References
- Abu Qdais, H., K. Bani Hani, and N. Shatnawi. 2010. Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm. Resources, Conservation and Recycling 54 (6):359–63. doi:https://doi.org/10.1016/j.resconrec.2009.08.012.
- Akbaş, H., B. Bilgen, and A. M. Turhan. 2015. An integrated prediction and optimization model of biogas production system at a wastewater treatment facility. Bioresource Technology 196:566–76. doi:https://doi.org/10.1016/j.biortech.2015.08.017.
- Alkaya, E., and G. N. Demirer. 2011. Anaerobic mesophilic co-digestion of sugar-beet processing wastewater and beet-pulp in batch reactors. Renewable Energy 36 (3):971–75. doi:https://doi.org/10.1016/j.renene.2010.08.040.
- Almomani, F. 2020. Prediction of biogas production from chemically treated co-digested agricultural waste using artificial neural network. Fuel 280 (April):118573. doi:https://doi.org/10.1016/j.fuel.2020.118573.
- Barik, D., and S. Murugan. 2015. An artificial neural network and genetic algorithm optimized model for biogas production from co-digestion of seed cake of Karanja and cattle dung. Waste and Biomass Valorization 6 (6):1015–27. doi:https://doi.org/10.1007/s12649-015-9392-1.
- Beltramo, T., C. Ranzan, J. Hinrichs, and B. Hitzmann. 2016. Artificial neural network prediction of the biogas flow rate optimised with an ant colony algorithm. Biosystems Engineering 143:68–78. doi:https://doi.org/10.1016/j.biosystemseng.2016.01.006.
- Beltramo, T., M. Klocke, and B. Hitzmann. 2019. Prediction of the biogas production using GA and ACO input features selection method for ANN model. Information Processing in Agriculture 6 (3):349–56. doi:https://doi.org/10.1016/j.inpa.2019.01.002.
- Dahunsi, S. O., S. Oranusi, J. B. Owolabi, and V. E. Efeovbokhan. 2016. Comparative biogas generation from fruit peels of fluted pumpkin (Telfairia occidentalis) and its optimization. Bioresource Technology 221:517–25. doi:https://doi.org/10.1016/j.biortech.2016.09.065.
- David R Legates, G. J. M. (1999). Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water resources research, VOL. 35, NO. 1, PAGES 233–241. 10.1029/1998WR900018
- Dibaba, O. R., S. K. Lahiri, S. T’Jonck, and A. Dutta. 2016. Experimental and artificial neural network modeling of a Upflow Anaerobic Contactor (UAC) for biogas production from vinasse. International Journal of Chemical Reactor Engineering 14 (6):1241–54. doi:https://doi.org/10.1515/ijcre-2016-0025.
- Dos Santos, L. A., R. B. Valença, L. C. S. da Silva, S. H. B. de Holanda, A. F. V. da Silva, J. F. T. Jucá, and A. F. M. S. Santos. 2020. Methane generation potential through anaerobic digestion of fruit waste. Journal of Cleaner Production 256. doi:https://doi.org/10.1016/j.jclepro.2020.120389.
- El-Mashad, H. M., and R. Zhang. 2010. Biogas production from co-digestion of dairy manure and food waste. Bioresource Technology 101 (11):4021–28. doi:https://doi.org/10.1016/j.biortech.2010.01.027.
- Farzaneh-Gord, M., B. Mohseni-Gharyehsafa, A. Arabkoohsar, M. H. Ahmadi, and M. A. Sheremet. 2020. Precise prediction of biogas thermodynamic properties by using ANN algorithm. Renewable Energy 147:179–91. doi:https://doi.org/10.1016/j.renene.2019.08.112.
- Ghatak, M. D., and A. Ghatak. 2018a. Artificial neural network model to predict behavior of biogas production curve from mixed lignocellulosic co-substrates. Fuel 232 (May):178–89. doi:https://doi.org/10.1016/j.fuel.2018.05.051.
- Gueguim Kana, E. B., J. K. Oloke, A. Lateef, and M. O. Adesiyan. 2012. Modeling and optimization of biogas production on saw dust and other co-substrates using artificial neural network and genetic algorithm. Renewable Energy 46:276–81. doi:https://doi.org/10.1016/j.renene.2012.03.027.
