Dedan Kimathi University of Technology
Nyeri, Kenya
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Kariuki Z.,Jomo Kenyatta University of Agriculture and Technology | Kariuki Z.,Dedan Kimathi University of Technology | Kiptoo J.,Jomo Kenyatta University of Agriculture and Technology | Onyancha D.,Dedan Kimathi University of Technology
South African Journal of Chemical Engineering | Year: 2017

This study points out potential of rogers mushroom (Lepiota hystrix) biomass in biosorption of copper and lead from aqueous solutions. The efficiency of biosorption was tested in batch experiments and the metal ion concentration analyzed using flame atomic absorption spectrometry. The analysis of FTIR spectrum reveals that the metal ions uptake by roger mushroom involves interaction of metal ion and hydroxyl, carboxyl and carbonyl groups of the biomass at optimum pH of 4.5–6.0 and sorbent mass of 1.5–2.1 g for Cu and Pb, respectively. Adsorption capacities were found to be 3.9 and 8.9 mg/g at a contact time of 25–40 min and initial metal ion concentration of 300–500 μg/g for Pb and Cu, respectively. The biosorption process follows second order kinetics and fitted the Langmuir isotherm model. The result shows that rogers mushroom biomass has a good potential to be used in removal of metal ions and can be used up to three adsorption/desorption cycles without losing efficiency. Its use in real life situation can alleviate pollution and increase the quality of water for human consumption and sanitary purposes. © 2017 The Authors

Irungu S.N.,Dedan Kimathi University of Technology | Muchiri P.,Dedan Kimathi University of Technology | Byiringiro J.B.,Dedan Kimathi University of Technology
Energy Science and Engineering | Year: 2017

The cement production process is energy intensive both in terms of the thermal energy (firing the kiln, drying and De carbonation) and electrical energy for driving the numerous drives within the process line. The average specific power consumption of the case study plant was 111 kWh/ton of cement with an average peak demand of 9.7 MW. The high cost of electric power at 0.14 USD/kWh results in very high cost of production that significantly lowers the company's profit margin and limits its competitive advantage. The generation of electrical power from waste heat recovery would reduce the electricity power bill through partially substituting the power procured from the national grid. This research evaluated the potential that the plant has for generating electrical power from the hot waste gases vented into the atmosphere and it was found that the plant has the potential to generate 3.4 MWh of electrical power. This results to a net potential to generate 2.89 MWh of electrical power after factoring in the auxiliary power consumption by Waste heat recovery plant system at 15%. This ultimately gave a reduction of 33% in the electricity power bill of the case study plant. The paper recommends the installation of a steam rankine cycle for the power generating plant. In this work the authors designed the steam boilers for the waste heat recovery plant for conversion of thermal energy to electrical energy, selected a commercial steam turbine and evaluated its economic feasibility and established that the designed plant would have a simple payback period of 2.7 years. © 2017 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.

Nyongesa H.O.,University of the Western Cape | Musumba G.W.,Dedan Kimathi University of Technology | Chileshe N.,University of South Australia
Architectural Engineering and Design Management | Year: 2017

To exploit market opportunities, disparate enterprises can pool their core competencies to form a temporary organisation. This inter-organisational collaboration of enterprises is commonly referred to as a virtual enterprise (VE). However, the VEs’ partner selection problems influence their overall performance. Due to the limited construction-specific studies on the evaluation and selection of partners, the current research sought to address this knowledge gap by investigating the partner selection problem. The partners comprising 10 construction companies carrying out project tasks for a large building in Nairobi, Kenya, were evaluated. Data were collected using a sequential mixed-methods approach consisting of a focus group interview, questionnaires and algorithms. The study proposed the use of the partner selection and performance evaluation technique. This technique combines fuzzy approximate reasoning with the conventional analytic hierarchy process algorithm, designed to deal with imprecise evaluators’ judgments. A multi-agent systems approach was chosen to simulate VEs. Results show that the chosen techniques are both efficient and effective. In particular, the reduced group fuzzy analytic hierarchy process reduces the number of pairwise comparisons required when comparing a large number of attributes. Application to the construction management domain, in particular, is germane to the research. © 2017 Informa UK Limited, trading as Taylor & Francis Group

