Prediction Modelling to Enhance Anaerobic Co-digestion Process of OFMSW and Bio-flocculated Sludge Using ANN | ||
| Pollution | ||
| دوره 10، شماره 1، بهار 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 | ||
|
| ||
|
آمار تعداد مشاهده مقاله: 850 تعداد دریافت فایل اصل مقاله: 715 |
||