Random forest yöntemi kullanarak polimer elektrolit membran (PEM) yakıt hücrelerinin ömrünün belirlenmesi
Determining life span in polymer electrolyte membrane (PEM) fuel cell using random forest method
Yazar: HAVVA NUR SAĞDIÇ
Danışman: PROF. DR. HANZADE AÇMA ; PROF. DR. SERDAR YAMAN ; DOÇ. DR. HALİT EREN FİGEN
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Kimya Mühendisliği Ana Bilim Dalı / Kimya Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2024
Konu: Kimya Mühendisliği = Chemical Engineering

Özet:
Günümüzde petrol kaynaklarımız hızla tükenmektedir. Ayrıca, otomobillerin 
egzozlarından yayılan gazların neden olduğu hava kirliliği ve petrol kullanımının 
beraberinde getirdiği diğer çevresel sorunlar da bulunmaktadır. Geleneksel içten 
yanmalı motorların yerine geçme vaadi sunan hidrojen yakıt hücreleri, temiz ve 
çevreyi kirletmeyen bir enerji kaynağı olmaları nedeniyle 21. yüzyılda iyi bir seçenek 
haline gelmiştir. Yakıt hücreleri, hidrojen (yakıt) ve oksijenin (hava) kimyasal 
enerjisini doğrudan elektrik ve ısı enerjisine dönüştüren güç elemanlarıdır. Bu 
reaksiyon sonucunda yan ürün olarak elektrik, su ve ısı açığa çıkar. Yakıtın enerjiye 
dönüşümü yanma ile değil, elektrokimyasal bir süreçle gerçekleşir. Bu süreç oldukça 
temiz, sessizdir ve yakıt yanmasından 2-3 kat daha verimlidir. Yakıt hücrelerinin 
avantajları arasında sadece su üretmeleri, yüksek enerji dönüşüm verimliliğine sahip 
olmaları ve umut verici bir süreç olmaları yer almaktadır. Yakıt hücreleriyle çalışan 
elektrikli araçların geliştirilmesi, karbon monoksit (CO) emisyonlarını azaltmanın en 
büyük nedenlerinden biridir. Yakıt hücrelerinin yeniden şarj edilmesine gerek yoktur. 
Diğer pil türlerinin aksine, yakıt hücreleri sadece sürekli bir hidrojen yakıtı tedariğine 
ihtiyaç duyar. Birçok yakıt hücresi türü bulunmaktadır. Bu çalışmada, başlıca hedef, 
maliyetli ve uzun süreli deneyler yapılmaksızın Polimer Elektrolit Membran (PEM) 
yakıt hücresinin ömrünü giriş parametreleri girerek tahmin etmektir. Hedef 
doğrultusunda Random Forest yöntemi seçilmiştir. Literatürden elde edilen deneysel 
verilerden bir set oluşturulmuş ve deneyin girdileri ile çıktıları yapay zekaya 
tanıtılmıştır. Sonuç olarak, herhangi bir veri setinde PEM yakıt hücresinin ömrü bir 
çıktı olarak tahmin edilebilir. Sonuç olarak, bu tahmin modelinden elde edilen 
sonuçlar, PEM yakıt hücrelerinin performansını ve dayanıklılığını artırmaya yönelik 
gelecekteki araştırma ve geliştirme çalışmalarına önemli katkılar sağlayabilir. Makine 
öğrenimi gibi ileri düzey hesaplama tekniklerinden yararlanarak, yakıt hücreleri gibi 
temiz enerji teknolojilerinin benimsenmesinde ilerlemeyi hızlandırabiliriz.

Abstract:
Today, our petroleum resources are rapidly depleting. Additionally, there are 
environmental problems associated with the air pollution caused by the gases emitted 
from car exhaust and the use of petroleum. Hydrogen fuel cells, which promise to 
replace traditional internal combustion engines, have become a promising option in 
the 21st century due to their clean and environmentally friendly energy potential.
