تعداد نشریات | 161 |
تعداد شمارهها | 6,533 |
تعداد مقالات | 70,514 |
تعداد مشاهده مقاله | 124,131,231 |
تعداد دریافت فایل اصل مقاله | 97,237,496 |
Prediction Modelling to Enhance Anaerobic Co-digestion Process of OFMSW and Bio-flocculated Sludge Using ANN | ||
Pollution | ||
دوره 10، شماره 1، فروردین 2024، صفحه 481-494 اصل مقاله (1.35 M) | ||
نوع مقاله: Original Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/poll.2023.365129.2065 | ||
نویسندگان | ||
Kinjal C Shroff* ؛ Nirav G. Shah | ||
Civil Engineering Department, Faculty of Technology & Engineering, The Maharaja Sayajirao University of Baroda, Vadodara-390001, Gujarat, India | ||
چکیده | ||
Artificial neural networks (ANNs) simulate an anaerobic co-digestion process of Organic Fraction of Municipal Solid Waste (OFMSW) and bio-flocculated sludge for a mesophilic lab-scale semi-continuous feed reactor. The operational, substrate quality and process control parameters such as Organic Loading Rate, Hydraulic Retention Time, pH, VFA/Alkalinity ratio and Total Solids are input variables and methane yield and Volatile Solids removal are outputs for ANN modelling. The lab-scale experimental results are used to develop a prediction model using fitting application for ANN. The network architecture was optimized to achieve accurate predictions, resulting in a 5-19-2 architecture for methane yield and a 5-17-2 architecture for %VSremoval. The training was performed using the Bayesian Regularization (trainbr) algorithm, leading to high coefficients of determination (R2) of 0.953 and 0.978 for methane yield and %VSremoval, respectively. The results demonstrate the effectiveness of neural network-based modelling in capturing complex relationships within the methane yield process, facilitating accurate prediction of crucial output parameters. | ||
کلیدواژهها | ||
Organic Fraction of Municipal Solid Waste؛ Bio-flocculated sludge؛ Artificial Neural Network | ||
مراجع | ||
Abdel daiem, M. M., Hatata, A., Galal, O. H., Said, N., & Ahmed, D. (2021a). Prediction of biogas production from anaerobic Co-digestion of Waste Activated sludge and wheat straw using two-dimensional mathematical models and an artificial neural network. Renewable Energy, 178, 226–240. https://doi.org/10.1016/j.renene.2021.06.050 Abdel daiem, M. M., Hatata, A., Galal, O. H., Said, N., & Ahmed, D. (2021b). Prediction of biogas production from anaerobic Co-digestion of Waste Activated sludge and wheat straw using two-dimensional mathematical models and an artificial neural network. Renewable Energy, 178, 226–240. https://doi.org/10.1016/j.renene.2021.06.050 Alam, M. N. (2016). Codes in MATLAB for Training Artificial Neural Network using Particle Swarm Optimization Cochlear Implant View project Application of operation research on solving electrical engineering problems View project Codes in MATLAB for Training Artificial Neural Network using Particle Swarm Optimization. https://doi.org/10.13140/RG.2.1.2579.3524 Antonio, R., Universidad, V., México, L. S., Garro, B. A., Sossa, H., & Vázquez, R. A. (2011). Back-Propagation vs Particle Swarm Optimization Algorithm: which Algorithm is better to adjust the Synaptic Weights of a Feed-Forward ANN? In Article in International Journal of Artificial Intelligence. www.ceser.in/ijai.html Betiku, E., Okunsolawo, S. S., Ajala, S. O., & Odedele, O. S. (2015). Performance evaluation of artificial neural network coupled with generic algorithm and response surface methodology in modeling and optimization of biodiesel production process parameters from shea tree (Vitellaria paradoxa) nut butter. In Renewable Energy (Vol. 76, pp. 408–417). Elsevier Ltd. https://doi.org/10.1016/j.renene.2014.11.049 Chen, W. Y., Chan, Y. J., Lim, J. W., Liew, C. S., Mohamad, M., Ho, C. D., Usman, A., Lisak, G., Hara, H., & Tan, W. N. (2022). Artificial Neural Network (ANN) Modelling for Biogas Production in Pre-Commercialized Integrated Anaerobic-Aerobic Bioreactors (IAAB). Water (Switzerland), 14(9). https://doi.org/10.3390/w14091410 Dach, J., Koszela, K., Boniecki, P., Zaborowicz, M., Lewicki, A., Czekała, W., Skwarcz, J., Qiao, W., Piekarska-Boniecka, H., & Białobrzewski, I. (2016). The use of neural modelling to estimate the methane production from slurry fermentation processes. In Renewable and Sustainable Energy Reviews (Vol. 56, pp. 603–610). Elsevier Ltd. https://doi.org/10.1016/j.rser.2015.11.093 Dahunsi, S. O., Oranusi, S., Owolabi, J. B., & Efeovbokhan, V. E. (2016a). Mesophilic anaerobic co-digestion of poultry dropping and Carica papaya peels: Modelling and process parameter optimization study. Bioresource Technology, 216, 587–600. https://doi.org/10.1016/j.biortech.2016.05.118 