Syed Ammal Engineering College

About: Syed Ammal Engineering College is a based out in . It is known for research contribution in the topics: Diesel fuel & Biodiesel. The organization has 285 authors who have published 349 publications receiving 4258 citations.

The effect of cerium oxide additive on the performance and emission characteristics of a CI engine operated with rice bran biodiesel and its blends

Abstract: In this study, the rice bran oil (RBO) has been converted into methyl ester with an aid of transesterification reaction. Chemically, transesterification means conversion of triglyceride molecule or a complex fatty acid into alcohol and ester by removing the glycerin and neutralizing the free fatty acids. The B20 blend samples [80% diesel + 20% biodiesel] were prepared for each methyl ester obtained from RBO and then the cerium oxide (CeO2) nanoparticles were added to the each B20 blend samples at a dosage of 50 ppm and 100 ppm with an aid of ultrasonicator. Moreover, in the absence of any engine modifications, the performance and emission characteristics of those blend samples have been investigated from the experimentally measured values such as density, viscosity, cloud point, pour point, and calorific value while the engine performance was also analyzed through the parameters like exhaust gas temperature (EGT), brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), exhaust emis...

Environmental effect of CI engine using microalgae methyl ester with doped nano additives

Abstract: Algae are organisms that grow in marine environments and use carbon dioxide and light to create bio-mass. There are two groupings of algae: microalgae and macroalgae. Macroalgae are the large, multi-cellular algae often seen growing in ponds. Microalgae, on the other hand, are tiny, unicellular algae that normally grow in suspension within a body of water. Algae oil from microalgae has the possible to become a sustainable fuel source as biodiesel. Microalgae are produced through photosynthesis by utilizing sunlight, water, carbon dioxide and other nutrients. The Botryococcus braunii algal oil was extracted by mechanical extraction method. The transesterification reaction of Botryococcus braunii algal oil with methanol and base catalyst was used for the production of biodiesel. The samples B20 were prepared for each methyl ester obtained from Botryococcus braunii algal oil separately and then the doping of TiO 2 and SiO 2 nanoparticles were added to the each B20 blend samples at a dosage of 50 ppm and 100 ppm with an aid of ultrasonicator. Moreover, in the absence of any engine modifications, the performance and emission characteristics of those blend samples have been investigated from the experimentally measured values such as density, viscosity, calorific value, etc. while the engine performance was also analyzed through the parameters like BSFC, BTH, exhaust emission of CO, HC, NOx and CO 2 . The experimental results reveal that the use of biodiesel blend with nano additives in diesel engine has exhibited good improvement in performance characteristic and reduction in exhaust emissions.

The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment

Abstract: Recently, researchers have built new deep learning (DL) models using a single image modality to diagnose age-related macular degeneration (AMD). Retinal fundus and optical coherence tomography (OCT) images in clinical settings are the most important modalities investigating AMD. Whether concomitant use of fundus and OCT data in DL technique is beneficial has not been so clearly identified. This experimental analysis used OCT and fundus image data of postmortems from the Project Macula. The DL based on OCT, fundus, and combination of OCT and fundus were invented to diagnose AMD. These models consisted of pre-trained VGG-19 and transfer learning using random forest. Following the data augmentation and training process, the DL using OCT alone showed diagnostic efficiency with area under the curve (AUC) of 0.906 (95% confidence interval, 0.891–0.921) and 82.6% (81.0–84.3%) accuracy rate. The DL using fundus alone exhibited AUC of 0.914 (0.900–0.928) and 83.5% (81.8–85.0%) accuracy rate. Combined usage of the fundus with OCT increased the diagnostic power with AUC of 0.969 (0.956–0.979) and 90.5% (89.2–91.8%) accuracy rate. The Delong test showed that the DL using both OCT and fundus data outperformed the DL using OCT alone (P value < 0.001) and fundus image alone (P value < 0.001). This multimodal random forest model showed even better performance than a restricted Boltzmann machine (P value = 0.002) and deep belief network algorithms (P value = 0.042). According to Duncan’s multiple range test, the multimodal methods significantly improved the performance obtained by the single-modal methods. In this preliminary study, a multimodal DL algorithm based on the combination of OCT and fundus image raised the diagnostic accuracy compared to this data alone. Future diagnostic DL needs to adopt the multimodal process to combine various types of imaging for a more precise AMD diagnosis.