Hailu Z.,Pan African University | Langat K.,Jomo Kenyatta University of Agriculture and Technology | Maina C.,Dedan Kimathi University of Technology
Journal of Communications | Year: 2017

Different variants of optical orthogonal frequency divisions multiplexing (O-OFDM) have been proposed for intensity modulation/direct detection (IM/DD) based indoor optical wireless communication. Among those schemes, direct current biased optical OFDM (DCO-OFDM) and asymmetrically clipped optical OFDM (ACO-OFDM) are widely adopted variants. These schemes have either reduced spectral or power efficiencies, but the co-existence of both good spectral and power efficiency is vital to tackle the challenge of limited bandwidth and limited optical power constraint scenarios. In this paper, simultaneous transmission of Multiple ACO-OFDM frames on both even and odd subcarriers has been proposed to achieve better spectral and energy efficiencies. The analysis of the theoretical bit error rate (BER) performance of the proposed scheme has been done and compared with the result from Monte Carlo simulation for Additive white Gaussian noise (AWGN) channel environment. A good agreement has been achieved between the theoretical BER bound and the simulated BER from Monte Carlo simulation. The proposed scheme provides better spectral efficiency (SE) compared to ACO-OFDM and equivalent SE compared to DCO-OFDM. In addition, it has shown superior energy efficiency performance than both ACO-OFDM and DCO-OFDM of equal SE. © 2017 Journal of Communications.

Zamankhan P.,Dedan Kimathi University of Technology | Zamankhan P.,University of KwaZulu - Natal
Mathematical Problems in Engineering | Year: 2015

The air-water mixture from an artificially aerated spillway flowing down to a canyon may cause serious erosion and damage to both the spillway surface and the environment. The location of an aerator, its geometry, and the aeration flow rate are important factors in the design of an environmentally friendly high-energy spillway. In this work, an analysis of the problem based on physical and computational fluid dynamics (CFD) modeling is presented. The numerical modeling used was a large eddy simulation technique (LES) combined with a discrete element method. Three-dimensional simulations of a spillway were performed on a graphics processing unit (GPU). The result of this analysis in the form of design suggestions may help diminishing the hazards associated with cavitation. © 2015 Piroz Zamankhan.

Wa Maina C.,Dedan Kimathi University of Technology
IEEE AFRICON Conference | Year: 2015

Biodiversity monitoring is important in assessing the state of an ecosystem and determining if conservation actions are required. This is particularly important when conservation resources are scarce. However, traditional methods of biodiversity monitoring are labour intensive and cannot be applied in every ecosystem where there is need. In order to expand the application of biodiversity monitoring, there is need to automate this important task. In this work we present an application of audio diarization methods for biodiversity monitoring and show how these methods can be used to measure the abundance of indicator taxa in areas of interest. The use of audio recordings has the potential to reduce the time and effort spent in biodiversity monitoring. The experiments are performed on a freely available dataset of bird song recordings with the birds serving as indicator taxa in the ecosystem of interest. We are able to estimate the number of bird species in the recordings and this information can be used to estimate the species richness in an ecosystem. © 2015 IEEE.

Wa Maina C.,Dedan Kimathi University of Technology | Muhia A.,Jomo Kenyatta University of Agriculture and Technology | Opondo J.,Dedan Kimathi University of Technology
2016 IST-Africa Conference, IST-Africa 2016 | Year: 2016

This paper presents low cost laboratories that aim to enhance the teaching of electrical and electronic engineering. The laboratories have been designed and developed based on the Raspberry Pi microprocessor system and are aimed at exposing students to the integration of software and hardware in electrical engineering in addition to ensuring that students appreciate the theoretical foundations of areas such as digital signal processing and instrumentation. The paper describes the laboratory exercises developed. Feedback from students was collected and analysed. The positive response shows the effectiveness of this approach in teaching electrical and electronic engineering. © 2016 IIMC.

