PHD-Department of Energy Engineering

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    Simulation and Optimisation of a Drying Model for a Forced Convection Grain Dryer
    (Kenyatta University, 2018) Osodo, Booker Onyango
    Forced convection grain dryers are more efficient and achieve greater drying rates than natural convection dryers. However, it is necessary to provide an appropriate solar air heater in order to achieve the required drying air temperature. Well sized fan and drying cabinet, as well as an optimal combination of air velocity, temperature and grain layer thickness are also essential for improved performance of such a dryer. In order to predict variation of moisture content with time during the drying process, it is necessary to have an appropriate drying model. In this study carried out at Njoro, Nakuru County in Kenya, an experimental grain dryer was sized, fabricated and its performance investigated under different drying conditions. Simulation of air flow within an initial model of the dryer was done and the results used to size the fan and drying cabinet. The effect of air velocity, grain layer thickness, number of trays and temperature on drying efficiency (ratio of energy used in removing moisture to sum of energy lost by drying air and that used for running fan) and moisture removal rate (ratio of mass of moisture removed to mass of wet grain per unit time) was investigated. The Taguchi approach was used to determine the optimal combination of drying air velocity, temperature and grain layer thickness that could be used to ensure greatest drying efficiency and Moisture Removal Rate (MRR). Analysis of Variance (ANOVA) and Least Square Differences (LSD) tests were used to determine whether change of air velocity and grain layer thicknesses significantly affected drying efficiency as well as MRR. The best fitting drying model for drying maize grain was selected and subsequently used to develop a computer simulation model for predicting drying time. On the basis of simulation results, number of trays and mass of grain to be dried per batch, the experimental grain dryer developed was of dimensions 0.5 m x 0.5 m x 1.0 m and was equipped with a 0.039 kW centrifugal fan. MRR was found to decrease with increase in grain layer thickness as long as air velocity was kept constant. For example, at 0.41 m/s air velocity, as grain layer thickness increased from 0.02 to 0.08 m, MRR decreased from 0.061 to 0.022 kg moisture / (kg wet grain. hour). Drying efficiency decreased with increase in drying air temperature where-as MRR increased with rise in air temperature as long as air velocity and layer thickness remained constant. For an air velocity of 0.41 m/s and 0.04 m grain layer thickness, drying efficiency was 23.5% at 40 °C and reduced to 10.1 % at 55 °C. On the other hand, MRR increased from 0.045 to 0.058 kg moisture / (kg wet grain. hour) over the same temperature range. It was found that when drying a given grain layer thickness, use of two trays did not significantly improve MRR as compared to the use of one. As a result of the optimisation process, it was also determined that when drying was done under laboratory conditions, a combination of 0.41 m/s air velocity, 45 °C air temperature and 0.02m layer thickness resulted in greatest MRR and drying efficiency. The drying model that best describes the drying curve was found to be the Midilli model. The optimal drying parameters, if applied by the user of the dryer, will result in optimal drying rate and drying efficiency, and this in turn will lead to reduced post-harvest grain loss. The computer simulation model developed will enable the farmer to plan drying schedules. Application of simulation to size the fan and dryer cabinet should be emulated by those who seek to size dryers. It is recommended that further study be carried out to determine the effect of grain porosity on dryer performance. Investigations should also be done to find ways of utilizing the warm exhaust air from the dryer.
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    Design Optimization of Municipal Solid Waste Incinerators Using Mathematical Modeling and Computer Simulation
    (Kenyatta University, 2020-07) Sarakikya, Halidini Hatibu
    Environmental pollution caused by the Municipal Solid Waste (MSW) incinerators has raised concerns about the quality of incinerators and the incineration process in Tanzania. Engineers and Scientists in general have appreciated that the installation of functional incinerators will increase the incineration process efficiency. Among the methods to achieve this is the application of mathematical modeling for the incineration process. Literatures have showed that related study in incinerators, incineration process and mathematical modeling in general has received little attention. The broad objective of this study was to optimize the design of municipal solid waste incinerators by using mathematical modeling and computer simulation. Computation Thermal Predictions (CTP) mathematical relations applying different types of incineration parameters including temperature, density, velocity and species concentration were formulated based on theories of incineration process with proper assumptions. The solution of the mathematical model developed was done and accomplished by computational fluid dynamics (CFD). Finite element method of numerical analysis was applied during the process to govern temperature and species concentration at different stack height of the model incinerator. Tests were performed on the physical model incinerator and data were analyzed after experiments. These data were applied in testing and verification of the mathematical models and provided the exact temperature and amount of flue gases which can be released from the stack without polluting the atmosphere. The results show that it is possible to forecast temperature and flue gases by the application of mathematical expressions. It also can be applied to develop more and accurate computational thermal predictions (CTP) model for the simulation of incineration process and experimentally regulate temperature and species concentration at different location of the incinerator. The results provided by the computational thermal predictions (CTP) were very close to those of experiments obtained from physical model. Therefore, there is an agreement between the empirical model and experiment as they show true trend of the incineration process.