Statistical Data Analysis on Renewable Biogas Energy Production Report

This report presents a statistical analysis of renewable biogas energy production, focusing on data collection, analysis, and interpretation. The study investigates biogas production from various organic waste sources, including domestic, agricultural, and landfill wastes. The methodology involves collecting data on parameters like carbon dioxide, methane, and nitrous oxide production. Statistical techniques, such as mean, median, mode, standard deviation, and correlation analysis, are applied to evaluate the relationships between different variables and assess the reliability of biogas production. The report includes tables and graphs illustrating the statistical distributions and correlations. The discussion section interprets the results, comparing the performance of different waste sources and analyzing the factors influencing biogas yield. The analysis aims to determine the efficiency and cost-effectiveness of biogas production, offering recommendations for optimizing energy production and promoting the use of renewable energy sources. The report concludes by emphasizing the importance of biogas as a sustainable energy source and suggesting further research to improve production methods and expand its application.

Statistical Analysis on Renewable - Biogas Energy Production 1
STATISTICAL ANALYSIS ON RENEWABLE - BIOGAS ENERGY PRODUCTION
Student ID Number
Module Code
Year Module Run
Assessment Title: Statistical Analysis on Renewable - Biogas Energy Production

Statistical Analysis on Renewable - Biogas Energy Production 2
Abstract
Energy production is considered as an economic wellbeing for the diversity of any
country and includes; fossils fuels, renewable energy, nuclear power source, wind energy supply,
hydroelectric and geothermal energy supply. The renewable energy supplies are sourced from
various parameters such as wind, moving water, sunlight and geothermal heat that re not subject
to depletion. The process of energy supply consist various networks for energy conservation,
generation, transportation and energy consumption (Weiland, 2010). The connection provides
basic flexibility for transforming energy and supply to various areas of coverage. However,
energy supply is faced by various challenges such as energy shortage in some areas that require
restoration. Therefore, the best solution to such shortages is to apply the statistical data analysis
to solve the problem. The major objectives for the determination of such aspects is apply
statistical techniques in calculation of the costs. Research methodologies have been incorporated
so that the supply of power for development and coming up with solutions arising in this area.
Renewable energy systems are designed for the purpose of statistical analysis and addressing the
emergency issues with uncertain parameters (Teleke, Baran, Bhattacharya, and Huang, 2010).
Introduction
Renewable biogas energy is an economic, social and politically determined set of modern
basis of energy globally. This energy is obtained from inorganics domestic waste resources that
continually re-used based on human consumption, time scale including; heat, the rains, sun rays,
wind, tides and even the waves. Arguably, an estimate of 20% worldwide energy consumption is
obtained from the renewable resources, 10% comes from biomass that is used for heating

Statistical Analysis on Renewable - Biogas Energy Production 3
purposes, and 4.6 coming from hydroelectricity (Salihu, Alam, AbdulKarim and Saalleh, 2012).
However, another 3.5% from the renewable energy sources accounted for the multipurpose such
as modern biomass, solar energy, bio-fuels energy, wind energy. Consequently total
contributions of this renewable energy in field production of electricity are approximated to be
20% with some 15% contribution from hydroelectricity and 4% from the renewable sources of
power. These categories of electricity have resulted in the various choices that make a network of
the system composing the energy generation, transformation, supply, transportation and finally
energy consumption (Poschl, Ward, and Owende, 2010).
The research study based its discussions on the biogas production from different sources
of organic material wastes that were well prepared in the digester through fermentation. This
method of energy production provides great relief to the cut off of expenses and the costs of
living. Biogas energy is the best power for the purpose of cooking, home lighting, and better in
running of the machines and this kind of energy is environmental friendly with reduced risks
(Panawar, Kaushik and Kothari, 2011). It was observed that the organic material wastes were
first decomposed enabling stability and aerobic and anaerobic digestion followed respectively.
The report studies the statistical data analysis on the techniques used in the energy renewable
that assists in determining the reliability of the energy for the commercial use by the customers
(Wieland, 2010).
Methodology
Most of the statistical data is collected for the determination of the techniques used for the
renewable biogas energy production. The categorical analysis of dependent data and independent