Maina C.W.,Dedan Kimathi University of Technology
2015 IST-Africa Conference, IST-Africa 2015 | Year: 2015

Kenya's rich biodiversity faces a number of threats including human encroachment, poaching and climate change. Since Kenya is a developing country, there is need to manage the sometimes competing interests of development, such as infrastructure development, and conservation. To achieve this, tools to effectively monitor the state of Kenya's various ecosystems are essential. In this paper we propose a biodiversity monitoring software tool that integrates acoustic indices of biodiversity, recognition of species of interest based on their vocalizations and acoustic census. This tool can be used by non-experts to determine the current state of their ecosystems by monitoring the state of bird species that serve as indicator taxa and whose abundance is related to the abundance of other terrestrial vertebrates including the 'big five'. The tool we propose exploits state-of-the art advances in signal processing and machine learning to perform biodiversity monitoring, bird species detection and census in a joint framework. Using publicly available data we demonstrate how current acoustic indices of biodiversity can be improved by incorporating machine learning based audio segmentation algorithms. We also show how open source toolkits can be used to build bird species recognition systems. Code to reproduce the experiments in this paper is available on Github at © 2015 IIMC International Information Management Corporation Ltd.

Kiplangat D.C.,Kerala University | Kiplangat D.C.,Dedan Kimathi University of Technology | Asokan K.,College of Engineering, Trivandrum | Kumar K.S.,Kerala University
Renewable Energy | Year: 2016

Simple linear methods are widely used for time series modelling and prediction and in particular for the forecast of wind speed variations. Linear prediction models are popular for their simplicity and computational efficiency, but their prediction accuracy generally deteriorates beyond a few time steps. In this paper we demonstrate that the prediction accuracy of simple auto-regressive (AR) models can be significantly improved, by as much as 60.15% for day-ahead predictions and up to 18.25% for week-ahead predictions, when combined with suitable time series decomposition. The comparison with new reference forecast model (NRFM) also shows similar accuracy gain of week ahead predictions. The combined model is capable of forecasting wind speed up to 7 days ahead with an average root mean square error less than 3 m/s. We also compare the performance of AR and f-ARIMA models in wind speed prediction and observe that the f-ARIMA model is no better than the AR model when used in combination with time series decomposition. © 2016 Elsevier Ltd.

Muchiri P.N.,Dedan Kimathi University of Technology | Pintelon L.,Center for Industrial Management | Martin H.,Open University Nederland | Chemweno P.,Center for Industrial Management
International Journal of Production Research | Year: 2014

Equipment maintenance and system reliability are important factors affecting the organisations ability to provide quality and timely services to customer. While maintenance remains an important function to manufacturing, it is only recently that attempts have been made to quantify its impact on equipment performance. In this research, an approach of linking maintenance with equipment performance is developed using simulation modelling. The modelling approach involves defining probabilistic models and assumptions affecting system performance, such as: the probabilistic model for the initial failure rate/intensity of the equipment; the probabilistic model for the system deterioration and corresponding effect; the probabilistic model for the random times of corrective maintenance (CM) and preventive maintenance (PM) that takes into the account the types of maintenance plans/policies and the potential dependency between CM and PM times; and the probabilistic model for the random effects of CM and PM on the reliability of the equipment. Using a continuous manufacturing equipment, the model is used to analyse the impact of deterioration, failures and maintenance (policies, timing and efficiency) on equipment performance. It is shown that modelling the effect maintenance provides a basis of evaluating maintenance efforts with the potential application in performance evaluation and decision support while investigating opportunities for manufacturing equipment performance improvement. © 2013 © 2013 Taylor & Francis.

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