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The demands on future energy conversion technologies are becoming increasingly stringent. Biofuels, which are considered to have a critical role in meeting growing energy needs, must find increasing avenues for compliance. Accordingly, ternary fuel blends have received significant attention because their physiochemical properties can be very similar to diesel, while overcoming some challenges associated with traditional biofuel use. Consequently, this work assesses the use of alcohol-biodiesel-vegetable oil blends in Compression Ignition (CI) engines. Three ethanol-biodiesel-vegetable oil blends were developed using 10%, 20% and 30% alcohol and their performances were compared to diesel and neat coconut oil. These blends were tested in a single cylinder diesel engine and their performances assessed using energy, emissions and exergy analyses. The results indicated that the blends had better brake thermal efficiency (BTE) values than diesel at high to medium loads, with the E30 blend having the highest BTE value of 31% at full load conditions as compared to 28.9% for diesel. The blends were also found to be comparable to diesel based on a First Law energy analysis. Additionally, it was found that the blends had better nitric oxides (NO) emission levels than diesel; at full load conditions, the E30 blend had the lowest value of 281 ppm as compared to diesel having a value of 299 ppm. However, they were found to have comparable levels for the other emissions characteristics that were examined. Further, the Second Law analyses indicated that the blends made better use of their fuel energy potential and thus, can be considered as a more suitable fuel for CI engine combustion. Collectively, the results suggest that the ternary blends are a viable candidate for future energy conversion via CI engines. Keywords: Ternary blends; ethanol; coconut oil; CI engines; exergy analysis; alternative fuels

As the cocoa industry continues to grow, there is an increasing need for greater efficiency and higher levels of quality in all areas. The objective assessment of pod stem cut quality is one such critical area, as it not only directly impacts productivity but wider industry economics. Despite this, and the significance of cut quality in other agricultural applications, there is little done in the area of developing an objective and reliable assessment method. This work proposes, develops and tests a Fourier based image processing approach for assessing cut quality. The proposed Fourier Peak Index (FPI) method is implemented in MATLAB 2013 via a series of algorithms. Further, a windowed FPI (WFPI) is also developed and implemented in the same environment. Both methods are tested using a set of 40 images, comprising of 10 reference images, 15 poor cut images and 15 good cut images. The results obtained showed that the FPI method had a 93% accuracy in categorising good cuts, 60% accuracy in categorising poor cuts and an overall accuracy of approximately 77%. It was particularly noted that poor cuts with long, smooth excess bark material attached to the stems, were poorly categorised by the FPI method. Additionally, the method’s effectiveness was found to be significantly influenced by image lighting, as this determined the amount of data loss during the image binarisation step. Notwithstanding, the WFPI method was found to be effective in categorising the images that were incorrectly categorised by the FPI method. The combined efforts of both methods had the potential to increase detection and categorisation accuracy to a maximum of 97%

The increasing use of biofuels in recent years has resulted in renewed consideration of compression ignition (CI) engines for power generation. However, the differences in chemical properties of these fuels have led to some variation in key engine performance parameters. Accordingly, researchers have begun to investigate the use of vibration-based approaches in assessing these behaviors. Despite some progress in assessing the use of diesel in CI engines, very little has been done on assessing the use of fuel blends. The current work, therefore, proposes a vibration-based approach to assessing engine performance in CI engines. It hypothesizes that the variation in cylinder pressure with combustion can be effectively monitored via engine vibration signals. It further proposes the use of a hybrid calculus-statistical method for the analysis of the vibration data. Accordingly, tests were conducted using a single-cylinder engine made to operate on different fuel blends. Conventional thermodynamic data were recorded during its operation. These data were used to calculate fundamental engine performance indicators. Simultaneously, vibration data were collected from the engine using an accelerometer mounted on the engine casing. The vibration data were analyzed using a matlab algorithm. The results showed that the proposed method is able to track the variations in combustion performance, for changes in the fuel used. More importantly, based on the approach, a parameter, which characterizes the nature of combustion taking place, is proposed. The approach proves to be a feasible method for assessing combustion performance of different fuels in CI engines, with minimal computational requirements.