Statistical Analysis on Renewable - Biogas Energy Production 4
variables was carried out for the collection of responses regarding biogas energy production. The
parameters include landfills, waste water treatment, agricultural wastes, wood biomass and
Domestic wastes used for the gas production such as the poultry wastes; kitchen wastes
groundnut remains, and orange husks (Chiew, Iwata and Shimada, 2011). The interdependence
existing among the various variables were also analyzed by applying the Z score method for the
independence of the variables. The statistical techniques for the determination of the mean,
median, mode, standard deviation and regression of the sample data is regulated for different
datasets. The Coefficient of correlation is also determined for the consumer of the biogas energy
(Liserre, Sauter and Hung, 2010). Most of biogas renewable energy relies on the other forms
such as sunlight, wind, and the hydroelectric due to the heating of the surface of the earth. energy
Sample data collection
Biogas energy supply started with a sample of about 30% using domestic wastes and 60%
of agricultural wastes. The samples were the most preferred du to their low bacterial content
digestion and higher cellulose components from the agricultural materials. A high production of
about 809 cubic centimeters were supplied from the sample that contained half (50%) by poultry
content (Li et al. 2011).
Statistical data analysis
Statistical distributions are plotted for the sample data and the interpretation is carried out
for the estimation of parameters selected. For each of the estimated parameter a graph is plotted
for to check the normal distribution of the sample data.

Statistical Analysis on Renewable - Biogas Energy Production 5
Table1.0
Parameters of the
sample data
% resource available
Landfills
Waste water
treatment
Agricultural sources
Wood biomass
Domestic wastes
60
45
30
20
10
Table 2.0: Coefficient of correlation between gases produced in biogas energy production
Carbon Dioxide and
Methane gas
Carbon Dioxide and
Nitrous Oxide gas
Methane and Nitrous
Oxide gas
Correlation of
Coefficient(R2)
Z value
-0.093349675
1.2536
0.574353922
-0.1542
-0.732310199
1.0253
Table 3.0: Mean Median, Mode and SD of the gases produced in Biogas energy.
Statistical Categories (CO2) ( CH4) (NO2)
Mean( X ) 156.456 121.0138 20.0642

Statistical Analysis on Renewable - Biogas Energy Production 6
Median 132.642 120.3556 20.6356
Mode - - 24.5452
Standard
Deviation(s)
28.258632 5.654826 2.08456
Coefficient
of
variation
0.078526 0.0542586 0.06458
Discussion
The mean for carbon (IV) Oxide was the highest with 10%, followed by 6% and 2% NO2
obtained from the median from the above gases. However the above variation in the mean,
median and standard variation show that the normal distribution of the gases value for each of
the parameters compared to the mean value (Hosseini and Wahid, 2013). The standard deviation
of Carbon dioxide (CO2), CH4, and Nitrous gas is 28.258632, 5.654826 and 2.08456
respectively, meaning that the sample of 60% lies between their mean (X) value and the standard
deviation (Weiland, 2010).
However, the higher value of mean for Carbon (IV) Oxide shows that the values are
likely to be less reliable on considering the given data. While the mean for methane gas in the
biogas energy production is more reliable considering the mean shown from above compared to
the mean of Nitrous gas (NO2) N the mean is the most reliable since the mean is the lowest from

Statistical Analysis on Renewable - Biogas Energy Production 7
the table shown above (Holm, Alseadi and Oleskowicz , 2009). The variation of the coefficient
of variation for methane gas is approximately to 3.565% which means that the graph will not be
equally spread considerably with the other parameters such as Carbon (IV) Oxide CO2, and
Nitrous gas NO2 (Lund,2009).
Table 4.0
Parameter Biogas from
landfills
Waste water
treatment
Agricultural
sources
Wood
biomass
Domestic
wastes
Energy
obtained (TJ)
700 850 25 23 15
Energy
supply (Gwh)
25 30 10 8 6
Mean 3.456 2.568 2.125 2.431 2.012
Median 3.125 2.464 2.045 1.965 1.568
Standard
Deviation
0.1568 0.1256 0.1245 0.0945 0.0945
Coefficient
of variations
%
6.523 5.856 4.568 3.569 2.586
1.543 1.423 -1.253 -0.9564 0.5623

Statistical Analysis on Renewable - Biogas Energy Production 8
Z value
Statistical Discussion on Table 4.0
A sample data is collected from the sources of biogas production for the purpose of
carrying out the tests. For each and every source landfill, waste water, agricultural wastes, wood
biomass and domestic wastes are determined for the statistical analysis. Therefore, the arithmetic
mean, median, standard deviation (SD) is calculated for of the sets in the biogas energy
production (Haas et al. 2011). Evidently, from table 4.0 the critical Z values are determined for
each and every parameter and their statistical distribution for the sample calculated fir normal
distribution.
Figure 2: Methane Emission in Carbon Dioxide for the production
of biogas energy.
150140130
120
Average CO2
Median
Mean
Methane Emission in Carbon Dioxide in biogas energy production
0.08
0.06
0.04
0.02
0
90100110
Density

