Bütanol-Diesel yakıtı kullanılan bir sıkıştırma ateşlemeli motorda motor performansı ve egzoz emisyonlarının yapay sinir ağları ile tahmini

Abstract

Bu çalışmada saf Diesel yakıtı ve 5 farklı bütanol-Diesel yakıt karışımları (%3, 6, 9, 12 ve 15) kullanılan bir Diesel motorunda (SA) farklı devir sayılarında ve tam yük durumunda motor performansı ve egzoz emisyonları yapay sinir ağları (YSA) ile modellenmiştir. Deneysel çalışmalarda; tek silindirli bir Diesel motoru kullanılmıştır. Sunulan YSA modelinde; Scaled Conjugate Gradient ve Levenberg–Marquardt algoritmaları, tek katman ve sigmoid transfer fonksiyonu kullanılmıştır. Girdi katmanı devir sayısı ve karışım oranını içermektedir. Çıktı katmanı ise özgül yakıt tüketimi, efektif verim, NOx emisyonu ve CO emisyonu parametrelerini içermektedir. Ağın performansı için ortalama mutlak yüzde hata (MAPE) değerleri ve ortalama hata kareleri (MSE) hesaplanmıştır. Geliştirilen YSA modelin, deneysel sonuçlarla uyum içinde olduğu görülmüştür.

Keywords

• Diesel motorlar,Yapay sinir ağları,Motor performansı ve egzoz emisyonları

Prediction of engine performance and exhaust emissions using artificial neural network in a compression ignition engine fueled with butanol-diesel fuel

Abstract

In this study, engine performance and exhaust emission of a Diesel engine (CI) at full load and various speeds conditions using only Diesel fuel and five different blends with butanol (3, 6, 9, 12 and 15 v/v %) were modeled by using Artificial Neural Network (ANN). A single-cylinder diesel engine was used in the experimental studies. Single layer, logistic sigmoid transfer function Scaled Conjugate Gradient and Levenberg–Marquardt algorithms were used in the presented ANN model. Input layer includes engine speed and blending ratio. Output layer includes parameters of brake specific fuel consumption, effective efficiency, NOx emission and CO emission. Mean absolute percentage error (MAPE) data and mean square error (MSE) and were calculated for performance of the networks. It was obtained that there was a consistency among the presented ANN model and the data obtained from experiments.

Keywords

• Diesel engines,Artificial neural networks,Engine performance and exhaust emissions

References

1. Gürgen S. Modeling of Cyclic Variability Using Artificial Neural Network in a Diesel Engine Fueled with Alternative Fuel Blends. MSc Thesis, Karadeniz Technical University, Trabzon, Turkey, 2016.
2. Şahin Z, Aksu ON. “Experimental investigation of the effects of using low ratio n-butanol/diesel fuel blends on engine performance and exhaust emissions in a turbocharged DI diesel engine”. Renewable Energy, 77, 279-290, 2015.
3. Kumar V, Gupta D, Siddiquee MWN, Nagpal A, Kumar N. “Performance and emission characteristics of n-butanol and iso-butanol diesel blend comparison”. SAE Technical Paper 2015- 01-2819, 2015.
4. Salih Ö. “Alkollerin içten yanmalı motorlarda alternatif yakıt olarak kullanılması”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 19(1),97-114, 2014.
5. Bilgin A, Durgun O, Sahin Z. “The effects of diesel-ethanol blends on diesel engine performance”. Energy Sources, 24(5), 431-440, 2002.
6. Kumar S, Cho JH, Park J, Moon I. “Advances in diesel-alcohol blends and their effects on the performance and emissions of diesel”. Renewable and Sustainable Energy Reviews, 22, 46-72, 2013.
7. Giakoumis EG, Rakopoulos CD, Dimaratos AM, Rakopoulos DC. “Exhaust emissions with ethanol or n-butanol diesel fuel blends during transient operation: A review”. Renewable and Sustainable Energy Reviews, 17, 170-190, 2013.
8. Rakopoulos DC, Rakopoulos CD, Giakoumis EG, Dimaratos AM, Kyritsis DC. “Effects of butanol–diesel fuel blends on the performance and emissions of a high-speed DI diesel engine”. Energy Conversion and Management, 51, 1989-1997, 2010.

9. Doğan O. “The influence of n-butanol/diesel fuel blends utilization on a small diesel engine performance and emissions”. Fuel, 90(7), 2467-2472, 2011.
10. Chen G, Yu W, Li Q, Huang Z. “Effects of n-butanol addition on the performance and emissions of a turbocharged common-rail diesel engine”. SAE Techincal Paper 2012-0148, 7191, 2012.
11. Shanmugam P, Sivakumar V, Murugesan A, Ilangkumaran M. “Performance and exhaust emissions of a diesel engine using hybrid fuel with an artificial neural network”. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 33(15), 1440-1450, 2011.
12. Çay Y, Çiçek A, Kara F, Sağiroğlu S. “Prediction of engine performance for an alternative fuel using artificial neural network”. Applied Thermal Engineering, 37, 217-225, 2012.
13. Ghobadian B, Rahimi H, Nikbakht AM, Najafi G, Yusaf TF. “Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network”. Renewable Energy, 34(4), 976-982, 2009.
14. Meng X, Jia M, Wang T. “Neural network prediction of biodiesel kinematic viscosity at 313K”. Fuel, 121, 133-140, 2014.
15. Rakopoulos DC, Rakopoulos CD, Hountalas DT, Kakaras EC, Giakoumis EG, Papagiannakis RG. “Investigation of the performance and emissions of bus engine operating on butanol/diesel fuel blends”. Fuel, 89(10), 2781-2790, 2010.