Yapay zeka teknikleriyle PEMFC membran elektrot yapısının polarizasyon eğrilerinin tahmini

Yıl 2024, , 1538 - 1544, 15.10.2024

Haydar Bayar , Erkan Dursun

https://doi.org/10.28948/ngumuh.1536258

Öz

Proton değişim membranlı yakıt hücreleri (PEMFC) otomobiller, otobüsler, kesintisiz güç kaynakları ve kombine ısı güç sistemlerinde ticari olarak kullanılmaktadır ve yakıt hücresi pazarında önemli bir yere sahiptir. Yakıt hücresi performansı, tasarım ve üretim süreçlerinde polarizasyon eğrileri ile karakterize edilir. Bu çalışma, farklı çalışma koşullarında eğitilip test edilen karşılaştırmalı yapay zeka (AI) modelleri kullanarak bir PEMFC'nin polarizasyon eğrilerini tahmin etmektedir. AI model girdileri hücre sıcaklığı, nem, anot-katot akışı ve membran direncidir. Çıktılar ise hücre voltajı ve akım yoğunluğudur. Model çıktıları, MATLAB yazılımı kullanılarak 50°C, %100 nem koşullarındaki deneysel değerlerle karşılaştırılmıştır. ANFIS tahmini için hataların ortalama karekökü (RMSE) 0.056112 iken, ANN tahmini için 0.011919'dur. Bu sonuçlar, Yapay Sinir Ağı (ANN) yönteminin, PEMFC'nin Membran Elektrot Yapısının (MEA) davranışını tahmin etmede Uyarlamalı Nöro Bulanık Çıkarım Sistemi (ANFIS) yönteminden daha iyi performans gösterdiğini göstermektedir. Modeller yüksek doğrulukla umut verici sonuçlar vermiştir.

Anahtar Kelimeler

Yapay zeka, Elektrokimyasal cihazlar, Enerji Dönüşümü, Yakıt hücreleri

Prediction of polarization curves of PEMFC membrane electrode assembly using artificial intelligence technics

Öz

Proton exchange membrane fuel cells (PEMFCs) are used commercially in automobiles, buses, uninterruptible power supplies, and combined heat power systems, holding a significant place in the fuel cell market. Fuel cell performance is characterized by polarization curves in design and manufacturing processes. This study predicts a PEMFC’s polarization curves using comparative artificial intelligence (AI) models trained and tested under different operational conditions. The AI model inputs are cell temperature, humidity, anode-cathode flow, and membrane resistance. The outputs are cell voltage and current density. The model outputs are compared with experimental values for 50°C, 100% humidity using MATLAB software. The average Root Mean Square Error (RMSE) for the ANFIS prediction is 0.056112, while for the ANN prediction it is 0.011919. These results indicate that the Artificial Neural Network (ANN) method outperforms the Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting the behavior of the PEMFC’s Membrane Electrode Assembly (MEA). The models showed promising results with high accuracy.

Anahtar Kelimeler

Artificial intelligence, electrochemical devices, energy conversion, fuel cells

Teşekkür

Authors would you like to thank UNIDO-ICHET and Prof. Mehmet Suha Yazıcı for providing support to this study.

Kaynakça

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