School of Engineeringhttp://repository.dkut.ac.ke:8080/xmlui/handle/123456789/11222024-03-29T01:51:09Z2024-03-29T01:51:09ZSatellite-Based Analysis of 20-Year Trends in Water Levels and Land Cover Change in Key Kenyan LakesGichuhi, Ann W.Achieng, Kevin O.Adero, Nashon J.http://repository.dkut.ac.ke:8080/xmlui/handle/123456789/84502024-02-21T06:44:44Z2023-11-01T00:00:00ZSatellite-Based Analysis of 20-Year Trends in Water Levels and Land Cover Change in Key Kenyan Lakes
Gichuhi, Ann W.; Achieng, Kevin O.; Adero, Nashon J.
Lakes play a pivotal role in supporting biodiversity and serving as reservoirs of easily
accessible surface water resources, making them integral components of both the blue
economy and local livelihood systems. In recent times, the water levels of Kenyan lakes
have exhibited fluctuations, with a noteworthy upward trend observed over the past
two decades. This increase in water levels has led to shoreline flooding, displacing
communities residing in proximity to these lakes. The repercussions have been
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profound, encompassing human and animal casualties and a loss of biodiversity within
these regions. This study centers its focus on ten economically significant Kenyan lakes:
Baringo, Bogoria, Elementaita, Jipe, Magadi, Naivasha, Nakuru, Olbolosat, Turkana,
and Victoria. Leveraging geospatial data derived from satellite remote sensing and
hydrological information sourced from spaceborne platforms, the research employs
trend analysis techniques to scrutinize the temporal evolution of lake water levels and
to pinpoint their likely determinants. The study's findings reveal a substantial
transformation in lake water levels over the last decade, manifesting a distinct and
consistent upward trajectory during the study period. Climate change, intricately
connected to environmentally degrading human activities such as land clearance for
agriculture and infrastructure development, emerges as the primary catalyst behind
these fluctuations. The implications of these findings extend to various domains,
including integrated water resources management, environmental monitoring, and
property development within Kenya and the broader region. This study and its future
endeavors stand to gain from the recent advancements in space technologies for earth
observation, notably exemplified by Kenya's recently launched Taifa-1 earth
observation nanosatellite. These innovations facilitate enhanced spatial-temporal
monitoring capabilities, crucial for the sustainable management of natural resources.
In light of the study's outcomes, it is recommended that similar methodologies and data
sources be employed to establish a systematic and ongoing monitoring and assessment
framework for lake water levels. Such an approach holds the potential to inform
evidence-based policies and decisions, safeguarding critical natural resources and
ensuring their sustainable stewardship for generations to come.
2023-11-01T00:00:00ZA Python Script for The Homogenization of Nonlinear Properties of Three-Dimensional Metal Matrix Composites Using AbaqusWaithira, AllanSchnack, Eckarthttp://repository.dkut.ac.ke:8080/xmlui/handle/123456789/84302024-02-19T10:04:03Z2023-11-01T00:00:00ZA Python Script for The Homogenization of Nonlinear Properties of Three-Dimensional Metal Matrix Composites Using Abaqus
Waithira, Allan; Schnack, Eckart
Metal Matrix Composites, commonly known as MMCs, have gained popularity and
application in different engineering fields due to their good combination of mechanical
properties. However, the nonlinear behavior of MMCs under different conditions
makes it difficult to predict their mechanical behaviors. Notably, complete methods
that fulfil the general modeling requirements of MMCs have not been established yet.
This study presents a Python script for the homogenization of nonlinear properties of
the metal matrix Aluminum Silicon Carbide using ABAQUS finite element commercial
software. The script utilizes the Nonuniform Transformational Field Analysis method
(NTFA), which is a widely accepted technique for the homogenization of composite
materials on a three-dimensional metal matrix Representative Volume Element (RVE)
and predicts the effective properties under different Periodic Boundary Conditions
(PBC). NTFA is based on solving a set of partial differential equations using the finite
element method, and the solutions are used to calculate the effective properties of the
composite. The main advantage of the script is that it's automated and can be easily
modified and applied to different types of MMCs and complex geometries. The results
obtained from the study are investigated and found to be in good agreement with
experimental data and other existing numerical methods.
