## 1 Introduction
### Context and Motivation
These technologies are increasingly integrated into waste-based agricultural systems, where compost and digestate serve as biofertilizers, contributing to sustainable nutrient cycling and reducing reliance on synthetic inputs. Recent research emphasizes the role of such organic amendments in promoting soil health, improving crop productivity, and advancing circular bioeconomy strategies in agriculture [10]. Driven by the rise of solid waste reduction policies, which aim to shift society's production and consumption patterns toward environmental sustainability, efforts are focused on improving material flow efficiency through waste utilization and its reintegration into the production cycle. These policies include the enforcement of stricter regulations on waste segregation at the source, incentives for reuse and recycling, and the promotion of clean technologies and more sustainable industrial processes. In response to these policies, companies must continue to play a leading role in contributing to the country's economic growth and development. This requires adopting practices that achieve greater efficiency while delivering high-quality products and services [11]. The Fourth Industrial Revolution (Industry 4.0) is currently integrating digital technologies with industrial processes to generate innovative solutions in manufacturing, enhancing its reliance on real-time feedback. As part of this revolution, the application of DTs has expanded beyond the manufacturing sector to a wide range of fields, including medical interventions and virus response, biomanufacturing, terrestrial system simulation, environmental monitoring, climate change mitigation, transportation, food processing and manufacturing, energy production, and urban planning and development [12]. Given this, the need has emerged to implement methodologies for improving production and service systems that, through the use of DTs, enhance productivity in the processes of transformation and utilization of organic waste. The concept of process DTs has been utilized to analyze and support waste management processes, specifically to assess performance costs and service quality in certain countries [13]. DTs is a term associated with Industry 4.0. DTs create a virtual model of a physical entity, simulate the behavior of the physical entity using data, and are characterized by real-time synchronization, accurate mapping, and high fidelity. These characteristics are achieved through real-virtual interaction feedback mechanisms, data fusion analysis, and decision iteration optimization, enabling the interaction and integration of the physical world and the information world. This integration enhances or extends new capabilities for the physical entity [14]. It can be established that NASA developed an early DTs model in the 1970s to simulate the behavior of Apollo 13 [15]. However, this concept was fully introduced in a presentation by [PERSON] in 2002, where he discussed how a virtual model of a product could significantly aid in its lifecycle management [14]. For the development of DTs, various methodologies exist, and each creation stage involves a different level of complexity. Authors such as [16] assert that the complexity and capability of DTs vary depending on the objectives of the DTs being developed. DT implementations in industrial systems can generally be classified into three categories: data-driven, model-driven, and hybrid approaches. The data-driven category relies on real-time data acquisition and machine learning models to infer system behavior, enabling rapid adaptation to changing conditions [17]. In contrast, model-driven DTs are based on theoretical or physics-based representations of the system, prioritizing system control and simulation accuracy [16]. Hybrid models combine both approaches, integrating sensor data with simulation models to enhance prediction capabilities and support complex decision-making processes [18].