Statistical Analysis on Renewable - Biogas Energy Production 9
Normal Distribution of bar chart Graph
Result Analysis
1. Correlation
The correlation between any two parameters can be shown using Coefficient
of correlation. The formula is shown below:

Statistical Analysis on Renewable - Biogas Energy Production 10
Recommendations
It is argued that biogas production is the cheaper energy source of power with 62% of the energy
supply. Arguably, Agricultural wastes from the farm produce greatly contribute to this energy
supply in manner that other organic wastes such as the sewerage and the manure decomposition
also determines the amount of energy produced. The biogas energy production however depends
on the various parameters that determines the possibilities if of the costs in the supply.
Comparatively to the other sources of energy such as wind, solar biogas energy was
recommendable due to its costs and it provides efficient and flexible power with the
incorporation of other driver.
Production of biogas energy from the domestic wastes
The graph shows production of biogas by use of domestic wastes.
The figure above shoes the daily supply of biogas and energy from the digester plant.
Arguably estimate f thirty grams of poultry waste and thirty grams of groundnuts remains. The
coefficient of correlation (R2) was also graphically determined for the values of the trend as 0.96

Statistical Analysis on Renewable - Biogas Energy Production 11
of daily biogas production. The gas production started on the seventh day of and captured for the
period of 7 days (Foo and Hameed 2010). Therefore, the value of coefficient of correlation
recorded significant outcome for future use, determination and prediction for energy production.
Outcomes are most expected in the prediction of the percentage of the energy production. The
initial gas production that is responsible for the whole process in termed inactive (Haas, et al.
2011). The aerobic process presents the oxygen was utilized in digester inducing the Bactria to
be inactive. The initial gas remained in the digester ids Carbon (IV) Oxide. In the process
methane gas constantly increased in the volume up to the maximum level on the 18th day and the
level started lowering gradually (Evans, Sttrezov and Evans, 2009).
Conclusion
In conclusion, biogas energy production is considered as aerobic fermentation of the various
parameters such as domestic wastes, agricultural wastes, wood wastes and landfills. In a period
of twenty two days the high volumes of biogas energy have been produced from the mixtures of
various categories of energy renewable sources (Esen and Yuksel, 2013). A digester in this case
produced large volumes of the gas hence the higher energy production for the domestic use. I t
was however, noted that the digester to be increased to more than one for the better and efficient
production of the energy. The two digesters will be designed in a way that one processes the gas
at the peak and when the other one is the initial stage of production. In general renewable biogas
energy is cost friendly for maintenance and production even environmentally friendly
References
Aghaei, J. and Alizadeh, M.I., 2013. Demand response in smart electricity grids equipped with
renewable energy sources: A review. Renewable and Sustainable Energy Reviews, 18, pp.64-72.

Statistical Analysis on Renewable - Biogas Energy Production 12
Akella, A.K., Saini, R.P. and Sharma, M.P., 2009. Social, economical and environmental
impacts of renewable energy systems. Renewable Energy, 34(2), pp.390-396.
Bayrakcı, A.G. and Koçar, G., 2012. Utilization of renewable energies in Turkey's
agriculture. Renewable and Sustainable Energy Reviews, 16(1), pp.618-633.
Chiew, Y.L., Iwata, T. and Shimada, S., 2011. System analysis for effective use of palm oil
waste as energy resources. Biomass and Bioenergy, 35(7), pp.2925-2935.
Edenhofer, O., Madruga, R.P., Sokona, Y., Seyboth, K., Matschoss, P. and Kadner, S., 2012.
Renewable energy sources and climate change mitigation. SRREN_FD_SPN_Final Report,
http://www. ipcc. ch/report/srren.
Ellabban, O., Abu-Rub, H. and Blaabjerg, F., 2014. Renewable energy resources: Current status,
future prospects and their enabling technology. Renewable and Sustainable Energy Reviews, 39,
pp.748-764.
Esen, M. and Yuksel, T., 2013. Experimental evaluation of using various renewable energy
sources for heating a greenhouse. Energy and Buildings, 65, pp.340-351.
Evans, A., Strezov, V. and Evans, T.J., 2009. Assessment of sustainability indicators for
renewable energy technologies. Renewable and sustainable energy reviews, 13(5), pp.1082-
1088.
Foo, K.Y. and Hameed, B.H., 2010. Insight into the applications of palm oil mill effluent: a
renewable utilization of the industrial agricultural waste. Renewable and Sustainable Energy
Reviews, 14(5), pp.1445-1452.
Haas, R., Resch, G., Panzer, C., Busch, S., Ragwitz, M. and Held, A., 2011. Efficiency and