Modeling of pneumatic tube conveying systems and improved methods proposals

Pneumatic Systems, in which compressed air is used as an energy source, are used in almost every field of industry. With the developing technology, the need for these systems is increasing. Pneumatic transport systems are used in the healthcare field for the practical and safe transport of various samples between hospital units. However, a poorly designed system can cause inefficiency and sample deterioration such as hemolysis. In this context, the problems encountered in the existing systems were examined and studies were carried out to solve these problems. Artificial intelligence algorithms were used to make the system more efficient. To shorten the transport distance, which is one of the most important parameters affecting the efficiency of the system, the system is modeled with a graph structure and integrating the shortest path algorithms into the system, it is aimed to save energy and time in the transport system. To maintain uninterrupted and high-quality health services in hospitals, human resources, and other resources should be used effectively. To reduce the role of human resources in the system and provide advantages such as speed, cost, and ease of use, it was proposed to move the system to the coded labeling system and related studies were carried out. In addition, the hospital pneumatic system is modeled using artificial intelligence techniques such as fuzzy logic, ANFIS, and artificial neural networks. For the proposed model, first of all, the parameters affecting the performance of the system were determined, the data set of the model was created with these parameters, and experimental studies were carried out. In addition, the mathematical model of the system was obtained by using the Multivariable nonlinear regression method. All proposed models are expected to provide a scientific and practical solution to pressure modeling for hospital Pneumatic systems. It is also expected to contribute to hospital pneumatic systems in terms of industrial and electrical energy gain.