One of the benefits of the supercritical CO ${}_2$ Brayton cycle in Sodium-cooled Fast Reactors is an enhanced plant safety, since potential reactions of CO ${}_2$ with liquid sodium have been reported to be less stringent than a sodium-water reaction found in the Rankine cycle. However, moderate chemical interactions between CO ${}_2$ and liquid sodium make detecting CO ${}_2$ ingress accidents harder. Thus, this paper proposes a new physics-based detection algorithm by comparing the real-time pressure measurements of two identical heat exchangers for the early detection. The CO ${}_2$ ingress occurs owing to a crack at the pressure boundary wall, a certain self-recovery of structural damage does not happen over time, and an accident probabilistically starts at only one component of two. The proposed physics-based method with the probabilistic analysis was compared to the pure data-based method. Finally, the damage degradation was developed with a simplified mass and energy transfer model, and the proposed algorithm was verified with experimental data. The results show that a 99.99% detection probability can be achieved for the air ingress of 30 cc/s, which is equivalent to the 0.12 g/s CO ${}_2$ ingress, in a 70 s detection time, limiting down to 0.1% false alarms due to sensor noise.

To protect our planet, the material recycling of domestic waste is necessary. Since the COVID-19 pandemic began, the volume of domestic waste has surged overwhelmingly, and many countries suffered from poor waste management. Increased demand for food delivery and online shopping led to a huge surge in plastic and paper waste which came from natural resources. To reduce the consumption of resources and protect the environment from pollution, such as that from landfills, waste should be recycled. One of precious recyclable materials from household waste is liquid cartons that are made of high-quality paper. To promote sustainable recycling, this paper proposes a vision-based inspection module based on convolutional neural networks via transfer learning (CNN-TL) for collecting liquid packaging cartons in the reverse vending machine (RVM). The RVM is an unmanned automatic waste collector, and thus it needs the intelligence to inspect whether a deposited item is acceptable or not. The whole processing algorithm for collecting cartons, including the inspection step, is presented. When the waste is inserted into the RVM by a user after scanning the barcode on the waste, it is relocated to the inspection module, and the item is weighed. To develop the inspector, an experimental set-up with a video camera was built for image data generation and preparation. Using the image data, the inspection agent was trained. To make a good selection for the model, 17 pretrained CNN models were evaluated, and DenseNet121 was selected. To access the performance of the cameras, four different types were also evaluated. With the same CNN model, this paper found the effect of the number of training epochs being set to 10, 100, and 500. In the results, the most accurate agent was the 500-epoch model, as expected. By using the RVM process logic with this model, the results showed that the accuracy of detection was over 99% (overall probability from three inspections), and the time to inspect one item was less than 2 s. In conclusion, the proposed model was verified for whether it would be applicable to the RVM, as it could distinguish liquid cartons from other types of paper waste.

In this paper, the design of a driving mechanism for a heavy-duty collaborative robot (cobot) capable of lifting payloads up to 20 kg is presented. This study focuses on an articulated robot utilizing a water-based Electro-Hydraulic Actuator (EHA). The Denavit–Hartenberg (D–H) representation was employed to relate the rotational angles and the end-effector’s location, facilitating the design of the actuators. The maximum required torques for joints 2 and 3, responsible for lifting for 12 s, were calculated under quasi-static and dynamic loading conditions. The results showed that the maximum required torques were 126.67 Nm and 58.86 Nm for joint 2 and 3, respectively. The maximum torque for joint 2 occurs when the pitch links are fully extended, whereas the maximum torque for joint 3 occurs when the third link is parallel to the ground. The torques, due to the inertia and Coriolis dynamic terms, were also calculated and found to be lower than those required for the gravitational term. Various maneuvering scenarios, along with Ansys Motion simulation, were analyzed for the verification of the results. Based on the calculated maximum torques, the linear actuators of the EHA were designed. The heavy-duty cobot can be built with the developed actuator proposed in this paper. The total weight of the entire frame was measured to be 14.59 kg, resulting in a high Payload/Weight (P/W) ratio of 1.37. In conclusion, the robot was made lighter and can operate more efficiently, particularly for heavy loads up to 20 kg.

This paper presents the application of a safety monitoring algorithm for collaborative robots (cobots). Cobots become increasingly common in various workplaces, such as manufacturing lines and even restaurant kitchens. The heavy-duty robots can assist human operators by handling heavy loads, reducing the physical strain of the job. However, safety concerns arise due to the close proximity and direct interaction between cobots and humans. To ensure real-time monitoring of potential accidents, this paper considers the use of a redundant fault detection algorithm. This algorithm relies on comparing outputs from redundant systems or sensor data with the actual robot's performance. Any significant discrepancy between these values signals a potential fault in the robot, triggering an accident alarm. In this study, the Gazebo simulator was employed as the redundant system. Gazebo is a widely-used, open-source 3D robotics simulator that integrates seamlessly with ROS (Robot Operating System). ROS 2, the next generation of ROS, serves as the middleware framework for building and executing robotic applications. The heavy-duty robot was modeled in Gazebo and controlled through ROS 2 nodes for communication and operation. A synthetic fault was introduced in one of the robot's joints, and another Gazebo simulation acted as the real system. Joint positions were monitored, and any deviation from normal operations signaled the presence of a fault. Consequently, integrating Gazebo with ROS 2 provides an effective analytical model to compare against real systems for safety monitoring purposes.