- Kafle, G. K., and L. Chen. 2016. Comparison on batch anaerobic digestion of five different livestock manures and prediction of biochemical methane potential (BMP) using different statistical models. Waste Management 48:492–502. doi:https://doi.org/10.1016/j.wasman.2015.10.021.
- Kong, H. 2019. ScienceDirect ScienceDirect ScienceDirect ScienceDirect A comparative life cycle assessment on mono- and co-digestion of A comparative life cycle assessment on food waste and sewage sludge food waste and sewage sludge assessing the feasibility Tong distri. Energy Procedia 158:4166–71. doi:https://doi.org/10.1016/j.egypro.2019.01.814.
- Li, W., M. A. H. Siddhu, F. R. Amin, Y. He, R. Zhang, G. Liu, and C. Chen. 2018a. Methane production through anaerobic co-digestion of sheep dung and waste paper. Energy Conversion and Management. doi:https://doi.org/10.1016/j.enconman.2017.08.002.
- Li, Y., F. Xu, Y. Li, J. Lu, X. S. GongLi, A. Shah, X. Zhang, H. Zhang, X. Gong, and G. Li. 2018b. Reactor performance and energy analysis of solid state anaerobic co-digestion of dairy manure with corn stover and tomato residues. Waste Management 81:117–27. doi:https://doi.org/10.1016/j.wasman.2017.11.041.
- Li, Y., Y. Chen, and J. Wu. 2019. Enhancement of methane production in anaerobic digestion process: A review. Applied Energy 240 (February):120–37. doi:https://doi.org/10.1016/j.apenergy.2019.01.243.
- Liu, G. 2013. Evaluating Methane Production from Anaerobic Mono- and Co-digestion of Kitchen Waste, Corn Stover, and Chicken Manure. Energy & Fuels 27 (4), 2085–2091; doi:https://doi.org/10.1021/ef400117f
- Lora, R., A. Maria, D. S. Antune, F. Valéria, A. Sánchez, R. Barrena, and X. Font. 2017. Technology overview of biogas production in anaerobic digestion plants : A European evaluation of research and development. Renewable and Sustainable Energy Reviews 80 (February):44–53. doi:https://doi.org/10.1016/j.rser.2017.05.079.
- Mao, C., Y. Feng, X. Wang, and G. Ren. 2015. Review on research achievements of biogas from anaerobic digestion. Renewable and Sustainable Energy Reviews 45:540–55. doi:https://doi.org/10.1016/j.rser.2015.02.032.
- Mata-Alvarez, J., J. Dosta, M. S. Romero-Güiza, X. Fonoll, M. Peces, and S. Astals. 2014. A critical review on anaerobic co-digestion achievements between 2010 and 2013. Renewable and Sustainable Energy Reviews 36:412–27. doi:https://doi.org/10.1016/j.rser.2014.04.039.
- McCall, J. 2005. Genetic algorithms for modelling and optimisation. Journal of Computational and Applied Mathematics 184 (1):205–22. doi:https://doi.org/10.1016/j.cam.2004.07.034.
- Muhammad, S., A. Burney, T. A. Jilani, and C. Ardil. 2008. Levenberg-Marquardt Algorithm for Karachi Stock Exchange Share Rates Forecasting. International Journal of Computational Intelligence, 1 (3), pp. 144–149
- Nair, V. V., H. Dhar, S. Kumar, A. K. Thalla, S. Mukherjee, and J. W. C. Wong. 2016. Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor. Bioresource Technology 217:90–99. doi:https://doi.org/10.1016/j.biortech.2016.03.046.
- Najafi, B., S. Faizollahzadeh Ardabili, S. Shamshirband, K. W. Chau, and T. Rabczuk. 2018. Application of anns, anfis and rsm to estimating and optimizing the parameters that affect the yield and cost of biodiesel production. Engineering Applications of Computational Fluid Mechanics 12 (1):611–24. doi:https://doi.org/10.1080/19942060.2018.1502688.
