Technology Center (CT), Postgraduate Program in Mechanical Engineering (PPGEM), Campus I, Federal University of Paraiba (UFPB), Joao Pessoa 58058-600, PB, Brazil.
Automation Coordination (CAUT), Federal Institute of Pernambuco (IFPE), Ipojuca 55590-000, PE, Brazil.
Sensors (Basel). 2022 Apr 18;22(8):3084. doi: 10.3390/s22083084.
A water supply system is considered an essential service to the population as it is about providing an essential good for life. This system typically consists of several sensors, transducers, pumps, etc., and some of these elements have high costs and/or complex installation. The indirect measurement of a quantity can be used to obtain a desired variable, dispensing with the use of a specific sensor in the plant. Among the contributions of this technique is the design of the pressure controller using the adaptive control, as well as the use of an artificial neural network for the construction of nonlinear models using inherent system parameters such as pressure, engine rotation frequency and control valve angle, with the purpose of estimating the flow. Among the various contributions of the research, we can highlight the suppression in the acquisition of physical flow meters, the elimination of physical installation and others. The validation was carried out through tests in an experimental bench located in the Laboratory of Energy and Hydraulic Efficiency in Sanitation of the Federal University of Paraiba. The results of the soft sensor were compared with those of an electromagnetic flux sensor, obtaining a maximum error of 10%.
供水系统被认为是向民众提供基本生活必需品的一项基本服务。该系统通常由多个传感器、换能器、泵等组成，其中一些元件成本高且/或安装复杂。通过间接测量某个量，可以获得所需的变量，从而在工厂中无需使用特定的传感器。该技术的贡献之一是使用自适应控制设计压力控制器，以及使用人工神经网络构建使用固有系统参数（如压力、发动机旋转频率和控制阀角度）的非线性模型，以估算流量。在研究的众多贡献中，我们可以突出强调在获取物理流量计、消除物理安装等方面的抑制作用。通过位于帕拉伊巴联邦大学卫生能源与水力效率实验室的实验台上的测试进行了验证。软传感器的结果与电磁流量传感器的结果进行了比较，最大误差为 10%。
