Modeling the Methane Production Kinetics of Anaerobic Co-Digestion of Agricultural Wastes Using Sigmoidal Functions

Rice straw co-digestion potential with cow dung and poultry droppings for maximizing biogas production in Bangladesh

Rice is the staple food of the people of Bangladesh. Burning and landfilling of carbon-rich rice straw (RS) causes greenhouse gas emissions. On the other hand, cow dung (CD) and poultry droppings (PD) produced from the livestock sector in Bangladesh could be a potential threat to the environment if the wastes are not properly managed. However, anaerobic co-digestion of RS with CD and PD could be an effective means of biogas generation. Therefore, co-digestion of CD and PD with carbon-rich RS was conducted in batch assay at seven different mixing ratios (100:0, 90:10, 70:30, 50:50, 30:70, 10:90, 0:100) separately. Mesophilic condition (35 °C) was maintained for 92 days of digestion time to investigate biogas production potential and find out optimal mixing ratios of both co-digestion sets. Co-digestion of CD and RS at 70:30 ratio significantly showed maximum biogas yield (441.7 ± 54.1 ml/gVS). Additionally, an increase in biogas yield in this ratio was 212.11 % and 38.10 % compared to mono-digestion of CD and RS, respectively. Another co-digestion set of PD with RS showed highest biogas yield (344.8 ± 22.3 ml/gVS) at 90:10 ratio. The 90:10 ratio of PD and RS improved biogas yield by 173.16 % and 7.8 % as compared to mono-digestion of PD and RS, respectively. Co-digestion of RS with CD and PD had a statistically significant effect (P ≤ 0.05) on biogas production. Furthermore, kinetic modelling outcomes suggested the modified Gompertz model as ideal for forecasting biomethane production over time in both cases. The findings of this study will help in the implementation of ACoD at the field level.

Predicting alfalfa leaf area index by non-linear models and deep learning models

Leaf area index (LAI) of alfalfa is a crucial indicator of its growth status and a predictor of yield. The LAI of alfalfa is influenced by environmental factors, and the limitations of non-linear models in integrating these factors affect the accuracy of LAI predictions. This study explores the potential of classical non-linear models and deep learning for predicting alfalfa LAI. Initially, Logistic, Gompertz, and Richards models were developed based on growth days to assess the applicability of nonlinear models for LAI prediction of alfalfa. In contrast, this study combines environmental factors such as temperature and soil moisture, and proposes a time series prediction model based on mutation point detection method and encoder-attention-decoder BiLSTM network (TMEAD-BiLSTM). The model's performance was analyzed and evaluated against LAI data from different years and cuts. The results indicate that the TMEAD-BiLSTM model achieved the highest prediction accuracy (R² > 0.99), while the non-linear models exhibited lower accuracy (R² > 0.78). The TMEAD-BiLSTM model overcomes the limitations of nonlinear models in integrating environmental factors, enabling rapid and accurate predictions of alfalfa LAI, which can provide valuable references for alfalfa growth monitoring and the establishment of field management practices.