Browsing by Author "Assefa, Berhanu (PhD)"
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Item Analysis of Fillers for Production of Alternative Building Materials Using Magnesia Cement(Addis Ababa University, 2008-01) Tadele, Tamrat; Assefa, Berhanu (PhD)Fillers, which are available in the country, are essential for the production of magnesia cement boards. These include pumice and lignocellulostic fillers such as bagasse, sawdust and coffee husk. While applying these fillers to produce the boards, their nature and performance should be studied. Magnesia cement was used as binding materials; varying boards were produced for different proportion fillers with fixed ratio of magnesium oxide and magnesium chloride. For the board produced, density, water absorption, and setting time as well as mechanical properties such as the compressive strength and the bending strength determined. In addition, the effects of different fillers on the properties of the board and production cost were examined. When the saw dust and coffee husk filler substituted the current used bagasse filler, the water absorption and setting time of the boards reduced. While the density, bending strength and compressive strength of the board increased. The mix ratio of fillers and pumice powder had also influence on the physical and mechanical property of the boards. The test results showed as the proportion of the pumice increased, the mechanical strength of the boards increased and the setting time of the cement paste reduced. The cost analysis showed that the saw dust board production cost was lower than coffee husk and bagasse board cost. While the production cost of the coffee husk board was slightly higher than others. viii In aim of production of filler boards as alternative building materials are, the cost comparison showed in the possibility of producing relatively low price than the hollow concrete blocks included finishing work cost.Item Characterization of Physicochemical Parameters for Tap Water and Removal of Hardness Using Moringa Stenopetala seed as Natural Absorbent The Case of Mekelle Town, Tigray Ethiopia(Addis Ababa University, 2007-06) Mesfin, Amhagiyorgis; Assefa, Berhanu (PhD)The physicochemical examination of tap water used for domestic purposes in Mekelle town was carried out to ascertain their suitability for consumption. Water softening experiments were also conducted to observe the changes in total hardness, with varying dosages of a natural coagulant. The natural coagulant was extracted from Moringa stenopetala seed A total of twenty (20) water samples were collected from various parts of the town tap water used for domestic purposes and characterized for their physicochemical parameters, arising public interest. The physicochemical implications render Mekelle’s tap water unfit for human consumption, though it can be used for other purposes. Tap water samples containing high concentration of hardness from Mekelle town, Enda Mariam and Enda Giyorgis areas were used for hardness removal mechanism part of this study. The optimum hardness removal efficiency for Mekelle tap water sample produced from ground water source was approximately 58 % (from initial total hardness of 523.25 to final hardness result of 220.3 which is within Ethiopian and WHO standard, i.e. below 300 mg/l as CaCO3) which was attained at M.Stenopetala dosage of 200 mg/l. The optimum hardness removal efficiency for synthetic hard water analysis done by taking two factors, coagulant dose concentration and PH as independent factors was approximately 49 % (from 500 to 256.6 mg/l as CaCO3) which was attained at M.Stenopetala dosage of 200 mg/l and 6.5 PH value. The mechanism for hardness removal in hard water seems to be precipitation of insoluble products of the reaction between M.Stenopetala extract and the hardness causing ions. Even at a relatively higher dosage of the M.Stenopetala coagulant compared to the chemical softening, natural coagulant is preferred for economic use, health and environmental safety. Key words: Water Hardness, coagulant protein, Moringa Stenopetala, physicochemical parameters, Tap waterItem Effect of adding urea on biogas production potentials of selected fruit wastes in Addis Ababa, Ethiopia(Addis Ababa University, 2012-10) Dagnew, Getachew; Assefa, Berhanu (PhD)Fruit wastes are ideal candidates for anaerobic digestion because they contain high levels of easily biodegradable materials. These wastes from the central and biggest fruits, fish & vegetables retail and distribution market in Addis Ababa City, Ethiopia are poorly managed. Again problems such as low biogas/methane yield and process instability are often encountered in anaerobic digestion of these wastes, challenging its reliability and efficiency. This study evaluated the effect of adding urea on biogas (methane) potential of selected fruit wastes following characterization. A laboratory scale experiment on batch anaerobic mesophilic digestion was carried out. Selected and pretreated fruit wastes were fed to digesters using standard procedures. Analytical equipments, simple tools and statistical software were used for data analysis. The ultimate biogas yield from using avocado, banana, and mango fruit wastes as substrate is; 0.48, 0.57, 0.53 l/g VS without adding urea and 0.76, 0.82, 0.82 l/g VS adding urea with a statistically