Faculty of Engineering and Technology

Permanent URI for this collectionhttp://172.16.31.117:4000/handle/123456789/32

Browse

Recent Submissions

Now showing 1 - 4 of 4
  • No Thumbnail Available
    Item
    Simulation and Optimisation of Process Parameters in Experimental Vertical Pneumatic Maize Grain Dryer
    (Egerton University, 2023-08) Korir, Meshack Kipruto
    Maize plays a critical role as a staple food and income source in Kenya, yet a significant annual loss of 12% to 20% of the national output occurs due to high moisture content. To mitigate this, drying maize to a safe moisture level of 13.5% (dry basis) before storage is essential. However, drying processes are energy intensive, consuming about 60% of the total invested energy. This emphasizes the need for appropriate technology which is the vertical pneumatic maize grain dryer (PMGD). The objectives of this research were to validate simulation models for mass flow rate (MFR) of maize grain, determine the effect of moisture content (MC), air temperature (Ta), and MFR on moisture removal rate (MRR) and energy used (EU) in drying, and optimise energy proportioned for the grain drying (Ea) and transportation (Eg) to maximise MRR. Furthermore, optimise MC, Ta, and MFR to enhance MRR and minimise EU through Taguchi's method. The Beverloo (BEV), British Code of Practice (BCP), Tudor (TUD), and New simulation model (QN) were validated using actual MFR data obtained from maize grain flow through horizontal circular orifices of diameters ranging from 0.040 m to 0.056 m. The experimental conditions included MC levels of 20%, 25%, and 30% (wet basis), Ta of 60°C, 70°C, and 80°C, and MFR of 720 kg/h, 771 kg/h, and 864 kg/h, while maintaining an air MFR of 547 kg/h during 2 hours drying period for 70.0 kg of the grain. The actual MFR ranged from 720 kg/h to 1735 kg/h, 650 kg/h to 2006 kg/h for BEV, 851 kg/h to 2378 kg/h for BCP, 867 kg/h to 2010 kg/h for TUD and 706 kg/h to 1757 kg/h for QN model. The Student’s t-test results showed significant difference (P < 0.05) between the actual and models MFR except QN (P > 0.05). The effect of MC on MRR was significant (P < 0.05). However, MC did not have significant (P > 0.05) effect on Ea and Eg. The effect of Ta on MRR and Ea was significant (P < 0.05) except Eg (P > 0.05). The effect of MFR on MRR, Ea and Eg was not significant (P > 0.05). The optimum Ea and Eg for MRR were 7.3 kWh and 2.2 kWh, respectively. Additionally, the optimum MC, Ta and MFR for MRR were 20%, 80°C and 720 kg/h while that for EU was 20%, 60°C and 720 kg/h, respectively. The Page model with coefficient of determination of 0.99 and root mean square error of 0.0049 was suitable for describing variation of moisture ratio with time in maize grain drying. The availability and use of the optimised PMGD would provide applicable solutions to energy challenges in maize grain drying, ultimately leading to reduced postharvest losses and enhanced food security and income for farmers. This would contribute to the attainment of sustainable development goals, particularly in eradicating hunger and poverty.
