提出医院气动系统的新型数学模型

Büşra Takgil, R. Kara
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引用次数: 0

摘要

医院气动系统专业生产气动系统,是医院最基本的组件之一。它为医院内各种材料的运输问题提供了高效、经济的解决方案。然而,在现有的系统中,满足压缩空气的需求无需考虑成本控制,也不取决于运送的样本,这不仅使系统效率低下,还可能导致样本质量下降。本研究的主要目的是根据输送材料的类型提供速度/压力控制,以消除现有系统的缺点,如能源消耗和样品降解。本研究提出了一个新的数学模型,可用于制造更节能的医院气动系统。虽然文献中有很多关于各种气动系统的研究,但对医院气动系统控制的研究还不够。根据本研究获得的结果,确定了系统参数,并利用多元非线性回归法获得了系统的数学模型。利用遗传算法检验了所获数学模型的有效性,并优化了模型输入参数的系数。预计所提出的模型将有助于医院气动系统的使用,并为所提出的数学模型提供科学实用的解决方案。与目前使用的系统相比,所提出的数学模型的运输效率最高可提高 43%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Proposing a novel mathematical model for hospital pneumatic system
Hospital Pneumatic Systems, specializing in pneumatic systems, are among the most essential components for hospitals. It offers efficient and cost-effective solutions to problems related to the transportation of various materials in hospitals. However, in existing systems, the need for compressed air is met without worrying about cost control and without depending on the sample transported, and this not only makes the system inefficient but also may cause sample degradation. The main purpose of this study is to provide speed/pressure control according to the type of material transported to eliminate the disadvantages of existing systems such as energy use and sample degradation. In this study, a new mathematical model is presented that can be used to make more energy-efficient hospital pneumatic systems. Although there are many studies on various pneumatic systems in the literature, there is not enough for the control of hospital pneumatic systems. According to the results obtained in this study, the system parameters were determined and the mathematical model of the system was obtained by using the Multivariate nonlinear regression method. A genetic algorithm was used to test the validity of the obtained mathematical model and to optimize the coefficient of the input parameters of the model. It is expected that this proposed model will contribute to the use of hospital pneumatic systems and provide a scientific and practical solution to the proposed mathematical model. The proposed mathematical model provides up to %43 more efficient transportation over the currently used system that has been tested.
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