significant difference (p-value; 0.006 for avocado, 0.029 for banana, and 0.007 for mango FW at 95% Confidence Interval respectively). Thus urea addition significantly improved biogas yield. In relation to this, ultimate CH4 yield didn’t show difference in response to the treatment and gave 0.27, 0.33, 0.27 l/g VS without adding urea and 0.44, 0.46, 0.43 l/g VS for avocado, banana, & mango fruit wastes with adding urea where the later yield is above average yield reported for fruits and vegetables wastes. Further, the biogas manure contains nutrients in their useful form and better than the row waste for plant growth signifying the advantages of anaerobic digestion. Prediction of biogas and biogas manure obtainable from the city’s fruit wastes is also made. Keywords: fruit waste; urea; biogas; methane; volatile acids; and plant nutrientItem Modeling Chemical Engineering Processes Using Artificial Neural Networks(Addis Ababa University, 2005-01) Ambaw, Alemayehu; Assefa, Berhanu (PhD); Tefera, Nurelegne (PhD)In this thesis the application of feed forward type artificial neural networks to model chemical engineering processes are demonstrated with reference to five different problems. Neural network models are constructed and employed to predict vapor-liquid equilibrium (VLE) data of twelve different binary systems having different chemical structures and solution types (azeotrope-nonazeotrope) in various conditions (isothermal or isobaric). It is observed that the data found by neural network model gives an excellent agreement with the experimental data. In fact the neural network model can be treated as a powerful means for VLE data prediction in a fast and reliable way. This study has confirmed the feasibility of using a neural network to capture the nonlinear and interacting relationships between the moisture content and different drying conditions of potato. Simulating time series temperature profiles of adiabatic batch reactor has also investigated. Neural network trained with a limited number of experimental data were capable of predicting fresh data that were not used to train the network. The results obtained in using the developed models are physically sound as expected from experience. Simulating a human operator controlling a chemical plant is also a good instance where the advantage of using artificial neural networks is demonstrated in the thesis. This thesis also describes the use of multilayer feed forward neural networks as a CO2 analyzer. It was proved that MLP-type network of a relatively simple structure made it possible to predict the CO2 effluent from a furnace. Taking in to account the difficulties in experimental conditions, complicated measurements and unavoidable errors of devices used, limit the precision of laboratory measurement results. The accuracy of the results generated by the developed neural network models may be considered satisfactory for engineering calculations.Item Neural Network predictive process modeling: Application to food processing(Addis Ababa University, 2009-03) Agide, Mesfin; Assefa, Berhanu (PhD)Currently, food processing industry is driven by several requirements. This requirement includes ensuring safety, meeting quality standard and customer expectation and reducing production cost to be competent in market. To achieve this requirement they have to operate at optimum process conditions all the time. In food processing, due to the nature of the process, it is difficult to find and operate at the best conditions solely by experience. The Ethiopia food industry is no coping up with such requirement due cost of optimization and low level of education of works operating in the production system. Thus, it is necessary modeling of the process or part of the process to capture the relation of between important process parameters and use the model to control and improve the process better. In addition, it is found necessary to make the model accessible for the operators working in Ethiopian industry. Using artificial neural network method is found to be very good modeling to tool to solve food engineering problems. In this thesis, therefore, artificial neural network method is used to model and tested for selected food industry engineering problems, specifically, water activity prediction, predictive food microbiology and control chart pattern recognition. The model is enclosed in interactive software so that it could also be used by people that do not have sophisticated mathematical and technical skills. The result obtained for all problems shows that neural network modeling can be used to model food process and to predict food process parameters with sufficient accuracy.Item Performance Evaluation of Drinking Water Treatment Plant (case Study: Gambella town Drinking water Treatment Plant)(Addis Ababauniversity, 2013-01) G/Tsadik, Tariku; Assefa, Berhanu (PhD)The conventional water treatment plant, especially in developing countries, faces major challenges in terms of assessing its operation and performance due to inappropriate technologies, insufficient equipment and deficiency in skilled expertise. Simple but efficient technologies are therefore necessary for reasonable evaluation of the daily performance of the plant. Turbidity is thought of as a convenient surrogate to give favorable indication of the biological and