## 1 Introduction
### Related Works
More recent proposals, such as those by [27; 28; 29], incorporate cognitive capabilities or predictive control mechanisms, yet their complexity hinders implementation in rural or low-resource contexts. In this context, the methodology proposed in this study represents a novel and necessary contribution. It is distinguished by its modular structure, low implementation cost, and customization capacity, validated in a real-world composting facility in Colombia. By integrating low-cost sensors, accessible modeling tools, and a continuous feedback scheme, this approach provides an effective and scalable alternative for sectors traditionally excluded from industrial digitalization. This work not only addresses a methodological gap identified in the literature but also offers a practical, adaptable, and replicable framework aligned with circular economic principles. Recent research has begun to explore the application of DT technologies in domains closely related to composting, revealing a growing interest in using virtual-physical systems for environmental monitoring and process optimization. For instance, ref. [30] introduced the concept of Circular DTs applied to climate-smart soil systems, integrating community composite networkswithin a UNESCO Geopark. Their approach emphasizes semantic interoperability and ontological modeling to simulate organic matter flows and environmental impact, offering a conceptual but not yet operational framework for circularity [30]. In the domain of wastewater treatment, ref. [20] conducted a comprehensive technical review, identifying major advances in real-time modeling, cyber-physical integration, and control strategies using DTs in activated sludge processes. They stress the importance of hybrid models (combining physics-based and data-driven approaches) and discuss challenges in sensor accuracy, standardization of interfaces, and lifecycle management of the twin itself [20]. Although rich in technical insight, their review focuses primarily on high-tech implementations with limited attention to cost-effectiveness or adaptability to decentralized systems. Similarly, ref. [31] proposed a DTs-based framework for solid waste collection optimization in smart cities, leveraging geospatial data and urban infrastructure models to address route planning and resource allocation. Their system relies on GIS-integrated virtual environments and simulation-based decision support tools, demonstrating potential benefits in efficiency and sustainability. However, the proposed model lacks a modular, replicable implementation framework and does not address biological waste transformation processes such as composting [31]. These studies collectively underscore the potential of DTs in environmental applications but also reveal a gap in field-validated, low-cost, and domain-specific methodologies for organic waste processing. The present study addresses this need by proposing and validating a DT framework tailored specifically to composting processes, with emphasis on  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Title of the Study** & **Main Focus/Contribution** & **Type of DT** \\ \hline \multirow{4}{*}{Advancing the Smart Region Digital Twin: The Case of UNESCO GEOfood [32]} & Proposed a digital twin-driven design for a community composite network in a UNESCO & \multirow{2}{*}{Conceptual model} \\  & & \\  & & \\ \cline{1-1}  
modularity, adaptability, and direct operational feedback. By integrating real-time sensing, simulation, and decision support, and demonstrating statistically validated improvements in process efficiency, this work contributes a practical model for sustainable waste management, suitable for broader adaptation in circular economy initiatives. To support this analysis, a systematic literature review was conducted using four academic databases: Scopus, ScienceDirect, IEEE Xplore, and SpringerLink.