Hydrogen is used in fuel cells. Like petroleum, hydrogen is not directly found on Earth 
but must be produced using other sources. Currently, a significant portion of hydrogen 
is produced through the steam methane reforming (SMR) process. Another method of 
hydrogen production is water electrolysis; if electricity generated from renewable 
energy technologies is used in this process, hydrogen can be produced entirely from 
renewable energy sources. The main issues with hydrogen production are: (i) it is an 
energy-intensive process, and (ii) to make a real environmental contribution, it must 
rely on renewable energy sources instead of the SMR process. There is broad 
consensus that producing hydrogen from renewable energy sources (solar, wind, etc.) 
holds great promise for the sustainable development of the world. Hydrogen is not 
directly available in nature but can be produced using other sources. Like any product, 
hydrogen must be packaged, transported, stored, and transferred to bring it from 
production to end use. The primary technological challenge for a functional hydrogen 
economy is storage, and finding a cost-effective way to store hydrogen remains a 
challenge. To be useful for transportation, hydrogen's energy density must be 
increased. However, solutions to the hydrogen storage problem are rapidly emerging. 
The scientific community and commercial organizations are working intensively on 
technologies with the highest hydrogen storage potential per unit volume. The main 
hydrogen storage technologies are compressed hydrogen gas storage, liquefied 
hydrogen storage, solid-state hydrogen storage, and chemical hydrogen storage. 
Compared to other gases used as fuel (natural gas, LPG), much smaller amounts of 
hydrogen can be stored using the same storage techniques. In other words, a larger 
volume is required to store the same amount of hydrogen. Hydrogen storage 
technologies are critical for hydrogen-powered energy systems. While traditional 
technologies store hydrogen as compressed gas and cryogenic liquid, underground 
storage is emerging as the preferred method for large-scale applications.
The fuel cell was first developed by Sir William Grove (1811-1896), an English lawyer 
and amateur scientist. Fuel cells are power devices that convert the chemical energy 
of hydrogen (fuel) and oxygen (air) directly into electricity and heat. As a result of this 
reaction, electricity, water, and heat are released as by-products. The conversion of 
fuel into energy occurs not through combustion, but through an electrochemical 
process. This process is very clean, quiet, and 2-3 times more efficient than fuel 
combustion. The advantages of fuel cells include producing only water as a by￾product, having high energy conversion efficiency, and being a promising technology. The development of fuel cell-powered electric vehicles is one of the main reasons for 
reducing carbon monoxide (CO) emissions. Fuel cells do not need to be recharged. 
Unlike other types of batteries, fuel cells only require a continuous supply of hydrogen 
fuel. Various types of fuel cells have been thoroughly researched and studied to 
increase production potential and make them as accessible and usable by as many 
people as possible. The type of catalyst is very important as it affects the likelihood of 
undesirable side reactions. The catalyst must have high activity and selectivity over a 
wide temperature range. A fuel cell consists of three main components: two electrodes 
(anode and cathode), two catalyst layers, and one electrolyte (membrane). The two 
electrodes surround the electrolyte, and the catalyst layers are placed between the 
electrodes and the electrolyte. The process begins with the oxidation of a hydrogen 
molecule at the anode. Through oxidation, two electrons (2e-
) and two hydrogen ions 
(2H+
) are released. These ions are separated using a polymer electrolyte membrane 
(PEM). This membrane allows hydrogen ions to pass directly to the cathode while 
blocking electrons, forcing them to pass through an external circuit. This flow of 
electrons generates the produced energy. Finally, when both hydrogen ions and 
electrons reach the cathode, they combine with oxygen and are reduced to water (H2O).
A fuel cell is two to three times more efficient than a gasoline-powered internal 
combustion engine. There are many different types of fuel cells available for a wide 
range of applications. These applications include Mobility/Transportation, Power 
Systems, Energy Storage, Consumer Electronics, and Unmanned Aerial Vehicles.
There are various fuel cell architectures, all of which generally consist of an anode and 
cathode electrode where reactions occur, electrons are conducted, and an electrolyte 
that provides ion conductivity, connecting the two electrodes. All types of fuel cells 
are based on the same electrochemical principles, but they use different materials, 
temperatures, and performance characteristics. Different fuel cell types are 
distinguished by the type of electrolyte material used. Fuel cells are divided into five 
types based on the electrolyte material used: Polymer Electrolyte Membrane Fuel Cell 
(PEMFC), Molten Carbonate Fuel Cell (MCFC), Solid Oxide Fuel Cell (SOFC), 
Alkaline Fuel Cell (AFC), and Phosphoric Acid Fuel Cell (PAFC).
