## 1 Introduction
### Related Works  
In addition to conceptual developments, several real-world applications of DTs have recently emerged in waste-related domains. For instance, ref. [2] developed a sensor-driven DT for composting systems that monitored maturity stages and emissions in real time, demonstrating its potential for environmental control. Ref. [3] applied a DT framework to optimize biohydrogen production from agricultural waste, integrating supply chain simulation and carbon neutrality goals. Ref. [19] implemented a DT-based planning tool in grocery retail that reduced food waste by aligning inventory with predictive demand. These cases illustrate the growing feasibility of DTs in sectors characterized by biological variability, decentralized infrastructure, and sustainability demands. To synthesize this trend, Table 1 provides a structured overview of eight representative studies that applied DTs to composting, anaerobic digestion, waste sorting, agriculture, and wastewater treatment. These works underscore both the potential and the limitations of current DT implementations in environmental and low-tech contexts. They also reveal the lack of modular, cost-effective, and field-validated methodologies tailored to systems such as composting, which this study seeks to address. The development of DTs has revolutionized digital transformation in Industry 4.0 and Smart Manufacturing by providing digital replicas of physical systems for real-time analysis and optimization. These DTs enhance efficiency, reduce costs, and optimize processes, yet their effective implementation requires clear methodologies. Various methodologies focus on aspects such as scalability, interoperability, and adaptability to technologies like IIoT and Big Data. In manufacturing, DT modeling approaches are categorized into three groups: data-driven, model-based, and hybrid approaches that combine both [17]. Addressing DTs involves understanding their impact across a variety of sectors, including manufacturing, healthcare, energy, and infrastructure management. It also requires recognizing the challenges they face soon. Authors like [18] affirm that DT technology currently faces challenges like those encountered by Artificial Intelligence (AI) and the Internet of Things (IoT). By utilizing digital models of wastewater treatment structures, DTs analyze real-time data to predict and adjust their conditions [20]. As they evolve, DTs enhance environmental decision-making by effectively optimizing control, data utilization, and integration between the wastewater treatment industry and socio-environmental interactions [12]. This same concept can be adapted to the use of DTs in organic waste management. To date, few studies have explored the use of DTs in organic waste treatment. This work introduces an innovative methodological framework that enables real-time monitoring and predictive optimization of composting systems, an area underexplored in the literature but scalable to other production and service processes. Although the concept of DTs has been extensively explored in sectors such as manufacturing, energy, and healthcare, their application in organic waste treatment, particularly in composting processes, remains limited and underreported. Most existing methodologies, such as [21], the DTs Maturity Model (DTMM) [22], or the Asset Administration Shell (AAS) [23], are designed for highly digitized environments, focusing on standardization and interoperability. While robust in industrial settings, these frameworks face limitations when applied to systems characterized by biological variability and low technological infrastructure. Likewise, methodological approaches such as those proposed by [24; 25; 26] are systematic and well-structured but are predominantly oriented toward advanced manufacturing and require technical resources and personnel that are often unavailable in sectors like composting.