

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Protonic ceramic electrochemical cells (PCECs) offer promising paths for energy storage and conversion. Despite considerable achievements made, PCECs still face challenges such as physiochemical compatibility between componenets and suboptimal solid–solid contact at the interfaces between the electrolytes and electrodes. In this study, a novel approach is proposed that combines in situ electrochemical characterization of interfacial electrical sensor embedded PCECs and machine learning to quantify the contributions of different cell components to total degradation, as well as to predict the remaining useful life. The experimental results suggest that the overpotential induced by the oxygen electrode is 48% less than that of oxygen electrode/electrolyte interfacial contact for up to 1171 h. The data‐driven machine learning simulation predicts the RUL of up to 2132 h. The root cause of degradation is overpotential increase induced by oxygen electrode, which accounts for 82.9% of total cell degradation. The success of the failure diagnostic model is demonstrated by its consistency with degradation modes that do not manifest in electrolysis fade during early real operations. This synergistic approach provides valuable insights into practical failure diagnosis of PCECs and has the potential to revolutionize their development by enabling improved performance prediction and material selection for enhanced durability and efficiency.

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Valorization of Agriculture Residues into Value‐Added Products: A Comprehensive Review of Recent Studies

Hoang,

Van Anh,

Yusuf

et al. 2024

The Chemical Record

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Global agricultural by‐products usually go to waste, especially in developing countries where agricultural products are usually exported as raw products. Such waste streams, once converted to “value‐added” products could be an additional source of revenue while simultaneously having positive impacts on the socio‐economic well‐being of local people. We highlight the utilization of thermochemical techniques to activate and convert agricultural waste streams such as rice and straw husk, coconut fiber, coffee wastes, and okara power wastes commonly found in the world into porous activated carbons and biofuels. Such activated carbons are suitable for various applications in environmental remediation, climate mitigation, energy storage, and conversions such as batteries and supercapacitors, in improving crop productivity and producing useful biofuels.

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Artificial intelligence technique based performance estimation of solid oxide fuel cells

Cited by 6 publications

References 13 publications

Overview of Approaches to Increase the Electrochemical Activity of Conventional Perovskite Air Electrodes

Overview of Approaches to Increase the Electrochemical Activity of Conventional Perovskite Air Electrodes

Root Cause Analysis of Degradation in Protonic Ceramic Electrochemical Cell with Interfacial Electrical Sensors Using Data‐Driven Machine Learning

Valorization of Agriculture Residues into Value‐Added Products: A Comprehensive Review of Recent Studies

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