Modelling and Simulation to Predict Partial Mobile Velocity Effect on Clostridium Transport in Heterogeneous Lateritic and Silty Formation in Ahoada West, Rivers State of Nigeria
Issue:
Volume 2, Issue 2, June 2017
Pages:
16-24
Received:
2 October 2016
Accepted:
13 May 2017
Published:
12 July 2017
DOI:
10.11648/j.jeece.20170202.11
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Abstract: This paper is to predict the behaviour of clostridium transport in heterogeneous lithostratification depositions. Heterogeneity of the concentration were also monitored, fluctuation were observed base on the pressure from several phase considered in the system to monitor the transport process of these type of microbial specie. The depositions of this contaminant were observed to experiences lots of vacillations base on the deposition of substrate in the strata through their migration process. Partial mobile velocity was also observed in the system in most structured deposition of the formation thus affect the transport by developing accumulation of the contaminant in some deposited strata. These were observed to reflect on degree of porosity pressure including stratification variation experiences in the study area. simulation were carried out for validation of the derived model for the study, theoretical values generated were compared with experimental data, and both parameters developed best fits expressing validation of the model. The study is imperative because it has reflected in the behaviour of the clostridium under the influences of micronutrient, this affect its rate of concentration on ground water quality, experts in the field will definitely find these model useful in monitoring and investigation of ground water quality in deltaic environment.
Abstract: This paper is to predict the behaviour of clostridium transport in heterogeneous lithostratification depositions. Heterogeneity of the concentration were also monitored, fluctuation were observed base on the pressure from several phase considered in the system to monitor the transport process of these type of microbial specie. The depositions of th...
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NOX Emission Reduction by Non Thermal Plasma Technique
Nasser Morgan,
Diaa Ibrahim,
Ahmed Samir
Issue:
Volume 2, Issue 2, June 2017
Pages:
25-31
Received:
9 June 2017
Accepted:
4 July 2017
Published:
24 July 2017
DOI:
10.11648/j.jeece.20170202.12
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Abstract: The current work demonstrates the feasibility of atmospheric pressure non-thermal plasma technique for NOX pollution control. Atmospheric pressure dielectric barrier discharge plasma reactor has been constructed for the treatment of the exhaust of 4kWs free load diesel engine. The nature and properties of the discharge were identified through studying electrical characterization of the discharge cell. The effect of applied voltage, discharge power and discharge length on the removal and energy efficiency of NOX has been investigated. Different parameters including, NOX removal efficiency, specific energy density and energy cost per molecule have been calculated, analyzed and interpreted. It has been found that the removal efficiency of NOX was varied from (16%-74%) at energy cost of values varied from (123-390 eV/molecule). The obtained data represents promising results and offers a solution for NOX pollution reduction.
Abstract: The current work demonstrates the feasibility of atmospheric pressure non-thermal plasma technique for NOX pollution control. Atmospheric pressure dielectric barrier discharge plasma reactor has been constructed for the treatment of the exhaust of 4kWs free load diesel engine. The nature and properties of the discharge were identified through study...
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Output Power Prediction of Photovoltaic Module Using Nonlinear Autoregressive Neural Network
Samuel Bimenyimana,
Godwin Norense Osarumwense Asemota,
Li Lingling
Issue:
Volume 2, Issue 2, June 2017
Pages:
32-40
Received:
1 June 2017
Accepted:
15 June 2017
Published:
26 July 2017
DOI:
10.11648/j.jeece.20170202.13
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Abstract: Precise prediction of generated output power plays an essential aspect in many sectors of power system like in solar energy sources which is the current topic being discussed on. It is of great role in every system but the prediction of output power for solar energy system is a tough task due to the influence of numerous parameters and fluctuations. Photovoltaic module being main part of the solar power system has many factors which can influence its performance where temperature is paramount. In this paper, the output power of a certain photovoltaic module was estimated under change of temperature and prediction of its future output power was done referring to the estimated power by nonlinear neural network. Both monthly and annual predictions were done through training, validation and test processes. The best monthly performance was achieved equal to 0.9743 at epoch 3 with regression values for training, test and validation all equal to 0.74274, 0.7166, 0.83388 and 0.75604 respectively. While the best annual best performance was achieved equal to 0.10284 at epoch 6 with regression values for training, test, validation and all equal to 0.76576, 0.73665, 0.71678 and 0.75386 respectively. Finally, results showed that nonlinear autoregressive neural network was good and effective for prediction of the photovoltaic module output power.
Abstract: Precise prediction of generated output power plays an essential aspect in many sectors of power system like in solar energy sources which is the current topic being discussed on. It is of great role in every system but the prediction of output power for solar energy system is a tough task due to the influence of numerous parameters and fluctuations...
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