Dynamic simulation provides an efficient approach for improving the efficiency of parabolic trough power plants and control circuits. In the dynamic simulation, the possibilities and operating conditions of the plant are evaluated regarding materials, processes, emissions, or economics. Several studies related to the dynamic simulation of the parabolic trough technology are summarised and discussed in this work. This study is the first research that presents a thorough description of the advanced control circuits used in the solar field and thermal storage system of a parabolic trough power plant. This power plant was implemented using advanced process simulation software (APROS). The dynamic model was built based on the real specifications of the power plant.

The common design of thermal power plants is fundamentally oriented towards achieving a high-process performance, with market demands necessitating enhanced operational stability as a result of ongoing global support for renewable energy sources. Indeed, dynamic simulation represents one useful and cost-effective choice for optimizing the flexibility of parabolic trough power plants (PTPP) in a range of transient operating conditions, such as weather changes, resulting again in variations of the output load as well as varying start-up times. The purpose of this review is to provide an overview of steady-state and dynamic modelling for PTPP design, development, and optimization. This gives us a greater opportunity for a broad understanding of the PTPPs subjected to a variety of irradiance solar constraints. The most important features of the steady-state and their uses are reviewed, and the most important programs used in steady-state modelling are also highlighted. In addition, the start-up process of the plant, thermal storage system capacities and response dynamics (charging and discharging modes), and yearly electricity yield can be analyzed using dynamic modelling. Depending on the dynamic simulation, specific uses can be realized, including control loop optimization, load estimation for critical in-service equipment, and emergency safety assessment of power plants in the event of an outage. Based on this review, a detailed overview of the dynamic simulation of PTPP, and its development and application in various simulation programs, is presented. Here, a survey of computational dynamic modelling software commonly applied for commercial and academic applications is performed, accompanied by various sample models of simulation programs such as APROS, DYNAMICS, DYMOLA, and ASPEN PLUS. The simulation programs generally depend on the conservation equations of mass, momentum, species, and energy. However, for the equation of equilibrium, specific mathematical expressions rely on the basic flow model. The essential flow models involved, together with the basic assumptions, are presented, and are supplemented through a general survey covering popular simulation programs. Various previous research on the dynamic simulation of the PTPP are reviewed and analyzed in this paper. Here, several studies in the literature regarding the dynamic simulation of the PTPP are addressed and analyzed. Specific consideration is given to the studies including model verification, in order to explore the effect of modelling assumptions regarding the simulation outputs.

Mobile robots play a crucial role in Industry 4.0, particularly in dynamic and unstructured environments where moving obstacles present significant challenges. This study applies the YOLOv5 object detection algorithm to enhance robotic perception and obstacle avoidance. The primary objective is to improve the accuracy and speed of object detection in real-time scenarios, ensuring safer and more efficient navigation for robots. The research contribution lies in developing a lightweight YOLOv5 model optimised for robotic applications, capable of detecting objects with high accuracy. The model was trained on a diverse dataset of 10,700 images, including static and dynamic objects such as chairs, fans, fire extinguishers, and humans, captured under various conditions and orientations. The dataset was divided into training (70%), validation (15%), and testing (15%) subsets. The proposed model achieved a mean average precision (mAP) of 0.73 at a confidence threshold of 0.374, demonstrating superior performance compared to the YOLOv4 model in terms of accuracy and processing speed. Notably, the model excelled in detecting static objects such as chairs, achieving a perfect recognition rate of 1.00, while encountering challenges with moving objects such as humans due to motion blur and rapid changes in body posture. These findings highlight the model’s potential for real-time applications in industrial and unstructured environments. In conclusion, this study demonstrates that the enhanced YOLOv5 model significantly improves object detection and collision avoidance capabilities in robotic systems.

Additive manufacturing is driving a significant change in industry, extending beyond prototyping to the inclusion of printed parts in final designs. Stereolithography (SLA) is a polymerization technique valued for producing highly detailed parts with smooth surface finishes. This study presents a hybrid intelligent framework for modeling and optimizing the SLA 3D printer process’s parameters for Acrylonitrile Butadiene Styrene (ABS) photopolymer parts. The nonlinear relationships between the process’s parameters (Orientation, Lifting Speed, Lifting Distance, Exposure Time) and multiple performance characteristics (ultimate tensile strength, yield strength, modulus of elasticity, Shore D hardness, and surface roughness), which represent complex relationships, were investigated. A Taguchi design of the experiment with an L18 orthogonal array was employed as an efficient experimental design. A novel hybrid fuzzy logic–Particle Swarm Optimization (PSO) algorithm, ARGOS (Adaptive Rule Generation with Optimized Structure), was developed to automatically generate high-accuracy Mamdani-type fuzzy inference systems (FISs) from experimental data. The algorithm starts by customizing Modified Learn From Example (MLFE) to create an initial FIS. Subsequently, the generated FIS is tuned using PSO to develop and enhance predictive accuracy. The ARGOS models provided excellent performances, achieving correlation coefficients (R²) exceeding 0.9999 for all five output responses. Once the FISs were tuned, a multi-objective optimization was carried out based on the weighted sum method. This step helped to identify a well-balanced set of parameters that optimizes the key qualities of the printed parts, ensuring that the results are not just mathematically ideal, but also genuinely helpful for real-world manufacturing. The results showed that the proposed hybrid approach is a robust and highly accurate method for the modeling and multi-objective optimization of the SLA 3D process.

Low-Cost Myoelectric Prosthetic Hand Design and Fabrication