COMPARATIVE EVALUATION ON THE POTENTIALS OF SHEEP RUMEN CONTENTS FOR BIOGAS GENERATION
DOI:
https://doi.org/10.33003/fjs-2022-0602-1757Keywords:
Biogas, Biofertilizer, Sheep Rumen Contents, Digester, Anaerobic Digestion, SlurryAbstract
Biogas refers to a gas produced by the biological breakdown of organic matter in the absence of oxygen. It is as a clean biofuel produced by microorganism during anaerobic digestion of organic matter. The study was carried out to determine the potentials of sheep rumen contents for biogas production through anaerobic digestion. Proximate analysis was carried out on the substrate. The results shows that nitrogen, potassium and phosphorus have 0.58 ± 0.02 (mg/ml), 425 ± 5.00 (mg/ml), and 2.57 ± 0.03 (mg/ml) respectively. After proximate analysis, four (4) local digesters with capacity of 500g tins were designed and used for the collection of gas via passage pipe systems. The digesters were used to digest the slurry (mixture of Sheep rumen content and water) which were mixed in the ratio of 2:1 for a period of eight weeks (56 days) retention time until the biogas reduced significantly. The pH values of the slurries were adjusted to neutral. The digesters were stirred thrice daily to avoid scum formation in the digesters and allow for easy escape of gas produced. The total average yield of the gas produced were 820 cm3, 1070 cm3, 780 cm3, and 660 cm3 for D1, D2, D3, and D4 respectively. Isolation of Microorganisms were caried during the production where B. subtilis, E. coli, P. aeruginosa, and S. aureus were found. Biogas production from organic wastes which is an eco-friendly, which helps to solve most countries energy crisis.
References
Agboola, S. O. (2022a). The Decomposition and Aggregation Algorithmic Numerical Iterative Solution Methods for the Stationary Distribution of Markov Chain, Journal of Scientific and Engineering 2022, 9(1): 116 - 123. CODEN (USA): JSERBR. www.jsaer.com.
Agboola, S. O. (2022b). Analysis and Applications of Random Walk and Gambler’s Ruin on Irreducible Periodic Markov Chain, Jewel Journal of Scientific Research, Faculty of Science, Federal University of Kashere. (JJSR) 7(1): 1 – 8, 2022. www.fukashere.edu.ng/jjsr .
Agboola, S. O. (2021). Direct Equation Solving methods Algorithms Compositions of Lower -Upper Triangular Matrix and Grassmann–Taksar–Heyman for the stationary Distribution of Markov chains, International Journal of Applied Science and Mathematics (IJASM). 8(6): 87 – 96. www.ijasm.org.
Agboola, S. O. and Ayinde, S. A. (2022). On the Application of Successive Overrelaxation Algorithmic and Block Numerical Iterative Solutions for the Stationary Distribution in Markov Chain, Nigerian Journal of Pure and Applied Sciences, Faculty of Physical Sciences and Faculty of Life Sciences, University of Ilorin, Nigeria (NJPAS). 35 (1): 4263 – 4272. http://njpas.com.ng.
Agboola, S. O. and Ayinde, S. A. (2021). The Performance Measure Analysis on the States Classification in Markov Chain, Dutse Journal of Pure and Applied Sciences (DUJOPAS), Faculty of Science Journal, Federal University Dutse, Jigawa State.7(04b): 19 – 29.https://fud.edu.ng/dujopas.
Agboola, S. O. and Ayoade, A. A. (2022). Performance Measure Analysis of The Reachability Matrix And Absorption Probabilities For Close And Open Classification Group Of States In Markov Chain, Nigerian Journal of Scientific Research, (NJSR), Ahmadu Bello University Journals, Faculty of Science, ABU, Zaria. 20(4): 1 – 6. https://journals.abu.edu.ng. Njsr.abu.edu.ng.
Agboola, S. O. and Ayoade, A. A. (2021). On the Analysis of Matrix Geometric and Analytical Block Numerical Iterative Methods for Stationary Distribution in the Structured Markov Chains, International Journal of Contemporary Applied Researches (IJCAR), 8(11): 51 – 65, Turkey.http://www.ijcar.net.
Agboola, S. O. and Badmus, N. I. (2021a). The Application of Runge-Kutta and Backward Differentiation Methods for Solving Transient Distribution in Markov Chain, Nigerian Journal of Mathematics and Applications. Department of Mathematics, University of Ilorin, Nigeria. 31: 191 – 201.
Agboola, S. O. and Badmus, N. I. (2021b). Application of Renewal Reward Processes in Homogeneous
Discrete Markov Chain, Faculties of Life and Physical Sciences Journal, Federal University DutsinMa. FUDMA Journal of Sciences (FJS). 5(4): 210 – 215. https://fjs.fudutsinma.edu.ng
Agboola, S. O. and Nehad A. S. (2022). On the Application of Matrix Scaling And Powering Methods of Small State Spaces For Solving Transient Distribution In Markov Chain, FUDMA Journal of Sciences (FJS), Faculties of Earth Science and Physical Science Journal, Federal University DutsinMa.6(1): 135 – 140.https://fjs.fudutsinma.edu.ng
Azizah, A., Welastica, R., Nur, F., Ruchjana, B. and Abdullah, A.(2019). An application of Markov chain for predicting rainfall data at West Java using data mining approach, Earth and Environmental Science, 303(1): 203 – 216.
Clemence. T. (2019). Markov chain modelling of HIV, Tuberculosis, and Hepatitis B transmission in Ghana, Hindawi, Interdisciplinary Perspective on Infectious Disease, 27(1): 204 – 214.
Dayar., T. (1998). Permuting Markov chains to nearly completely decomposable form. Technical Report BU-CEIS-9808, Department of Computer Engineering and Information Science, Bilkent University, Ankara, Turkey. pp. 18 – 31.
Pesch, T., Schroder, S., Allelein, H. and Hake, J. (2015). A new Markov chain related statistical approach for modelling synthetic wind power time series, New Journal of Physics, DentschePhysikalishe, 35(2): 64 – 85.
Philippe, B and Sidje, B. (1993) Transient Solution of Markov Processes by Krylov Subspaces, Technical Report, IRISA—Campus de Beaulieu, Rennes, France. pp. 11 - 24.
Ramaswami, V. (1988). A Stable Recursion for the Steady State Vector in Markov chains of M/G/1 type. Communication in Statist. Stochastic Models, 4(1): 183–188.
Ramaswami, V and Neuts, M. F. (1980). Some explicit formulas and computational methods for infinite server queues with phase type arrivals. Journal of Applied Probability,17(1): 498–514.
Romanovsky, V.I. (1970). Discrete Markov Chains, Wolters-Noord off, Groningen, Netherlands. pp. 23 – 44.
Stewart, W. J. (1994). Introduction to the Numerical Solution of Markov Chains. Princeton University Press, Princeton, N.J. pp. 14 – 38.
Stewart, W. J. (2009). Probability, Markov Chain, Queues and Simulation, Princeton University Press, United Kingdom. pp. 1- 42.
Uzun, B. and Kiral, E. (2017). Application of Markov chain-fuzzy states to gold price, Science Direct. ELSEVIER,120(1): 365 – 371.
Vermeer, S. And Trilling, D. (2020): Toward a better understanding of a new user journeys: A Markov chain approach. Journalism Journal,21(1):879 – 894.
Zakaria, N. N., Mahmod, O., Rajalmgan, S., Hamita, D., Lazim, A. and Evizal, A. (2019). Markov chain model development for forecasting air pollution index of Miri, Sarawak, Sustainability11(1): 5190 -5202.
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