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Engineering  |  Engineering Projects in Community Service (EPICS)

33 Buckets – Chlorine Injection IoT

Chlorine disinfection is the most common water treatment method for inactivating bacterial contaminated water sources. However, it requires a precise dose (0.2 mg/L – 2.0 mg/L) to be effective against pathogens. Current methods of testing for residual chlorine in water are labor intensive, costly, and often inaccessible. They require the use of reagents to gauge chlorine levels colorimetrically. The difficulty in testing procedures for residual chlorine levels can cause improper chlorination in pools, industrial processes, and drinking water systems. The proposed solution is an autonomous residual chlorine sensor that utilizes electrochemistry to detect residual chlorine levels. The sensor measures the resistivity of a water sample using a stainless steel and platinum probe. The voltage reading is then converted into a residual chlorine level. The data is output to a user interface as well as transmitted to a network for remote monitoring. This process occurs automatically using only solar power. Our approach utilizes the human-centered design process. The need for this type of technology came as a result of an assessment trip to Cusco, Peru. Rural communities surrounding Cusco lack access to clean water causing water-borne illnesses, especially in children. The method of disinfection used in the assessed communities is liquid chlorine disinfection. While several factors contributed to the consumption of contaminated water, testing equipment and methodology for chlorine was a primary contributor to the ineffectiveness of disinfection. This completed the ‘Identify User Needs’ portion of the human-centered design process. At this point, we have developed a prototype for the sensor that is able to detect changes in the resistivity between the two probes when submerged in the water sample. Preliminary testing has shown that a difference in residual chlorine levels causes a difference in resistivity readings. More formal, data-driven experiments, are being designed. We are now in the design specification phase of the human-centered design process.