Sensors for Self-Diagnosis of Electrochemical Treatment Technologies

Abstract

Electrochemical oxidation appears to be a practical technology for decentralized treatment of wastewater in regions where sewer-based wastewater treatment is too expensive or otherwise impossible to construct and maintain.1 As of now, the control processes used by many systems rely principally on successive events that are time dependent and come with little level of monitoring but little feedback into the system process.2-3 One reason is the high cost of sensors and automation systems used in industrial installations cannot easily translate into household or public bathroom products for developing countries where capital expenditure is limited.Additionally, failures associated with onsite treatment technology have to be addressed otherwise the health and the environment of the users in direct contact would suffer. In the technology developed by Hoffmann et al. 3 , most repairs are simple, requiring only a screwdriver, yet without interactive instructions, local electricians and plumbers have been unable to get units properly serviced. From ethnography research, we also found the potential for service interruption is something we must eliminate for true user adoption of our sanitation solution.As an answer to this challenge, we integrated a suite of inexpensive sensors (e.g. ORP, color, turbidity, voltage, current, water level, leakage) in and around the electrochemical treatment system (picture attached) and to a local computer (Raspberry Pi + Arduino) that can diagnose failure modes and send alerts to on-demand repair technicians and users4. Our M2M, IoT technology uses a proprietary mobile application to deliver straight-forward, pictorial instructions that guide maintenance technicians regardless of literacy level.In this study we present the results from the field testing of the system “at scale” in two identical prototypes using electrochemical disinfection and treatment of toilet wastewater. The results from this study enable a refinement in the choice of sensors and the algorithms used to diagnose electrochemical water treatment quality and prevent system failures.