A Review of Automation and Sensors: Parameter Control of Thermal Treatments for Electrical Power Generation.

This review delves into the critical role of automation and sensor technologies in optimizing parameters for thermal treatments within electrical power generation. The demand for efficient and sustainable power generation has led to a significant reliance on thermal treatments in power plants. However, ensuring precise control over these treatments remains challenging, necessitating the integration of advanced automation and sensor systems. This paper evaluates the pivotal aspects of automation, emphasizing its capacity to streamline operations, enhance safety, and optimize energy efficiency in thermal treatment processes. Additionally, it highlights the indispensable role of sensors in monitoring and regulating crucial parameters, such as temperature, pressure, and flow rates. These sensors enable real-time data acquisition, facilitating immediate adjustments to maintain optimal operating conditions and prevent system failures. It explores the recent technological advancements, including machine learning algorithms and IoT integration, which have revolutionized automation and sensor capabilities in thermal treatment control. Incorporating these innovations has significantly improved the precision and adaptability of control systems, resulting in heightened performance and reduced environmental impact. This review underscores the imperative nature of automation and sensor technologies in thermal treatments for electrical power generation, emphasizing their pivotal role in enhancing operational efficiency, ensuring reliability, and advancing sustainability in power generation processes.

Reduction of synchronous reluctance motor currents with minimization of direct and cross saturation magnetic model.

In this paper, two cost functions are presented which are minimized to provide the optimized reference currents for the controlled drive of the synchronous reluctance motor. Since the synchronous reluctance motor operation is divided into two operating regions: one up to rated speed (0-1740 rpm), known as constant torque region and variable power, and other above this speed, called field weakening region (1740-2000 rpm), each of the cost functions proposals is appropriate for a specific region. The optimization problem is solved by the method of Lagrange Multiplier which allows finding the minimum value of a function with one or more constraints. The tests show a reduction in the motor current value of around 2%.

Benchmark for peak detection algorithms in fiber Bragg grating interrogation and a new neural network for its performance improvement.

This paper presents a benchmark for peak detection algorithms employed in fiber Bragg grating spectrometric interrogation systems. The accuracy, precision, and computational performance of currently used algorithms and those of a new proposed artificial neural network algorithm are compared. Centroid and gaussian fitting algorithms are shown to have the highest precision but produce systematic errors that depend on the FBG refractive index modulation profile. The proposed neural network displays relatively good precision with reduced systematic errors and improved computational performance when compared to other networks. Additionally, suitable algorithms may be chosen with the general guidelines presented.