Determination of the reliability of the automated system for organoleptic water quality

Water quality is essential for a healthy life, so it is necessary to look for technologies to measure its parameters in real time and automatically. The purpose of this study was to implement and determine the reliability of an automated system to evaluate the organoleptic water quality intended for human consumption, in the urban distribution network of the district of Daniel Hernandez (Peru), using a programmable logic controller (PLC) and Simulink. The study was carried out from January to March 2024, corresponding to the rainfall season. In the process of the research, a data acquisition and processing algorithm was implemented in a Simatic S7 1500 PLC with analog input module; using the national sanitation foundation water quality index (NSF-WQI) methodology. The work focused on five key water parameters: Potential hydrogen (pH), electrical conductivity (EC), turbidity, free chlorine (FCL) and temperature. The methodology included programming in contact language (KOP) of the algorithm for calculating subscripts for each parameter, according to the functions established by NSF-WQI. Measurements were performed with 4-wire sensing devices with 4–20 mA current signals, ensuring data accuracy. The interface to visualize the parameters and the water quality index was implemented in Simulink, communicating via OPC UA with the PLC server, facilitating the graphical representation of the organoleptic water quality index. The overall equipment efficiency (OEE) or automated system implemented was 90.56%, indicating its acceptable reliability for evaluating water quality. By performing the measurements, with the sensors of the five parameters immersed in tap water, at each of the three established sampling points (Dwelling_1, Dwelling_2 and Dwelling_3) along the water distribution network, the system facilitated the automated and real-time evaluation of the quality, resulting in an average NSF-WQI of 83.08%, classifying the water as good for human consumption. This information is important for water quality management and can guide future treatments to achieve better quality.