## 4 Discussion
### Gaps  
Although progress has been made on issues related to the implementation of a monitoring and early warning system to study the physical and chemical stability of TSFs, knowledge gaps remain in the mining sector concerning how to provide quality, reliable, real-time information to mining companies, communities, and authorities through an intuitive, stable platform to avoid the risks of failure and socio-environmental harm [105; 106; 107]. This must be addressed in the coming years to strengthen operational management, promote a culture of risk reduction, and improve communication between the responsible parties and emergency responders [108; 109]. Although TSF monitoring systems apply Industry 4.0 tools in the initial stage, there are relevant advances that could be adopted from related mineral processing facilities. [PERSON] et al. [110] address the issue of severe variability in the fresh feed ore flow rate in a crushing circuit, which can lead to operational problems such as conveyor belt and sieve overload, interlocked crushers, early equipment wear, and silo overflow. This work presents a digital twin approach, where a digital model interacts with a physical asset. A numerical simulation is performed to evaluate the effectiveness of simultaneously regulating feeder speed and gate aperture. Similarly, [PERSON] et al. [111] present a review of machine vision applications for monitoring froth flotation plants, specifically focusing on enhancing their automated control systems through the analysis of froth images. These AI-related applications are mainly based on convolutional neural networks (CNNs) and transfer learning, for which GoogleNet and MobileNet are highlighted architectures. Concerning flotation processes, [PERSON] et al. [112] developed an intelligent online instrument to recognize ore type and degree of fragmentation on conveyor belts. They did so by utilizing a 2D LiDAR sensor combined with machine learning (ML) techniques. Five ML models' performances were compared in a random forest (RF) analysis to identify the one that performed best. case studies on the synergy of sensors with DT technology. Identifying synergies between sensor technologies with DTs to monitor the safety of TSFs is an initial stage for such research; more studies of this approach are needed to generate models and simulations of different TSF construction and operation scenarios to identify the possible risks [39; 113; 114]. Cost information about using sensors to monitor TSFs is also scarce [32]. In addition, there are gaps regarding data centralization, i.e., the idea that all of the data generated by a mining operation could be made available on a centralized platform for interested stakeholders, which contrasts with the reality of some mining operations, where data are dispersed and stored on several computers and servers. Integrating and synchronizing data from different sources into a single control platform would allow them to be leveraged cleanly and directly from mining operators, construction contractors, engineering contractors, equipment and materials suppliers, authorities, and communities, as well as integrated with the operational data on all of the processes associated with the value chain. This is of great importance due to the complexity of the information handled in mining operations, which reinforces the urgent need to adopt data interoperability standards at the industry level [115]. When IoT approaches are applied to industrial automation and control systems, the concept of the industrial IoT (IIoT) arises [116]. Therefore, IoT applications for TSF management could be considered within the IIoT field. In the IIoT, security vulnerabilities could affect systems. These vulnerabilities are categorized into several groups, including inadequate authentication and authorization, broken access controls, network vulnerabilities, a lack of encryption, device firmware and software vulnerabilities, and insufficient logging and monitoring.