physical quality of the treated water thus by extension provide a fair gauge of the performance of the treatment plant with respect to water purification. Besides, it is fairly simple to measure, cheap and can easily be understood by the operators. In this study the performance of Gambella town water treatment plant was assessed. The study was conducted by assessing unit process capability, design, operation and maintenance potential to meet optimized goals. From results of the assessments, root factors limiting optimum performance were identified and improvement options were proposed. Major unit processes were evaluated to project their design capabilities to meet current peak water demand by selecting appropriate loading rates as basis criteria. The results of the assessment found that all units had the capability to satisfactorily treat water at peak daily demand of 2000 m3/day. The study assessed turbidity performances of sedimentation and filtration units by setting optimized turbidity goals. The assessment results indicated that, settled water turbidity was measured less than 10 NTU. And filter turbidity spike of 6.5 NTU following backwash with a reduction to 0.6 NTU after one hour was observed. Generally optimized performance goals were not being achieved. This indicated high risk of microbial pathogens that could pass the filtration barriers in the finished water. Jar test experiments were conducted to evaluate the effectiveness of Aluminum Sulphate (recently used by the treatment plant), Ferric chloride and Ferric Sulphate by comparing the optimum dose at optimum pH for highest turbidity removal and relative costs. From the jar test results Aluminum Sulphate was found to be the effective chemical with 45 mg/l optimum dose at pH 7.1 and the treatment plant was recommended to continue using Aluminum Sulphate at the optimum dose for the raw water characteristics during the evaluation period. Treated water samples were collected from the clear-water well to test 14 water quality parameters according to the standard methods for water and waste water examinations. The collected samples were intended to show the x characteristics of the finished water only during the evaluation period. The samples were analyzed at the laboratory of GWTP and results were compared with WHO standards and guidelines for drinking water. Results of the analysis showed that all of the measured parameters were within the acceptable range. In the assessment of factors limiting performance of the treatment plant; major factors were categorized as design, operational and maintenance. No single factor was responsible for poor plant performance, although in general the study found that all factors influence the plant’s ability to work properly. Some of the primary operational problems and the intake structure’s adequacy significantly affected performance. Operational factors were found to have the highest rank. This finding, coupled with the fact that the plant had adequate capability, indicates that improving process control could significantly improve performanceItem Predictive Modelling of Kaliti Wastewater Treatment Plant Performance Using Artificial Neural Networks(Addis Ababa University, 2012-02) Sewnet, Getnet; Assefa, Berhanu (PhD)Artificial neural networks are a form of artificial intelligence that have the capability of learning, growing, and adapting within dynamic environments. A reliable model for any wastewater treatment plant is essential in order to provide a tool for predicting its performance and to form a basis for controlling the operation of the process. This would enable to assess the stability of environmental balance at minimized operational costs. Wastewater treatment process is complex and attains a high degree of nonlinearity due to the presence of bio-organic constituents that are difficult to model using mechanistic approaches. Predicting the plant operational parameters using conventional experimental techniques is also a time consuming step and is an obstacle in the way of efficient control of such processes. In this work, a soft computing approach based on back propagation artificial neural networks, which employed genetic algorithm and partial mutual information to perform input selection, was used to acquire the knowledge base of Kaliti wastewater treatment plant and has been applied for predicting and optimizing selected plant performance variables viz. pH, BOD5, COD, NH3, and TDS effluent concentration of the plant. In the model structure of the treatment plant performance, two different functional structures in the configuration of the network are constructed and compared. In the first configuration Multiple-Input-Single-Output (MISO), structures differing in the type of data used, raw operational data and outlier removed data in the input layer, are used to build models for each of the five performance indicators selected in this work. Partial Mutual Information-based Input Selection (PMIS) and Genetic Algorithm (GA) based input selection algorithms are applied for both above mentioned MISO configuration based models. In the second configuration Multiple-Input-Multiple-Output (MIMO), GA based input selection is applied for both the raw and outlier removed data. The model performance was evaluated with statistical parameters and the simulation results indicates