  • No Thumbnail Available
    Item
    Modeling and Evaluation of Tractor Wheel Traffic Compaction Effect on Subsoiling Draft in the Agricultural Farm of Egerton University, Kenya
    (Egerton University, 2023-10) Abich, Samuel Otieno
    Soil performance is vital for the survival of human civilizations since it ensures provision of food for the human population. Soil compaction has impacted about 45% of agricultural soil and degraded an estimated 83 Mha of agriculture therefore reducing agricultural productivity. The objective of this study was to model and evaluate the compaction effect of tractor wheel traffic on sub soiling draft for soils in the agricultural farm of Egerton University, Kenya. Tractor wheel traffic experiments were conducted on the selected plots with varying levels of compaction. A dynamometer attached to the subsoiling equipment was used to measure the draft requirements during subsoiling. Soil samples were collected at various depths before and after tractor wheel passes and analyzed for physical properties (bulk density, moisture content, porosity, infiltration rate and saturated hydraulic conductivity) and mechanical properties (penetration resistance, cohesion, angle of internal friction and shear strength) were determined. The study employed a factorial experiment with a Completely Random Block design to look at the effects of five wheel passes (1, 2, 3, 4, 5) on soil properties at depths of 0 - 20, 20 - 30, and 30 - 40 cm with three replications. The wheel passes were equivalent to vertical loads of 26, 51, 77, 102 and 128 kN respectively. Increasing the number of wheel passes and the depth caused a significant increase in the soil's bulk density (from 1256 to 1593 kg m-3), penetration resistance (642 to 1539 kPa), strength (121.20 to 156.97 kPa), internal angle of friction (29 to 35o), and cohesion (6.84 to 8.42 kPa), while decreasing the moisture content (from 41 to 33%), infiltration rate (15.30 to 3.35 mm h-1), porosity (34 to 5%) and saturated hydraulic conductivity (5.63 to 0.54 to mm h-1). The draft requirement for subsoiling increased from 1.40 kN for the no pass at the 0 - 20 cm depth to 10.68 kN for five wheel passes in the 30 – 40 cm depth. Subsoiling draft was modeled as a function of soil depth, bulk density, penetration resistance, shear strength, angle of internal friction and cohesion. The R2 for Multiple Linear Regression (MLR), Dimensional Analysis, Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) were 0.9838, 0.8722, 0.9999 and 0.9941 respectively. The t-test revealed that there was no significand difference between the measured and predicted draft for the MLR (t = 0.13), Dimensional Analysis (t = 0.15), the ANN (t = 0.12) and ANFIS (t = 0.19) models. Conclusions of this study emphasize the significant influence of tractor wheel passes on soil properties and draft requirements. Recommendations include the promotion of optimized subsoiling strategies. This research contributes to promoting sustainable agricultural practices and soil management strategies.
  • No Thumbnail Available
    Item
    Simulation and Optimisation of Process Parameters in Experimental Vertical Pneumatic Maize Grain Dryer
    (Egerton University, 2023-08) Korir, Meshack Kipruto
    Maize plays a critical role as a staple food and income source in Kenya, yet a significant annual loss of 12% to 20% of the national output occurs due to high moisture content. To mitigate this, drying maize to a safe moisture level of 13.5% (dry basis) before storage is essential. However, drying processes are energy intensive, consuming about 60% of the total invested energy. This emphasizes the need for appropriate technology which is the vertical pneumatic maize grain dryer (PMGD). The objectives of this research were to validate simulation models for mass flow rate (MFR) of maize grain, determine the effect of moisture content (MC), air temperature (Ta), and MFR on moisture removal rate (MRR) and energy used (EU) in drying, and optimise energy proportioned for the grain drying (Ea) and transportation (Eg) to maximise MRR. Furthermore, optimise MC, Ta, and MFR to enhance MRR and minimise EU through Taguchi's method. The Beverloo (BEV), British Code of Practice (BCP), Tudor (TUD), and New simulation model (QN) were validated using actual MFR data obtained from maize grain flow through horizontal circular orifices of diameters ranging from 0.040 m to 0.056 m. The experimental conditions included MC levels of 20%, 25%, and 30% (wet basis), Ta of 60°C, 70°C, and 80°C, and MFR of 720 kg/h, 771 kg/h, and 864 kg/h, while maintaining an air MFR of 547 kg/h during 2 hours drying period for 70.0 kg of the grain. The actual MFR ranged from 720 kg/h to 1735 kg/h, 650 kg/h to 2006 kg/h for BEV, 851 kg/h to 2378 kg/h for BCP, 867 kg/h to 2010 kg/h for TUD and 706 kg/h to 1757 kg/h for QN model. The Student’s t-test results showed significant difference (P < 0.05) between the actual and models MFR except QN (P > 0.05). The effect of MC on MRR was significant (P < 0.05). However, MC did not have significant (P > 0.05) effect on Ea and Eg. The effect of Ta on MRR and Ea was significant (P < 0.05) except Eg (P > 0.05). The effect of MFR on MRR, Ea and Eg was not significant (P > 0.05). The optimum Ea and Eg for MRR were 7.3 kWh and 2.2 kWh, respectively. Additionally, the optimum MC, Ta and MFR for MRR were 20%, 80°C and 720 kg/h while that for EU was 20%, 60°C and 720 kg/h, respectively. The Page model with coefficient of determination of 0.99 and root mean square error of 0.0049 was suitable for describing variation of moisture ratio with time in maize grain drying. The availability and use of the optimised PMGD would provide applicable solutions to energy challenges in maize grain drying, ultimately leading to reduced postharvest losses and enhanced food security and income for farmers. This would contribute to the attainment of sustainable development goals, particularly in eradicating hunger and poverty.
