Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Prediction of Kinematic Viscosities of Biodiesels Derived from Edible and Non-edible Vegetable Oils by Using Artificial Neural Networks
Arabian Journal for Science and Engineering · Aralık 2015
YÖKSİS Kayıtları
Prediction of Kinematic Viscosities of Biodiesels Derived fromEdible and Non edible Vegetable Oils by Using Artificial NeuralNetworks
Arab J Sci Eng · 2015 SCI-Expanded
Dr. Öğr. Üyesi SADİYE AYŞE ÇELİK →
YÖKSİS Kayıtları — ISSN Eşleşmesi
New WOA Variants for Superior Meta-heuristic Optimization with Multiple Hunter Whale Leading
2025 ISSN: 2193-567X SCI-Expanded Q2
Dr. Öğr. Üyesi SEMA SERVİ →
A New Lifetime Model: Properties, Estimation, and Applications in Quality Control
2025 ISSN: 2193-567X SCI-Expanded Q2
Arş. Gör. ERDEM CANKUT →
Brain Tumor Detection with Transfer Learning Models Based on Attention Modules
2026 ISSN: 2193-567X SCI-Expanded Q2
Dr. Öğr. Üyesi ZÜLEYHA YILMAZ ACAR →
Effect of Silica/Graphene Nanohybrid Particles on the Mechanical Properties of Epoxy Coatings
2019 ISSN: 2193-567X SCI-Expanded Q3
Doç. Dr. ŞAKİR YAZMAN →
Heuristic Optimization Based on Penalty Approach for Surface Permanent Magnet Synchronous Machines
2020 ISSN: 2193-567X SCI-Expanded Q1
Doç. Dr. MEHMET AKİF ŞAHMAN →
A Method of Classification Performance Improvement Via a Strategy of Clustering-Based Data Elimination Integrated with k-Fold Cross-Validation
2021 ISSN: 2193-567X SCI-Expanded Q2
Dr. Öğr. Üyesi ONUR İNAN →
Training Feed-Forward Multi-Layer Perceptron Artificial Neural Networks with a Tree-Seed Algorithm
2020 ISSN: 2193-567X SCI-Expanded Q3
Doç. Dr. AHMET CEVAHİR ÇINAR →
Solving Multi‑Objective Resource Allocation Problem Using Multi‑Objective Binary Artificial Bee Colony Algorithm
2021 ISSN: 2193-567X SCI-Expanded Q3
Prof. Dr. FATİH BAŞÇİFTÇİ →
Theoretical and Experimental Investigation of the Performance of an Atkinson Cycle Engine
2021 ISSN: 2193-567X SCI-Expanded Q3
Dr. Öğr. Üyesi HALİL ERDİ GÜLCAN →
Optimization of Electricity Generation Parameters with Microbial Fuel Cell Using the Response Surface Method
2022 ISSN: 2193-567X SCI-Expanded Q2
Prof. Dr. ŞAKİR TAŞDEMİR →
The Innovative Approach to Real-Time Detection of Fuel Types Based on Ultrasonic Sensor and Machine Learning
2024 ISSN: 2193-567X SCI-Expanded Q2
Prof. Dr. MEHMET ÇUNKAŞ →
Enhanced Gain Dual-Port Compact Printed Meandered Log-Periodic Monopole Array Antenna Design with Octagonal-Ring Shaped FSS for Broadband 28 GHz Applications
2024 ISSN: 2193-567X SCI-Expanded Q2
Dr. Öğr. Üyesi MEHMET YERLİKAYA →
The Innovative Approach to Real-Time Detection of Fuel Types Based on Ultrasonic Sensor and Machine Learning
2024 ISSN: 2193-567X SCI-Expanded Q2
Dr. Öğr. Üyesi UĞUR TAŞKIRAN →
Determination of Temperature Effects on Cortical Bone Milling Using Taguchi Method
2025 ISSN: 2193-567X SCI-Expanded Q2
Prof. Dr. SÜLEYMAN NEŞELİ →
Characterizing Machining Indicators with Machine Learning Models Under Cellulose Nanocrystal and Graphene-Based Nanofluid Conditions
2025 ISSN: 2193-567X SCI-Expanded Q2
Doç. Dr. MUSTAFA KUNTOĞLU →
Makale Bilgileri
ISSN2193567X
Yayın TarihiAralık 2015
Cilt / Sayfa40 · 3745-3758
Scopus ID2-s2.0-84946593695
Özet
In the present study, the seeds named as wild mustard (Sinapis arvensis L.) and safflower (Carthamus tinctorius L.) were used as feedstocks for production of biodiesels. In order to obtain wild mustard seed oil (WMO) and safflower seed oil (SO), screw press apparatus was used. wild mustard seed oil biodiesel (WMOB) and safflower seed oil biodiesel (SOB) were produced using methanol and NaOH by transesterification process. Various properties of these biodiesels such as density (883.62–886.35 kgm-3), specific gravity (0.88442–0.88709), kinematic viscosity (5.75–4.11 mm2s-1), calorific value (40.63–38.97 MJkg-1), flash point (171– 175∘C), water content (328.19–412.15 mgkg-1), color (2.0–1.8), cloud point [5.8–(-4.7)∘C], pour point [(–3.1)–(–13.1)∘C), cold filter plugging point [(−2.0)–(-9.0)∘C)], copper strip corrosion (1a–1a) and pH (7.831–7.037) were determined. Furthermore, kinematic viscosities of biodiesels and euro-diesel (ED) were measured at 298.15–373.15 K intervals with 1 K increments. Four different equations were used to predict the viscosities of fuels. Regression analyses were done in MATLAB program, and R2, correlation constants and root-mean-square error were determined. 1–7–7–3 artificial neural network (ANN) model with a back propagation learning algorithm was developed to predict the viscosities of fuels. The performance of neural network-based model was compared with the performance of viscosity prediction models using same observed data. It was found that ANN model consistently gave better predictions (0.9999 R2 values for all fuels) compared to these models. ANN model was showed 0.34 % maximum errors. Based on the results of this study, ANNs appear to be a promising technique for predicting viscosities of biodiesels.
Yazarlar (4)
1
Tanzer Eryilmaz
2
Murat Kadir Yesilyurt
3
Alper Taner
4
Sadiye Ayşe Çelik
ORCID: 0000-0002-0765-642X
Anahtar Kelimeler
Artificial neural network (ANN)
Fuel property
Kinematic viscosity
Prediction
Safflower (Carthamus tinctorius L.)
Wild mustard (Sinapis arvensis L.)
Kurumlar
Bozok Üniversitesi
Yozgat Turkey
Ondokuz Mayis Üniversitesi
Samsun Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Arabian Journal for Science and Engineering
Q1
SJR Skoru0,545
H-Index89
ÜlkeGermany
Multidisciplinary (Q1)
Metrikler
21
Atıf
4
Yazar
6
Anahtar Kelime