## 4 Discussion
This setup proved effective even in a rural environment with limited resources and minimal technical expertise. However, the implementation also exposed some technological risks, such as sensor drift, data communication interruptions, and dependence on local connectivity infrastructure. These risks were mitigated through calibration protocols, standard operating procedures (SOPs), and redundancy in manual data verification. The conditions of success identified include the presence of at least a basic digital infrastructure, clearly defined performance indicators (KPIs), and personnel with the capacity to respond to system recommendations. Moreover, the ability to adapt the virtual layer dynamically and to simulate future scenarios in response to environmental or operational changes was critical in achieving the desired outcomes. These insights will be instrumental in future deployments of the methodology across other sectors. This would optimize system performance and ensure regulatory compliance. Potential benefits include lower energy consumption, improved treatment quality, and greater operational stability. Beyond these sectors, the methodology can be extended to food processing, recycling, industrial fermentation, and smart logistics, where continuous monitoring and simulation capabilities could support process optimization, traceability, and waste reduction. However, transferring the methodology to more complex or regulated environments involves specific challenges, including the development of domain-specific simulation models, integration of diverse data sources, training of specialized personnel, and compatibility with existing systems such as SCADA or LIMS. Even so, the associated benefits, such as greater operational resilience, process transparency, and data-driven decision support, justify its implementation in alignment with circular economy strategies and Industry 5.0 objectives. Regarding limitations, it is important to highlight that the methodology was tested in a single case study, which limits the generalizability of the results. Furthermore, the availability of technological infrastructure and initial costs may pose a barrier to its implementation in sectors with limited resources. The technical complexity of the setup could also present a challenge in environments with little experience in advanced technologies. Compared to methodologies like [17; 24], this approach provides higher adaptability to context-specific variables and lower technical implementation costs. While previous work focused on manufacturing lines, our proposal addresses environmental applications with high variability. In addition to the comparison with the methodology of [24], this study incorporates a broader comparative analysis with other relevant works in the field of waste treatment and DTs. For example, the methodology proposed by [17] focuses on data-driven modeling for production systems using invariant patterns, but it lacks a contextualized application to biological or waste-related processes and does not demonstrate real-world validation or economic impact [17]. Similarly, ref. [4] developed a digital portfolio tool for waste mitigation based on cognitive DTs, emphasizing portfolio management over real-time operational control. Their work, while conceptually rich, does not implement a closed-loop feedback system or show measurable improvements in process efficiency [4]. In contrast, our approach integrates physical sensors, simulation, and dynamic feedback loops, achieving a 10% increase in composing efficiency and over 1200 kg/month in additional product yield. Ref. [13] explored the use of DTs in waste systems using business process modeling. However, their implementation remains theoretical, lacking performance metrics and real-time interaction between physical and virtual environments [13]. The case study also adheres to the principles of ecological validity, as it was conducted in a real operational environment using low-cost technologies and non-specialized personnel.