# Seasonal Shifts and Smart Stats: Improving Biodrying in Waste Management
## 5 Discussion
### Influence of Amount of Waste Loaded into the Bioboxes on Drying Efficiency  
The moderate positive correlation (r = 0.41) between the amount of waste filled into the bioboxes and drying efficiency (Figure 4) is a noteworthy finding. This relationship suggests that larger tonnages of waste loaded into the bioboxes tend to achieve slightly higher drying efficiencies. These findings are in agreement with the study by [PERSON] et al., which also found that the efficiency of the biodrying process is influenced by various geotechnical factors, such as the initial moisture content, organic content, bulk density, dry density, particle density, and porosity [30]. One possible explanation for the observed finding is that the biological component of municipal solid waste (MSW) has a relatively high density [31]. Consequently, bioboxes loaded with higher-density material may contain a greater proportion of biological material. This results in higher temperatures during aerobic degradation, which in turn enhances convective drying of the material [32; 33; 34]. This observation aligns with the findings of [PERSON] et. al [3], who noted that the scale of operation could influence biodrying performance. The statistical significance of this correlation (\(p<0.00001\)) underscores its importance in process optimization. Waste management facilities might consider optimizing the loading of bioboxes to maximize efficiency, balancing this against other operational constraints and safety considerations.  
The multiple linear regression model developed to predict drying efficiency revealed several important insights into the biodrying process. With an R-squared value of 0.25, the model explains 25% of the variability in drying efficiency, indicating that, while it captures some key factors, there are likely additional variables influencing the process that were not included in this analysis. Among the predictors, the amount of waste loaded into the bioboxes had the strongest positive influence, with a coefficient of 0.46. This suggests that, for every additional ton of waste filled, drying efficiency increased by approximately 0.46%, aligning with the earlier observed moderate positive correlation (r = 0.41) between waste load and efficiency. There are several other studies that have also analyzed the influence of different parameters on the biological treatment of waste (anaerobic digestion and biodrying), but they did not specifically address the effect of bulk densities of waste on biodrying efficiencies [35; 36]. The model also captured a slight negative impact of the \"Month\" variable (\(-0.36\)), supporting the observed seasonal variations in efficiency. Interestingly, the biobox number had a negligible negative influence (\(-0.034\)), confirming the earlier finding of consistent performance across the different bioboxes. While the model provides valuable insights, its relatively low explanatory power suggests that other factors, such as waste composition, ambient temperature, humidity, and microbial activity, may have a more significant impact on the biodrying process [21; 23; 26; 37]. Future research should aim to incorporate these additional variables in order to develop a more comprehensive predictive model for optimizing biodrying operations.