  • No Thumbnail Available
    Item
    Seasonal Effect of Climate Variability on Soil Moisture and Biomass Production: a Case Study of Maasai Mara Rangeland and Naivasha Cropland Ecosystems, Kenya
    (Egerton University, 2022-03) Kapkwang, Charles Chepkewel
    Globally, rangeland and cropland are ecosystems that rely exclusively on soil moisture influenced by dynamic interaction of eco-hydrologic variables caused by climate variability and soil for sustainable biomass/yield production. There are challenges of up to date variable measurements within adequate information of spatio-temporal soil moisture variation and biomass in the selected Kenyan ecosystems. The specific objectives under the study were the determination of spatio-temporal soil moisture storage and retention capacities in the ecosystems; Simulation of the influence of bi-seasonal soil moisture variability on rangeland and cropland biomass yield using coupled Hydrus-1D and Agricultural Production Systems Simulator. Finally, to analyse the impact of bi-seasonal soil moisture variation on land use land cover in rangeland and cropland vegetation. Remote sensing and Geographical Information System derived land use land cover classification maps from Normalized Difference Vegetation Index values for real-time monitoring were obtained and processed via MODIS and Proba-V imagery satellite data. Random undisturbed core soil samples collected from ten (10) sampling points with five varying replication depths of P1 (0-5cm), P2 (5- 10cm), P3 (15-20cm), P4 (35-40cm) and P5 (75-80cm) for ground based (in-situ) installed 5TM-ECH2O probes. Time series variation shows that volumetric water content of spatially distributed probes in wet season ranged between 0.11 and 0.32m3m-3 (0.16m3m-3) and in dry between 0.04 and 0.17m3m-3(0.11m3m-3) across the rangeland respectively. Cropland volumetric water content in wet season ranged between 0.13 to 0.37m3/m3 (0.22m3m-3) and dry between 0.06 to 0.22m3m-3(0.14m3m-3) respectively. Water retention shown that field capacity of soil water content at -3 bars ranged between 0.16cm3H2O/cm3soil and 0.22cm3H2O/cm3soil across the rangeland. APSIM model simulated cropland and rangeland above ground biomass reasonably well, where rangeland model performance gave NSE = 0.988, r = 0.000, RMSE = 0.103tonha-1 and R2 was 0.988. In overall, the rangeland covers approximately 717.203km2(46.75%) with total above ground grass biomass in dry and wet season of 35.094 tonha-1( 2,516,952.208 and 42.123 tonha-1 ( 3,021,074.197) tonnes per season respectively. Land use land cover change indicates gradual encroachment of livestock and commercial wheat farms into the grassland in the last decade (2009-2019). This has decreased (closed, evergreen broadleaved) forest cover while conversion of Naivasha cropland from rain fed to irrigated cropland is also gradually increasing. In conclusion, soil moisture, biomass and change in land use land cover vary seasonally as influenced by climate variability.