The design of classroom desktop-chairs (one size fits all) in many institutions is usually done with no consideration of ergonomics. Therefore, there is a mismatch between classroom desktop-chairs dimensions and students' anthropometric characteristics. This may cause musculoskeletal disorders and affect learning effectiveness due to sitting for a long time in an awkward position. Ergonomically designed furniture is known to reduce musculoskeletal disorders and improve the attentiveness of students in the classroom environment. This research, therefore, aimed to use the concept of ergonomics to design classroom desktop-chair for students in Uasin-Gishu County. Anthropometric data was collected from a total of three hundred and eighty-two (382) students of both genders. The selected tertiary institutions for the survey were Moi University (MU), University of Eldoret (UoE), Rift Valley Technical Training Institute (RVTTI) and The Eldoret National Polytechnic (TENP). Fourteen (14) anthropometric measurements were taken from students with the help of anthropometric tools. The research applied fundamental engineering principles of product design and was carried out in compliance with ISO 7250-1: (Basic human body measurements for technological design part 1: Body measurement definitions and landmarks). The data obtained was analysed using Minitab 17.0 statistical package, to get the mean, standard deviation, minimum, maximum, 5 th , 50 th and 95 th percentiles. SolidWorks 2019, was used to design a desktopchair. The analysed anthropometric data set was used to design, a suitable classroom desktop-chair. One type of ergonomically suitable classroom desktop-chair design was proposed to improve the match between classroom desktop-chairs dimensions and students' anthropometric characteristics. The analysed anthropometric data set can be used for the design of classroom desktop-chairs for students not only in the selected tertiary institutions but all over Kenya.
The rapid growth in the economy of many countries has led to high demand and over dependence on fossil fuels whose reserves are limited. Together with their undesirable environmental emissions, there has been need for identifying alternative fuels that are suitable for the diesel engines. Amongst other fuels, tyre pyrolysis oil (TPO) has been identified as a potential additive or supplement to the diesel fuel. When considering an alternative fuel, many factors are taken into consideration including emissions and performance in diesel engines. In in this review, published work on tyre pyrolysis oil with main focus on its use in diesel engines as an alternative is discussed. Production of tyre pyrolysis oil and the influence of pyrolysis process conditions on TPO yield are discussed. Optimum oil yield obtained during pyrolysis is within the range of 450-550 o C depending on the reactor conditions. Properties of TPO are also discussed and compared to those of diesel and relevant standards. The effect of TPO and its blends on engine performance with emphasis on fuel consumption, thermal efficiency and emissions are also reviewed. Overall, the diesel engine performs better with low concentration of TPO in the Diesel/TPO blend than with higher concentration of TPO. This is because fuel properties such as such aromatic content, density, viscosity, Sulphur content and low Cetane number are higher compared to diesel.
Investigating engine performance and emissions under varying conditions and for different fuels is a costly and timeconsuming exercise. This may also require sophisticated equipment which may not be readily available. In this study, two Analytical Neural Network (ANN) models were developed to predict diesel engine performance and emissions respectively, when fuelled by Distilled Tyre Pyrolysis Oil (DTPO). The models were based on back propagation learning algorithm. The data used to train and test the ANN was experimentally collected from a single cylinder four stroke diesel engine operated at speed ranging from 800 to 3500 rpm. The fuel blends contained 0-80% Distilled Tyre Pyrolysis Oil (DTPO) in diesel fuel. The fuel blends and engine speed were the input variables for each network. The performance of the model was evaluated by comparing experimental and ANN predicted results. The coefficient of determination (R 2) was found to be 0.9831, 0.9977, 0.9852, 0.9836, 0.9961, 0.9921 and 0.997 for Torque, Power, Brake Specific Fuel Consumption, Peak pressure, HC, NOx and CO respectively. The Mean Square Error between the measured and simulated values was 0.00396 for the engine performance model and 0.000163 for the emissions model. It can be concluded that the engine performance and emissions of a Diesel Engine running on DTPO and its blends with diesel fuel can be reliably predicted using Artificial Neural Network.
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