Sule, Ahmed and Latiff, Zulkarnain Abdul and Abbas, Mohammed Azman and Veza, Ibham (2023) Particulate matter and nitrogen oxide emissions prediction using artificial neural network for diesel engine running on biodiesel-diesel fuel with nano-additive. In: 2021 International Summit on Education, Technology and Humanity, ISETH 2021, 20 December 2021through 21 December 2021, Surakarta.
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Particulate matter and nitrogen oxide emissions prediction using artificial neural network for diesel engine running on biodiesel-diesel fuel with nano-additive.pdf Restricted to Repository staff only Download (243kB) |
Abstract
Fossil fuel combustions from automotive industries and vehicles causes second highest emission of gases influencing global warming and climate change. Biofuels and biodiesels are renewable energy sources and alternative candidates to fossil fuel but have limitations creating requirement for blending and application of additives to biodiesel-diesel fuels. Nano-additives is promising due to higher atomic level and surface area to volume ratio; however, higher cost of nano-additives makes random selection for testing many varieties difficult, also; nitrogen oxide (NOx) emissions and particulate matter (PM) from unburnt nanoparticles is a major challenge. This work therefore uses artificial neural network (ANN) feed forward back propagation as learning algorithm to predict PM and NOx emissions using experimental data from test conducted on a single cylinder diesel engine running on palm oil biodiesel blended with conventional diesel and Iron (II) oxide (Fe2O3) nano-additive stabilized in isopropyl as surfactant at three engine loads (25%, 50%, 75%). Levenberg-Marquardt was used for training data with 6 input, two hidden layers of 5 set (10 total) and 2 output layers. The target parameters (NOx and PM) were accurately predicted by ANN training, the highest performance denoted by R and R2 of values 0.99999 and 0.9999 respectively. Based on experimental results and weight of input parameters, it is conclusive that higher percentage by volume of nano-additive reduces PM until optimal level before 'excess' dose Fe2O3 nano-additive causes higher PM emitted; lower nominal NOx resulted with continuous nano-additive increment for all load conditions. A satisfactory ANN application for prediction was achieved.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Diesel fuel, Fossil fuels, Biofuels, Climate change, Artificial neural networks, Combustion engine, Nanoparticle, Oxides, Surface area to volume ratio, Learning and learning models |
| Divisions: | Faculty of Mechanical Engineering |
| Depositing User: | Maizatul Najwa Ahmad |
| Date Deposited: | 20 Sep 2024 16:25 |
| Last Modified: | 20 Sep 2024 16:25 |
| URI: | http://eprints.utem.edu.my/id/eprint/27911 |
| Statistic Details: | View Download Statistic |
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