Abstract
In response to intense competition, the pulp and paper industry is striving for greater efficiency and cost reduction by aiming for a more flexible production schedule. Moreover, the industry is prioritizing meeting global market demands for higher product quality, including more specialized items, while also enhancing productivity and environmental sustainability. Consequently, extensive research has been conducted to improve existing processes, with a growing focus on utilizing intelligent systems to control and optimize operations, presenting an appealing alternative for industry advancement. In a substation context, unlike traditional electrical equipment failures, malfunctions in Ethernet-style cyber components can disrupt operations, emphasizing the need for reliability. Selecting the appropriate Supervisory Control and Data Acquisition (SAS) package can mitigate the risk of widespread computer failures, ensuring compatibility across different vendors through the IEC-61850 protocol. This protocol has facilitated interoperability among SAS components, enabling a diverse range of manufacturers to contribute to the system's reliability. The naming conventions for neural networks and their characteristics vary. They typically involve rows and columns. Simple illustrations might include control scenarios, while applications are often tailored to specific systems in advance. Advanced humanoid robots, frequently employed for control tasks, embody human-like intelligence in robotics through algorithmic frameworks. They integrate fundamental control principles and strive to incorporate innovative concepts, reflecting ongoing efforts in the field of robotics. Upgrading the current traffic light system is made convenient by adopting an economic approach to infrastructure enhancement. A control system is established through an intermediate hardware device. This device serves as the signal controller, receiving input from the hardware and executing real-time operations accordingly. It effectively manages traffic light signals, addressing any violations and optimizing control through integrated hardware and software toolboxes within the device. This paper presents optional alternatives, which are assessed according to their deviation from the average solution. The mean solution, calculated through arithmetic mean, serves as the benchmark. The EDAS method, characterized by randomness, determines the mean solution. Proficiency in problem-solving is essential for implementing this method. Each criterion in the paper evaluates the performance of alternatives, assuming a normal distribution. To address these issues, a reliable EDAS method is introduced. This study introduces a hybrid method, combining Multi-Criteria Decision Making (MCTM) approaches with the proposed method. It addresses various criteria involved in decision-making processes. While MCTM methods are acknowledged for their quality, the thesis presents a hybrid solution. The research focuses on resolving material selection challenges in industrial applications through the Employment of Distance-based Assessment (EDAS). The methodology involves employing Design of Experiments (DOE) and EDAS to identify crucial material selection criteria and align them with the corresponding contexts. This process utilizes polynomial fitting to experimental data within multiple linear regression analysis. Burning zone temperature is got the first rank whereas is the Cold-end temperature is having the Lowest rank.