## 3 Results
### Propositional Stage
These include environmental variables (e.g., ambient temperature and humidity), human operator variability, raw material heterogeneity, and sensor reliability. Each factor is associated with mitigation measures that were implemented to minimize its impact. This framework helps to contextualize the operational conditions of the study and supports the interpretation of the experimental results under realistic field constraints. Figure 5: Comparison of composting process performance with and without the use of DTs. This workflow diagram summarizes the main components and data flows of the proposed DT implementation (see Figure 6). The physical environment includes composoting piles and operators, which are monitored using low-cost sensors. Data is acquired via microcontrollers and transmitted to a cloud-based processing layer using wireless protocols. The virtual environment receives real-time data for simulation and decision support, while visualization dashboards enable human-in-the-loop supervision. Operational feedback is then applied manually or automatically, closing the control loop and enabling continuous optimization under real-world conditions. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**External Factor** & **Potential Impact** & **Mitigation Measures** \\ \hline Ambient temperature & \begin{tabular}{l} Affects the internal temperature of compost piles, \\ altering composite efficiency. \\ \end{tabular} & \begin{tabular}{l} Fixed-time sampling and \\ temperature logging with \\ calibrated sensors. \\ \end{tabular} \\ \hline Ambient humidity & \begin{tabular}{l} May change the moisture \\ content of piles, leading to \\ deviations in aeration or \\ decomposition. \\ \end{tabular} & \begin{tabular}{l} Daily inspection routines and \\ irrigation based on humidity \\ thresholds. \\ \end{tabular} \\ \hline Operator intervention & \begin{tabular}{l} Inconsistencies in watering, \\ turning, or data logging may \\ affect process reliability. \\ \end{tabular} & \begin{tabular}{l} Basic operator training and \\ implementation of standard \\ operating procedures (SOPs). \\ \end{tabular} \\ \hline Variability of organic material & \begin{tabular}{l} Differences in material \\ composition affect \\ homogeneity and \\ decomposition rate. \\ \end{tabular} & \begin{tabular}{l} Pre-mixing and pre-selection \\ of materials to reduce \\ variability. \\ \end{tabular} \\ \hline Sensor drift or wear & \
\begin{tabular}{l} Accumulated errors in \\ temperature and humidity \\ measurements reduce \\ analytical accuracy. \\ \end{tabular} &
\begin{tabular}{l} Initial calibration and regular \\ verification of sensor accuracy. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: External factors and mitigation measures. Figure 6: DT Lifecycle for organic waste management. The implementation of the DTs in the composting plant, with an initial investment of USD 50 covering low-cost hardware such as sensors and an Arduino board--resulted in a monthly production increase of 1200 kg of compost. This improvement translated into additional revenues of USD 789.9 per month, or approximately USD 9478.8 annually. The Return on Investment (ROI) was calculated using the standard formula:  
\[\text{ROI}=\left(\frac{\text{Additional Revenue}-\text{Implementation Cost}}{ \text{Implementation Cost}}\right)\times 100 \tag{1}\]  
Substituting the actual values:  
\[\text{ROI}=\left(\frac{9478.8\text{ USD}-50\text{ USD}}{50\text{ USD}}\right)\times 100=18,\!857.6\% \tag{2}\]  
The economic analysis conducted, focused on the calculation of Return on Investment (ROI), was intentionally simplified to demonstrate the viability of the model in contexts with limited access to structured financial and technological data. This approach aligns with the characteristics of the rural composting facility studied, where indirect costs and accounting processes are not formally digitalized.