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All Journal LIMNOTEK - Perairan Darat Tropis di Indonesia Techno-Fish Mechanical Engineering for Society and Industry Automotive Experiences
Sri Mumpuni Ngesti Rahaju
Universitas Bung Karno
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation

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Journal : Automotive Experiences

 1 
Acetone-Butanol-Ethanol as the Next Green Biofuel - A Review Sri Mumpuni Ngesti Rahaju; Ibham Veza; Noreffendy Tamaldin; Ahmed Sule; Anthony C. Opia; Mohammed Bashir Abdulrahman; Djati Wibowo Djamari
Automotive Experiences Vol 5 No 3 (2022)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.6335

Abstract

The development of diesel engines faces challenging targets to satisfy stringent emissions regulation. To address this issue, the use of alcohol biofuels such as methanol and ethanol has attracted numerous attention due to their physicochemical properties and the possibility to be produced from renewable sources and agricultural waste material. Compared to ethanol, longer carbon alcohol such as butanol has higher energy density and lower latent heat, hygroscopicity, aggressivity, and toxicity. It can also be produced from biomass. Yet, despite its noticeable advantages, the use of butanol in the internal combustion engine is hindered by its low production efficiency. If Acetone-Butanol-Ethanol (ABE) is further distilled and purified, pure butanol and ethanol can be acquired, but this involves an energy-intensive process, thus increasing the production cost of butanol. To solve this problem, the direct use of ABE as a biofuel is considered a promising strategy. The idea of using ABE directly in internal combustion engines is then proposed to solve the economic issue of high butanol production costs. A scoping literature review was performed to screen and filter previously published papers on ABE by identifying knowledge gaps instead of discussing what is already known. Therefore, repeated and almost identical studies were eliminated, thus reporting only the most significant and impactful published papers. In terms of the objective, this article aims to review the progress of ABE as a promising biofuel in regard to the engine performance, combustion, and emission characteristics. Focus is also given to ABE’s physicochemical properties. Despite their considerable importance, the fuel properties of ABE are rarely discussed. Therefore, this review article intends to analytically discuss the physicochemical properties of ABE in terms of their calorific value, density, kinematic viscosity, and distillation. In general, it is concluded that engine emissions such as NOx and Particulate Matter (PM) could be reduced considerably with the use of ABE. Yet, the BSFC was found to increase due to the relatively lower calorific value and density of ABE blends as opposed to gasoline or diesel fuel, thereby increasing its fuel consumption. In terms of ABE’s fuel properties, in general, ABE can be used due to its satisfying physicochemical properties. However, it should be noted that the ABE-gasoline/diesel blends are greatly influenced by each of its component ratios (acetone, butanol, ethanol).
Comparison of Various Prediction Model for Biodiesel Cetane Number using Cascade-Forward Neural Network Sri Mumpuni Ngesti Rahaju; April Lia Hananto; Permana Andi Paristiawan; Abdullahi Tanko Mohammed; Anthony Chukwunonso Opia; Muhammad Idris
Automotive Experiences Vol 6 No 1 (2023)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.7050

Abstract

Cetane number (CN) is one of the important fuel properties of diesel fuels. It is a measurement of the ignition quality of diesel fuel. Numerous studies have been published to predict the CN of biodiesels. More recently, the utilization of soft computing methods such as artificial neural networks (ANN) has received considerable attention as a prediction tool. However, most studies in the use of ANN for estimating the CN of biodiesels have only used one algorithm to train a small number of datasets. This study aims to predict the CN of 63 biodiesels based on the fatty acid methyl esters (FAME) composition by developing an ANN model that was trained with 10 different algorithms. To the best of our knowledge, this is the first study to predict the CN of biodiesels using numerous ANN training algorithms utilizing sizeable datasets. Results revealed that the ANN model trained with Levenberg-Marquardt gave the highest prediction accuracy. LM algorithm successfully predicted the CN of biodiesels with the highest correlation and determination coefficient (R = 0.9615, R2 = 0.9245) as well as the lowest errors (MAD = 2.0804, RMSE = 3.1541, and MAPE = 4.2971). Hence, the Cascade neural network trained with the LM algorithm could be considered a promising alternative to the empirical correlations for predicting biodiesel’s CN.
Co-Authors Abdullahi Tanko Mohammed Ahmed Sule Ahmed Sule Anthony C. Opia Anthony Chukwunonso Opia April Lia Hananto Dhany Hermansyah Djati Wibowo Djamari Esam Abu Baker Ali Ibham Veza Mohammed Bashir Abdulrahman Muhammad Idris Muhammad Idris Muhammad Zacky Asy'ari Noreffendy Tamaldin Permana Andi Paristiawan Safarudin Gazali Herawan Santoso, D. A. Septya, Fanny Shuaibu Alani Balogun Sucahyo, S. C. Tomi Ramadona Yani, M.