- Nsair, A., S. O. Cinar, A. Alassali, H. A. Qdais, and K. Kuchta. 2020. Operational parameters of biogas plants: A review and evaluation study. Energies 13 (15):15. doi:https://doi.org/10.3390/en13153761.
- Pavi, S., L. E. Kramer, L. P. Gomes, and L. A. S. Miranda. 2017. Biogas production from co-digestion of organic fraction of municipal solid waste and fruit and vegetable waste. Bioresource Technology 228:362–67. doi:https://doi.org/10.1016/j.biortech.2017.01.003.
- Pütün, A. E., B. B. Uzun, E. Apaydin, and E. Pütün. 2005. Bio-oil from olive oil industry wastes: Pyrolysis of olive residue under different conditions. Fuel Processing Technology 87 (1):25–32. doi:https://doi.org/10.1016/j.fuproc.2005.04.003.
- Sathish, S., and S. Vivekanandan. 2016. Parametric optimization for floating drum anaerobic bio-digester using response surface methodology and artificial neural network. Alexandria Engineering Journal 55 (4):3297–307. doi:https://doi.org/10.1016/j.aej.2016.08.010.
- Sola, J., and J. Sevilla. 1997. Importance of input data normalization for the application of neural networks to complex industrial problems. IEEE Transactions on Nuclear Science 44 (3PART 3):1464–68. doi:https://doi.org/10.1109/23.589532.
- Tufaner, F., and Y. Avşar. 2016. Effects of co-substrate on biogas production from cattle manure: A review. International Journal of Environmental Science and Technology 13 (9):2303–12. doi:https://doi.org/10.1007/s13762-016-1069-1.
- Tufaner, F., Y. Avşar, and M. T. Gönüllü. 2017. Modeling of biogas production from cattle manure with co-digestion of different organic wastes using an artificial neural network. Clean Technologies and Environmental Policy 19 (9):2255–64. doi:https://doi.org/10.1007/s10098-017-1413-2.
- Tufaner, F., and Y. Demirci. 2020. Prediction of biogas production rate from anaerobic hybrid reactor by artificial neural network and nonlinear regressions models. Clean Technologies and Environmental Policy 22 (3):713–24. doi:https://doi.org/10.1007/s10098-020-01816-z.
- Ugwu, S. N., and C. C. Enweremadu. 2019. Effects of pre-treatments and co-digestion on biogas production from Okra waste. Journal of Renewable and Sustainable Energy 11:1. doi:https://doi.org/10.1063/1.5049530.
- Wang, J., and W. Wan. 2009. Kinetic models for fermentative hydrogen production: A review. International Journal of Hydrogen Energy 34 (8):3313–23. doi:https://doi.org/10.1016/j.ijhydene.2009.02.031.
- Wang, P., H. Wang, Y. Qiu, L. Ren, and B. Jiang. 2018. Microbial characteristics in anaerobic digestion process of food waste for methane production–A review. Bioresource Technology 248:29–36. doi:https://doi.org/10.1016/j.biortech.2017.06.152.
- Xu, F., Y. Li, X. Ge, L. Yang, and Y. Li. 2018. Anaerobic digestion of food waste– Challenges and opportunities. Bioresource Technology. doi:https://doi.org/10.1016/j.biortech.2017.09.020.
- Yu, Q., S. Jaroenpoj, and J. Griffith. 2015. Development of artificial neural network models for biogas production from co-digestion of leachate and pineapple peel. The Global Environmental Engineers 1 (2):42–47. doi:https://doi.org/10.15377/2410-3624.2014.01.02.2.
- Zhang, L., Y. W. Lee, and D. Jahng. 2011. Anaerobic co-digestion of food waste and piggery wastewater: Focusing on the role of trace elements. Bioresource Technology 102 (8):5048–59. doi:https://doi.org/10.1016/j.biortech.2011.01.082.
- Zhao, Y., F. Sun, J. Yu, Y. Cai, X. Luo, Z. Cui, Y. Hu, and X. Wang. 2018. Co-digestion of oat straw and cow manure during anaerobic digestion: Stimulative and inhibitory effects on fermentation. Bioresource Technology 269:143–52. doi:https://doi.org/10.1016/j.biortech.2018.08.040.