## 3 Results
### Propositional Stage
This type of integration not only optimizes operations by enabling real-time feedback and predictive decision-making but also improves resource efficiency and process consistency. For instance, dynamic control of aeration and irrigation based on sensor feedback reduced energy consumption and minimized the overuse of water. Additionally, the DT enabled early detection of deviations in temperature profiles, allowing for timely interventions that enhanced composting homogeneity and reduced batch variability. The overall structure of the DTs implemented in the composting facility is shown in Figure 4 (Section 2). This diagram summarizes the integration of the physical, cloud, and virtual layers, as well as their interaction with system operators. It also defines the timing of data acquisition and the corresponding actions triggered based on the measurements taken. Table 3 summarizes the technical configuration of the DT implemented for composting process monitoring and optimization. It presents the core system components, including sensor types, data acquisition frequency, operational thresholds, and modeled failure scenarios. The table also outlines the specific actions triggered by the DT system when defined thresholds are crossed or anomalies are detected. This structured representation complements the architectural description and provides insight into the practical functioning and responsiveness of the proposed methodology under field conditions. As illustrated, the physical layer captures real-time data through temperature and moisture sensors, which are then transmitted to the cloud layer for processing and storage. The virtual layer uses this data to simulate process behavior and generate predictions. The structure also highlights the role of human operators in executing system adjustments based on feedback from the DT. This bidirectional interaction between physical and digital components enables dynamic optimization of the composting process. The implementation of the DT increased composting efficiency from an average of 40% (35-43%) to 50% (40-54%), reducing variability and optimizing the process through real-time dynamic adjustments. The implementation of the DTs was evaluated over a 30-week period to gather performance data, which is presented in Figure 5. The performance reflects the weekly output measured at the time the products were packaged. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \multicolumn{2}{c}{**Aspect**} & \multicolumn{1}{c}{**Description**} \\ \hline DT Objectives & Optimize efficiency and homogeneity in the composting process in a high-capacity plant. & Real system Temperature and Humidity Sensors Operators managing the facility. \\ \cline{3-4} Architecture & Cloud Layer & Real-time data processing and storage; analysis using a simulation model. \\ \cline{3-4}  & Virtual Layer & Digital replica of the physical system; simulation of key parameters for prediction and dynamic adjustments. \\ \hline \multirow{4}{*}{Technical Resources Referenced} & Node-RED (data visualization and processing). & \multirow{2}{*}{Node-RED (data visualization and processing).} \\  & Arduino Uno. & \\ \cline{3-4}  & Stainless Steel Soil Moisture Sensor + LM393. & \\ \cline{3-4}  & DSI8B20 Temperature Sensor. & \\ \cline{3-4}  & Anylogic for process logic construction and data analysis. \\ \hline \end{tabular}
\end{table} 
Table 2: Description of the developed DT.