PEMFC is a scalable power source, meaning the same technology can be used in 
different applications. Due to its advantages, it is often preferred as a fuel source in 
mobile and stationary devices and the automotive industry. Additionally, its popularity 
is increasing in the aviation industry, and numerous projects aim to use PEMFCs in 
commercial passenger aircraft. In applications such as drones, where flight endurance 
and continuous environmental monitoring are required, lithium-ion batteries may not 
be beneficial. Due to their low energy densities, they cannot provide sufficient power 
for long flight durations. In contrast, PEMFCs can be used for longer flight durations 
due to their high energy density and fast refueling times. However, their current usage 
is not widespread due to commercial challenges. There are still many obstacles to 
making this technology commercially viable. The main ones include high-cost fuel 
cell systems, high maintenance costs, and short lifespans. The lifetime of a fuel cell 
operating under regular conditions can be very long, up to 26,300 hours; however, 
during accelerated durability tests, the voltage degradation rate is very high, especially 
during cold starts, where voltage degradation can reach 22.5 mV. Fuel cell lifespan 
requirements range from 4,000 hours for vehicle applications to 20,000 hours for buses 
and 40,000 hours for stationary applications. The longest known PEMFC test was a 
lifetime test reported by GE in 1979, which lasted 60,000 hours. Due to the absence of moving parts, fuel cells are naturally reliable systems. However, 
reactants, various materials including the catalyst, significant electrical potential and 
flux density, temperature, and pressure ranges cause material degradation under 
various operating conditions. Additionally, factors such as non-ideal water 
management, catalyst degradation, and fuel insufficiency can reduce the lifespan of a 
PEMFC and even lead to voltage drop. Managing the lifespan of the fuel cell stack 
depends on how these components and interacting conditions are designed and 
managed.
In this study, the main goal is to predict the lifespan of PEM fuel cells by inputting 
parameters without conducting costly and long-term experiments. To achieve this 
goal, the Random Forest method was selected. The Random Forest (RF) algorithm, 
proposed by L. Breiman in 2001, has been extremely successful as a general-purpose 
classification and regression method. This approach, which combines several random 
decision trees and aggregates their predictions by averaging them, has shown excellent 
performance in situations where the number of variables is much larger than the 
number of observations. Additionally, it is flexible enough to be applied to large-scale 
problems, can easily be adapted to various specific learning tasks, and provides 
measures of variable importance.
This supervised learning method is inspired by the early works of Amit and Geman 
(1997), Ho (1998), and Dietterich (2000) and operates based on a simple yet effective 
"divide and conquer" principle. It samples fractions of the data, grows a random tree 
predictor on each small part, and then aggregates and combines these predictors. What 
has contributed significantly to the popularity of forests is their applicability to a 
variety of prediction problems and their having few tuning parameters.
For lifespan prediction, experimental data obtained from research was first used to 
create different datasets. The RF application was initially tested using four basic 
parameters. It is known that the number of parameters can vary depending on the type 
of target variable, and therefore, it is common to start with as many parameters as 
possible and then reduce them until an optimal prediction is achieved.
Thus, the initial parameters were gradually reduced. The Python programming 
language was used for the Random Forest algorithm. To validate the progress of the 
algorithm, 80% of the data was used to train the model, and 20% was used to test the 
model. The training dataset needs to be larger than the test dataset to avoid overfitting. 
Predictions were evaluated by considering the R² and RMSE (Root Mean Square 
Error) values. Outliers were eliminated during the calculation process.
As a result, the outcomes obtained from this prediction model can make significant 
contributions to future research and development efforts aimed at improving the 
performance and durability of PEM fuel cells. By utilizing advanced computational 
techniques such as machine learning, we can accelerate the adoption of clean energy 
technologies like fuel cells.