Establishment of a Drone waste water Treatment Plants by Remote Monitor System
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The effluent quality and operational performance of a wastewater treatment plant depend on the operational efficiency of the equipment used in the plant. Conventionally, general equipment maintenance in a wastewater treatment plant is often scheduled based on the maintenance staff's findings from their inspections and tests. In other words, equipment that fails to meet the requirements or has sustained damage is identified during inspections and tests and will be subsequently serviced. This maintenance method requires large amounts of time, manpower and material resources, from the detection of abnormal equipment to the completion of maintenance.
This study proposes an improved concept of maintenance and service that is capable of predicting which equipment requires maintenance and servicing in a plant, thereby reducing the probability of equipment malfunctions or failures. A wireless sensor network and cloud computing are used to effectively manage and monitor a wastewater treatment plant; the water levels, operational conditions and important water quality parameters in each treatment unit are continuously monitored, and the monitoring data are uploaded in a timely fashion to the database of a cloud monitoring and control platform. Whether an anomaly has occurred in the wastewater treatment system is determined by comparing the monitoring data with the critical water levels, abnormal equipment conditions and continuous water quality monitoring data that are stored in the intelligent database. When the equipment sends abnormal signals, the intelligent system will alert the operators, who will then implement proper treatments or adjustments to achieve predictive maintenance. The advantages of this system are that it can prevent water quality deterioration, prevent the effluent quality from exceeding the discharge standards as a result of equipment damage and shorten the duration of abnormal equipment operation. This predictive maintenance and management system can more effectively utilize the maintenance staff's time than other techniques. The intelligent cloud-computing predictive maintenance system and its advantages are demonstrated with a case study of the Rong Lake Wastewater Treatment Plant in Kinmen County.
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