2023-11-01T00:00:00ZENVIROGUARD: Machine Learning-Powered Detection of Air-Polluting Vehicles in Kenyan roads using IoTMuthui, Benjamin MachariaMwangi, Kelvin MichukiNjoroge, Monica WanjikuMuguro, Josephhttp://repository.dkut.ac.ke:8080/xmlui/handle/123456789/84162024-02-16T09:39:56Z2023-11-01T00:00:00ZENVIROGUARD: Machine Learning-Powered Detection of Air-Polluting Vehicles in Kenyan roads using IoT
Muthui, Benjamin Macharia; Mwangi, Kelvin Michuki; Njoroge, Monica Wanjiku; Muguro, Joseph
This work delves into the urgent issue of air pollution caused by unroadworthy vehicles,
particularly in light of the growing concerns about climate change. In Kenya, UNEP
estimates that 90% of urban air pollution in rapidly growing cities like Nairobi comes
from motor vehicles. Through the application of cutting-edge IoT systems, our aim is
two-fold: first, to pinpoint these vehicles and, second, to mitigate their detrimental
environmental impacts. By seamlessly integrating advanced sensor technologies with
real-time monitoring, our proposed solution advocates for sustainable transportation
practices, ultimately leading to improved air quality and reduced greenhouse gas
emissions. The system compromises of; proximity sensors (PIR sensor and NDIR sensor),
ESP32 OV2640 board with built in microcontroller with a ttgo camera, WIFI module
and Bluetooth module, A GSM module and a trained machine learning model. The
motion sensor (PIR sensor) acts as a switch as once triggered it activates both the camera
and carbon monoxide sensor (NDIR sensor) which collect data and send it to a central
database. Need for implementation of machine learning is crucial based on a number of
reasons: Majority of the roads in third world countries are A2 roads and congestion is the norm
as there is high level emission within the small area allocated for the road. In addition, the roads
are at times occupied by other parties such as pedestrians, animals crossing the roads or even
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still, cyclists. Due to the challenges mentioned above, our proposal goes miles ahead by
incorporating k-means clustering CNN machine learning model for image processing. This will
enable differentiation between motor vehicles and non-motor vehicles. Our project is not
only justified but also crucial in the face of the substantial health and environmental
risks posed by air pollution. Traditional inspection methods have proven inadequate,
necessitating the integration of IoT technology for a more robust approach. By utilizing
IoT devices and image processing, we're enabling continuous monitoring of vehicle
emissions and roadworthiness, leading to timely intervention measures.
2023-11-01T00:00:00ZIntegration of Solar Energy into Milk ProcessingMuriithi, SylviaMugambi, BenjaminKimari, Patrickhttp://repository.dkut.ac.ke:8080/xmlui/handle/123456789/84152024-02-16T08:59:05Z2023-11-01T00:00:00ZIntegration of Solar Energy into Milk Processing
Muriithi, Sylvia; Mugambi, Benjamin; Kimari, Patrick
Milk processing plants are huge consumers of power because of the process heating and
cooling required in the different processes e.g., pre-heating, pasteurizing, refrigeration
and other processes like bottle filling and sealing. Take pasteurizing, for example,
heating litres of water to 90℃ using Kenya Power grid-tied electricity is costly leading
to high operation costs. This poses a threat to the future of such industries. An
evaluation of the total energy consumed in the Kiwama milk processing plant, located
in Nanyuki, was done. For the pre-heating process of the milk, wood fuel and electricity
are used. The use of wood fuel may not be sustainable due to the current climate change
being experienced with one of the causes being deforestation. Other processes such as
pasteurizing and cooling use grid-tied electricity which cost them a monthly average
cost of KSh130,926 in 2021. To reduce energy costs, the installation of solar panels and
a solar water heating system was recommended. For the solar PV system, 24 solar
panels each rated 400 W would meet 57% of the facility’s power demand. This
translates to an average monthly savings of KSh74,181. For the solar water heating
system, two solar water heaters are required each with a hot water tank capacity of
a,b
200L and flat plate collectors of area 2.5m
. This will save the facility a monthly average
energy cost of KSh6,726 and eliminate reliance on wood fuel. This way the facility will
also be contributing to Kenya’s Vision 2030 goal of having zero carbon emissions.
2023-11-01T00:00:00Z