Dahunsi, S. O., Oranusi, S., Owolabi, J. B., & Efeovbokhan, V. E. (2016b). Mesophilic anaerobic co-digestion of poultry dropping and Carica papaya peels: Modelling and process parameter optimization study. Bioresource Technology, 216, 587–600. https://doi.org/10.1016/j.biortech.2016.05.118 García-Gen, S., Rodríguez, J., & Lema, J. M. (2014). Optimisation of substrate blends in anaerobic co-digestion using adaptive linear programming. Bioresource Technology, 173, 159–167. https://doi.org/10.1016/j.biortech.2014.09.089 Intharathirat, R., Abdul Salam, P., Kumar, S., & Untong, A. (2015). Forecasting of municipal solid waste quantity in a developing country using multivariate grey models. Waste Management, 39, 3–14. https://doi.org/10.1016/j.wasman.2015.01.026 Kumar, S., Sau, S., Pal, D., Tudu, B., Mandal, K. K., & Chakraborty, N. (2013). Parametric performance evaluation of different types of particle swarm optimization techniques applied in distributed generation system. Advances in Intelligent Systems and Computing, 199 AISC, 349–356. https://doi.org/10.1007/978-3-642-35314-7_40 Le, L. T., Nguyen, H., Dou, J., & Zhou, J. (2019). A comparative study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in estimating the heating load of buildings’ energy efficiency for smart city planning. Applied Sciences (Switzerland), 9(13). https://doi.org/10.3390/app9132630 Momčilović, A. J., Stefanović, G. M., Rajković, P. M., Stojković, N. V., Milutinović, B. B., & Ivanović, M. P. (2018). The organic waste fractions ratio optimization in the anaerobic co-digestion process for the increase of biogas yield. Thermal Science, 22, S1525–S1534. https://doi.org/10.2298/TSCI18S5525M Mougari, N. E., Largeau, J. F., Himrane, N., Hachemi, M., & Tazerout, M. (2021). Application of artificial neural network and kinetic modeling for the prediction of biogas and methane production in anaerobic digestion of several organic wastes. International Journal of Green Energy, 18(15), 1584–1596. https://doi.org/10.1080/15435075.2021.1914630 Nguyen, H., Moayedi, H., Foong, L. K., Al Najjar, H. A. H., Jusoh, W. A. W., Rashid, A. S. A., & Jamali, J. (2020). Optimizing ANN models with PSO for predicting short building seismic response. Engineering with Computers, 36(3), 823–837. https://doi.org/10.1007/s00366-019-00733-0 Olden, J. D., & Jackson, D. A. (2002). Illuminating the “‘black box’”: a randomization approach for understanding variable contributions in artificial neural networks. In Ecological Modelling (Vol. 154). www.elsevier.com/locate/ecolmodel Putro, L. H. S., Budianta, D., Rohendi, D., & Rejo, A. (2020). Modeling methane emission of wastewater Anaerobic pond at Palm oil mill using radial basis function neural network. International Journal on Advanced Science, Engineering and Information Technology, 1, 260–268. https://doi.org/10.18517/ijaseit.10.1.9577 Ramachandran, A., Rustum, R., & Adeloye, A. J. (2019). Review of anaerobic digestion modeling and optimization using nature-inspired techniques. In Processes (Vol. 7, Issue 12). MDPI AG. https://doi.org/10.3390/PR7120953 Saghouri, M., Abdi, R., Ebrahimi-Nik, M., Rohani, A., & Maysami, M. (2020). Modeling and optimization of biomethane production from solid-state anaerobic co-digestion of organic fraction municipal solid waste and other co-substrates. Energy Sources, Part A: Recovery, Utilization and Environmental Effects. https://doi.org/10.1080/15567036.2020.1767728 Sathish, S., & Vivekanandan, S. (2016). Parametric optimization for floating drum anaerobic bio-digester using Response Surface Methodology and Artificial Neural Network. Alexandria Engineering Journal, 55(4), 3297–3307. https://doi.org/10.1016/j.aej.2016.08.010 Shroff, K. C., & Shah, N. G. (2023). The Performance Evaluation and Process Optimization of Anaerobic Co-digestion of OFMSW with Bio-flocculated Sludge from Secondary Settling Tank: A Key to Integrated Solid–Liquid Waste Management. Waste and Biomass Valorization. https://doi.org/10.1007/s12649-023-02176-7 Strik, D. P. B. T. B., Domnanovich, A. M., Zani, L., Braun, R., & Holubar, P. (2005). Prediction of trace compounds in biogas from anaerobic digestion using the MATLAB Neural Network Toolbox. Environmental Modelling and Software, 20(6), 803–810. https://doi.org/10.1016/j.envsoft.2004.09.006 Yang, J., Lu, L., Ouyang, W., Gou, Y., Chen, Y., Ma, H., Guo, J., & Fang, F. (2017). Estimation of kinetic parameters of an anaerobic digestion model using particle swarm optimization. Biochemical Engineering Journal, 120, 25–32. https://doi.org/10.1016/j.bej.2016.12.022 | ||
آمار تعداد مشاهده مقاله: 166 تعداد دریافت فایل اصل مقاله: 407 |