Renewable Energy

This research is carried out to study the used of SPC tools towards identifying the variation in quality finished product at Bio-organic fertilizer company. The objectives of this research are to analyze and monitor quality of finished product using Xbar R chart and identify the relationship between variables using scatter diagram. Data was obtained from Quality Department through finished product of 5.5.5PE from January 2008 until December 2008. This finished product can be measure according to pH, moisture, and crash. SPSS version 11.5 software has been used to analyze and interpret the data into control chart and scatter diagram. The unstructured interview with respective workers was done in order to support the data analysis. The finding revealed that the SPC tools can determine process variation in finished product through systematical analysis with more accurately. Since All Cosmos has no specific tool to analyze the data, it is suitable for them to implement this tool.

Solar energy applications have become one of the most promising alternatives to electricity production. Solar cells are also being used to equip many isolated consumers in areas far from the central grid around the world. In this experimental work, the effect of solar radiation on solar cell output such as voltage, current, and efficiency of a solar panels was investigated in the conditions of the city of Baghdad, Iraq climates. The data has been recorded using calibration devices. An analysis of the relationship between the intensity of solar radiation and the cell output was performed for voltage and efficiency. The results of the study show that the relationship between the intensity of solar radiation and cell output is a direct and very important relationship if not the most important among all the atmospheric variables. That increasing the intensity of solar radiation caused an increase in current output which means greater cell efficiency.

To further speed up the drilling operations and thus further reducing costs and lower risk operations, we can simulate and predict the conditions to achieve desired results. For this to work with programming and According to data from directional wells drilled in Ahvaz field, can to reached Optimum value for drilling in this field. With The modeling of well conditions and Drilling parameters as for Existing wells in this field, we will find the identical and reliable and functional model. Many factors are effective in rate of penetration. Neural network modeling for the relationship between these variables is very important and many help to optimization the process. In this paper, Using Bourgoyne and Young's equation explains the relationship between these variables. The first step in the application of neural networks made model at the starting drilling point of well. Neural network data can be divided into three parts. 70% of data for network training and 15% of the data for the network Validation and 15% of the data for network sensitivity analysis has been assigned. The percentage error in the calculations must reach down. Because the Studies with the optimal values should be to reduce the risk and acceleration of drilling process. Cycle process are includes approximately 2-4 million estimate for each analysis. All of these processes are repeated for Establish a relationship between variables and graphs.

In developing country like India, energy is most important to economical development of country thus energy sector plays an important role to increase in energy dependence and investment made on it. As we all know the compressed air generation consumes energy. Normally the compressed air system is an overlooked area in most of the industry but compressed air system is a costly source of energy about 7 to 10 times more expensive than electricity. Better methodology for design of energy efficient compressed air system, better maintaince practices and through energy efficiency measures can save the significant amount of energy of any industry. In this paper we have discussed the methodology for design of compressed air system for energy efficient compressed air system for industry. This paper also gives the comparative study of existing compressed air system of one industry with new optimized designed compressed air system for same industry using their specification.

In this paper, we proposed the development of neural adaptive controls to ensure the robustness of uncertain nonlinear multivariable systems. We used two techniques: Robust neural adaptive control and neural indirect adaptive control. The study of the stability and robustness of both techniques was performed by Lyapunov theory. To validate these techniques and discover their effectiveness, a simulation example was considered. The simulation results obtained by these two control techniques have shown the effects of disturbance compensation, good performance tracking data paths and stability control systems. Comparative studies between these two techniques show that the neural indirect adaptive control cannot mitigate the effect of disturbances compared to the robust neural adaptive control.

This research presents the description of the procedure for the manufacturing the Organo-lead halide Peroveskite Solar Cells (OPSC) . We descried the synthesis organo-lead halide peroveskite materials , MAPbIBr2 and MAPbBrCl2 , which employed as absorption layer in OPSCs . Optical properties for the peroveskite materials which used to prepare OPSCs is tested by the measurements the absorbance spectrum in order to calculate the energy gap . X-ray diffraction , AFM microscope and SEM microscope are used in our work to study the structure of the samples . Finally , the tests OPSCs by Light I-V Measurement System are implemented in order to obtained the parameters of the manufacturing devices . The Power Conversion Efficiencies (PCE) of OPSCs are (0.12%) and (0.07%) to the devices have MAPbIBr2 and MAPbBrCl2 as sensitized absorption layer, respectively. Measurements are tested at AM1.5 global sunlight (100 mW cm?2) .

The absorber layer antimony tin sulfide thin film was deposited by physical vapor deposition (PVD) using two source simultaneous deposition techniques for solar cell applications. The photoconductivity was measured with a photoconductivity spectrometer which confirms the photo-activeness of antimony tin sulfide. Thickness, band gap, refractive index and transmittance were calculated by using ellipsometry techniques. The transmittance of the material was above the visible range which decreases with the increase of wavelength. The material has n-type conductivity measured with hot probe technique.

Fault diagnosis research deals with real world problems in terms of plant efficiency, maintainability and reliability. Fault in process and manufacturing industries are crucial in order to improve production efficiency, quality of the product and the cost of production. This work is focused on the development of a fault diagnosis method for application to an actuator benchmark specifically designed for industrial fault diagnosis. The performance of the proposed approach is demonstrated on the DAMADICS benchmark problem.

There are several types of neural network architectures and training techniques that can be used to make an artificial neural network capable of fault detection and identification. They possess powerful characteristics such as fast learning, fault tolerance and ability to produce correct output when fed with partial inputs. Different Neural networks offer variety of framework for modeling and control based on their structure, dynamics and learning methods. The performance of different artificial neural networks in fault diagnosis task have been demonstrated in this paper.

Piezoelectric materials offer a promising approach to reduce our dependence on conventional source of electricity. Crowd Farming can be a useful technique to extract power from piezoelectric materials. In our work, calculation have been made based on piezoelectric flooring system in crowded area, as the source of pressure is the footsteps of the crowd. Further from the literature study on piezoelectricity, one footstep can provide electrical energy to light two 60-watt bulbs for one second and the effect of large number of people on piezoelectric floor can generate electricity of substantial quantity.