A state-of-the-art survey of solid oxide fuel cell parameter identification: Modelling, methodology, and perspectives

Steady-state and dynamic models of solid oxide fuel cells based on Satin Bowerbird Optimizer

Parameter identification of solid oxide fuel cell by Chaotic Binary Shark Smell Optimization method

Solid oxide fuel cells (SOFCs) are devices that can generate electrical power and heat by transforming the chemical energy stored in the fuels. Waste carbon from industries is a surplus that requires resources to be properly managed without harming the environment. To utilize the chemical energy stored in the waste carbon and convert it into electrical power via SOFC, gives an ethical advantage to produce green electricity at reduced fuel costs. However, operating under carbonaceous conditions requires special fabrication strategies for the SOFC to ensure prolonged efficient operation. In view of the above, the overarching aim of this review paper is to 1. Analyse state of the art vacuum-based fabrication techniques and compare them with conventional techniques to manufacture different classes of SOFCs used in literature till date, 2. Understand the reaction mechanisms for operating on various fuels at a range of operating temperatures, 3. Focus on the modes of operation for activated and industrial waste carbon fuelled SOFCs (CFCs) and calculate their respective fuel conversion efficiencies, power production ability and longevity 4. Suggest novel electronic and ionic conductors with various configurations that show promise when operated in carbonaceous environments yet remain unexplored by researchers, 5. Consolidate the latest life cycle assessment studies on multi-fuelled kW-MW class SOFCs to gauge their environmental and techno-economic impact assessment. The overall objective of this study is to provide the reader an understanding of the challenges and opportunities that exist in realistic implementation of CFCs in the existing infrastructure.

The role of vacuum based technologies in solid oxide fuel cell development to utilize industrial waste carbon for power production

Numerical investigations to determine the optimal operating conditions for 1 kW-class flat-tubular solid oxide fuel cell stack

Evaluation of steady-state characteristics for solid oxide carbon fuel cell short-stacks

We introduce Korean Language Understanding Evaluation (KLUE) benchmark. KLUE is a collection of 8 Korean natural language understanding (NLU) tasks, including Topic Classification, SemanticTextual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking. We build all of the tasks from scratch from diverse source corpora while respecting copyrights, to ensure accessibility for anyone without any restrictions. With ethical considerations in mind, we carefully design annotation protocols. Along with the benchmark tasks and data, we provide suitable evaluation metrics and fine-tuning recipes for pretrained language models for each task. We furthermore release the pretrained language models (PLM), KLUE-BERT and KLUE-RoBERTa, to help reproducing baseline models on KLUE and thereby facilitate future research. We make a few interesting observations from the preliminary experiments using the proposed KLUE benchmark suite, already demonstrating the usefulness of this new benchmark suite. First, we find KLUE-RoBERTa-large outperforms other baselines, including multilingual PLMs and existing open-source Korean PLMs. Second, we see minimal degradation in performance even when we replace personally identifiable information from the pretraining corpus, suggesting that privacy and NLU capability are not at odds with each other. Lastly, we find that using BPE tokenization in combination with morpheme-level pre-tokenization is effective in tasks involving morpheme-level tagging, detection and generation. In addition to accelerating Korean NLP research, our comprehensive documentation on creating KLUE will facilitate creating similar resources for other languages in the future. KLUE is available at this https URL.

Sunkyoung Kim

Papers

Evaluation of steady-state characteristics for solid oxide carbon fuel cell short-stacks

KLUE: Korean Language Understanding Evaluation.