that the MISO modelling approach achieves much more accurate predictions as compared with the MIMO modelling approach. Optimum model architecture of 14-43-1 for pH, 16-29-1 for BOD5, 14-50-1 for COD, 6-28-1 for NH3, and 10-48-1 for TDS were selected for predicting the performance of Kaliti wastewater treatment plant using MISO configuration. The linear correlation between predicted outputs and target outputs for the optimum model architecture described above are 0.97 for pH, 0.94 for BOD5, 0.98 for COD, 0.94 for NH3, and 0.98 for TDS. Keywords: Artificial neural networks; Back propagation; Genetic algorithm; Partial mutual information; Wastewater treatmentItem Predictive Modelling of Kaliti Wastewater Treatment Plant Performance Using Artificial Neural Networks(Addis Ababauniversity, 2012-02) Sewnet, Getnet; Assefa, Berhanu (PhD)Artificial neural networks are a form of artificial intelligence that have the capability of learning, growing, and adapting within dynamic environments. A reliable model for any wastewater treatment plant is essential in order to provide a tool for predicting its performance and to form a basis for controlling the operation of the process. This would enable to assess the stability of environmental balance at minimized operational costs. Wastewater treatment process is complex and attains a high degree of nonlinearity due to the presence of bio-organic constituents that are difficult to model using mechanistic approaches. Predicting the plant operational parameters using conventional experimental techniques is also a time consuming step and is an obstacle in the way of efficient control of such processes. In this work, a soft computing approach based on back propagation artificial neural networks, which employed genetic algorithm and partial mutual information to perform input selection, was used to acquire the knowledge base of Kaliti wastewater treatment plant and has been applied for predicting and optimizing selected plant performance variables viz. pH, BOD5, COD, NH3, and TDS effluent concentration of the plant. In the model structure of the treatment plant performance, two different functional structures in the configuration of the network are constructed and compared. In the first configuration Multiple-Input-Single-Output (MISO), structures differing in the type of data used, raw operational data and outlier removed data in the input layer, are used to build models for each of the five performance indicators selected in this work. Partial Mutual Information-based Input Selection (PMIS) and Genetic Algorithm (GA) based input selection algorithms are applied for both above mentioned MISO configuration based models. In the second configuration Multiple-Input-Multiple-Output (MIMO), GA based input selection is applied for both the raw and outlier removed data. The model performance was evaluated with statistical parameters and the simulation results indicates that the MISO modelling approach achieves much more accurate predictions as compared with the MIMO modelling approach. Optimum model architecture of 14-43-1 for pH, 16-29-1 for BOD5, 14-50-1 for COD, 6-28-1 for NH3, and 10-48-1 for TDS were selected for predicting the performance of Kaliti wastewater treatment plant using MISO configuration. The linear correlation between predicted outputs and target outputs for the optimum model architecture described above are 0.97 for pH, 0.94 for BOD5, 0.98 for COD, 0.94 for NH3, and 0.98 for TDS. Keywords: Artificial neural networks; Back propagation; Genetic algorithm; Partial mutual information; Wastewater treatmentItem Selection of Clay Adsorbents and Determination of The Optimum Condition for Defluoridation of Ground Water in Rift Valley Region(Addis Ababa University, 2007-01) Hassen, Ahmedin; Assefa, Berhanu (PhD); Tefera, Nurelegne (PhD)People in several regions of the Rift Valley of Ethiopia are suffering from skeletal and non-skeletal fluorosis as result of consuming water containing excessive fluoride. Defluoridation of drinking water using variety of material has been suggested by different researchers. This study assesses the fluoride adsorption characteristics of clays collected from different areas in Ethiopia. Bombawoha clay, Combolcha clay and Muger clay 2 were found to have the better potential as fluoride adsorbents. The effect of contact time, amount of adsorbent dose, pH, particle size, heat treatment of adsorbent and initial concentration of fluoride was investigated. The adsorption was rapid during the first one hour. The adsorption efficiency of fluoride was increased with adsorbent dosage. The defluoridation capacity was appreciable with in acidic pH range. Clay adsorbents treated in the range between 400 to 600oC gave better fluoride removal. The fluoride adsorption efficiency depends directly with initial fluoride concentration. The adsorption data were well fitted to the Langmuir isotherm model with adsorption capacity of 0.136, 0.168, and 0.191 for Bombawoha clay, Combolcha clay and Muger clay 2 respectively. Laboratory scale column were conducted and showed good removal of fluoride. At breakthrough the three clay samples Bombawoha clay, Combolcha clay and Muger clay 2 showed 0.1248, 0.235, 0.239 mg/g of adsorption capacity, 1.64, 1.1, 1.02 mg/L of residual fluoride and 300, 500, 500 mL of breakthrough volume respectively.