Browsing by Author "Muumbo, AM"

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    Analysis of raw iron ores in Kenya: case study of Mwingi north constituency in Kitui county
    (2014) Kiptarus, Joan J; Muumbo, AM; Makokha, AB
    Accurate prediction of downstream processing performance of a mineral ore is critical to resource evaluation and development decisions, where significant cost and productivity benefits can be realized through early determination of an ore’s properties and processing potential. This study aimed at investigating the chemical, qualitative and quantitative composition of raw iron ore from the deposits in Katse area (Mwingi North Constituency in Kitui County) which lies in the Mozambique mobile belt. The quality of the iron ore was evaluated to establish its suitability as a raw material for iron production in Kenya. Samples were obtained along the perimeter vertices and centre of a land parcel measuring 150m2 with each excavation being 50cm length, 50 cm width and 500cm depth. Detailed studies were conducted to establish the composition and properties of the 5 samples. X-ray Diffraction (XRD), X-ray Fluorescence (XRF) and Atomic Absorption Spectrometry (AAS) techniques were employed in the investigation. XRD studies revealed magnetite (above 86%) as the major mineral with subordinate amounts of hematite and quartz. XRF studies indicated a high content of iron (above 80%) with minor amounts of (5% Al2O3, < 5%TiO2, 1-44% SiO2, <1% of MnO, P2O5, SO3, K2O, V2O5). AAS experiment results indicated that the Iron content was above 90% with minor amounts of SiO2, Al2O3, CaO, MgO, Na2O, K2O, TiO2 and MnO elements. The quality of this ore was compared to generalized world market standards and ores from other nations. The results indicated that Katse ore is a rich Magnetite grade with Fe content above 80% with minor amounts of hematite, quartz and clay as the major gangue (<1% SiO2 and <1% Al2O3) and low contents of the deleterious elements ( <1 %), which correspond to acceptable levels for commercial iron ores.
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    E-Medical Consultation for Diagnosis and Treatment of Hyperten sion in Pregnancy: an Opportunity to achieve the 5 th Millennium Development Goal in Kenya
    (2012) Gudu, Jael; Gichoya, David; Muumbo, AM; Nyongesa, Paul
    This study sought to establ ish the challenges that the Reproductive Health Division (Hi gh Risk Pregnancy Clinic) at Moi Teaching and Referral Hospital based in Eldoret city, one of the two referral hospitals in Kenya, faces in adopting e-medical consultation as a way of impr oving maternal healthcare for patients with hypertension in pregnancy and reducing maternal deaths. In this paper, an outline of the strategies and pillars that the Clinic needs to adopt to embrace the use of e-medical consultation for the diagnosis, treatment and management of hypertension in pregnancy is pr esented. The survey conducted established that the division is still lagging behind and has not adopted the use of e-he alth, especially in the consultation sessions between the doctors and patients. The outlined strategies when implemented will help steer the Reproductive Health Division (High Risk Pregnancy Clinic) to wards making healthcare services available in an efficient and effective way to expectant women with the condition. The model could be adopted to extend the same to less endowed areas around the globe, where specialist: patient ratio is low.
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    Optimization of in-mill ball loading and slurry solids concentration in grinding of UG-2 ores: A statistical experimental design approach
    (2012) Makokha, AB; Moys, MH; Muumbo, AM; Kiprono, RJ
    The in-mill load volume and slurry solids concentration have significant influence on the ball mill product size and energy expenditure. Hence, better energy efficiency and quality grind can only be achieved with correct tuning of these influential operational factors to the desired optimum point. In view of the deficiencies of the classical “one-factor-at-a-time” methodology, statistical experimental design methodologies were applied in this study to optimize the slurry % solids and ball load volume during a batch ball milling process of UG-2 ore. The response surface methodology and central composite design were used to determine the best possible combination of ball load volume and slurry % solids for maximum size reduction index and minimum specific energy consumption (kW h/t). Second order response surface models were built to describe the relationship between the input factors and the response variables. Analysis of variance (ANOVA) tests and response surface plots were used to set the optimal level for each input factor. With compromise optimized values of 29% ball load volume and 75% slurry solids, the response surface models yielded specific energy consumption of 10.54 kW h/t and size reduction index of 3.93. Confirmatory experiments carried out in these optimized conditions resulted in specific energy consumption of 10.72 kW h/t and size reduction index of 3.91 thus corroborating the validity of the response surface models
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    Steady state inferential modeling of temperature and pressure in an air-swept coal pulverizing ball mill
    (Elsevier, 2009) Makokha, AB; Moys, Michael H; Couvas, Costa; Muumbo, AM
    Mill discharge temperature and differential pressure have a strong effect on efficiency and safety of a coal fired power plant. Therefore, it is imperative that they are closely monitored and controlled during mill operation to keep their levels within a predetermined safe and efficient operating range regardless of the rate at which the raw coal is fed to the mill. One way to achieve this is through a control schedule that compares the value obtained from the process to the stored set-point value to determine if there is any deviation that requires correction. This paper describes a steady state model that could be used alongside conventional controllers as an on-line shadow that provides inferential estimates of desired temperatures and pressure drops in the mill circuit which can be continuously compared with the actual values for adjustment. This would not only help to avoid the difficulties associated with direct measurement but also provide a means for early detection of drifts and failing sensors and serve as a temporal back-up for the out-of-order sensors. The model was tested using industrial data collected from four ball mills at a coal fired power plant in South Africa and the results show a reasonable agreement between the measured data and model predictions both qualitatively and quantitatively within a 5% error margin. The model outputs were found to be highly sensitive to the variation in mill loading, the primary air (PA) flow and the mill channel dimensions. Therefore, for validity of this model, accurate determination of all significant parameters is essential. For now, the model is only valid for the ball mills involved in the current study, but with availability of data it can be reproduced elsewhere.