A remote monitoring method, a system and a storage medium for a sewage treatment process are provided. The remote monitoring method for the sewage treatment process includes steps of: collecting sensor data with a sewage treatment data collection platform, wherein the sewage treatment data collection platform has at least one sensor for collecting sewage data; establishing an abnormal situation detection platform with a deep learning technology to detect an abnormal situation of the sensor data, and raising an alarm if the abnormal situation occurs; establishing an abnormal situation diagnosis platform with the deep learning technology to diagnose the detected abnormal situation, so as to determine a type of abnormal situation; and using the sensor data to optimize and control parameters of the sewage treatment process based on the deep learning technology.

A wastewater treatment management system including a plurality of monitoring stations, a treating station for introducing a treating agent to wastewater, and a principal processing facility for controlling a dose of the treating agent. A system configured to treat a wastewater stream collection system including a source of a treating agent, a metering valve, a sensor, and a controller operatively connected to the metering valve and the sensor. A non-transitory computer-readable medium including instruction that instruct a controller to perform a method of controlling addition of a treating agent into a wastewater stream collection system. A controller for a system configured to treat odor and control corrosion in a wastewater stream collection system that is operatively connectable to a metering valve for administering a treating agent to a wastewater stream collection system.

A cooperative learning system usable for process monitoring in which a monitoring model is provided for each of plural processes arranged in chronological order at predetermined transition time period intervals. The system stores, in chronological order, first monitoring data in a first process, second monitoring data in a second process, and at least one monitoring result from the first process output from a first monitoring model using the first monitoring data as an input parameter. The system performs parent model learning processing for the first monitoring model using the first monitoring data and the monitoring result from the first monitoring model, and performs child model learning processing for a second monitoring model using a monitoring result from the first monitoring model at a first time as teacher data and using the second monitoring data at a second time shifted from the first time by a transition period as an input parameter.

Apparatus, systems, and methods to monitor and control operation of an aquatic facility comprising a water basin, a water supply subsystem, and other subsystems. A simplified, centralized, scalable control subsystem comprises a base controller including with inputs and outputs and a human-machine interface. Sensors are operatively connected to the base controller and adapted to directly or indirectly sense one of a pre-selected set of parameters related to the operation of the aquatic facility. Actuators are operatively connected to the base controller and adapted to directly or indirectly actuate one of a pre-selected set of operations of the aquatic facility. The base controller is programmable relative to setpoints or other operational criteria of the aquatic facility; and actuation of at least a base subset of the actuators and graphical representation of the facility and the water supply, and the at least one subsystem, and the pre-selected operations of the aquatic system.

A data-knowledge driven multi-objective optimal control method for municipal wastewater treatment process belongs to the field of wastewater treatment. To balance the energy consumption and effluent quality, a data driven multi-objective optimization model, including energy consumption model and effluent quality model are established to obtain the nonlinear relationship along energy consumption, effluent quality and manipulated variables. Meanwhile, a multi-objective particle swarm optimization algorithm, based on evolutionary knowledge, is proposed to optimize the set-points of nitrate nitrogen and dissolved oxygen. Moreover, the proportional integral differential (PID) controller is designed to track the set-points. Then the effluent quality can be improved and the energy consumption can be reduced.

A system and method of managing water in an evaporative cooling system includes one or a combination of three components. A first component is to purify incoming water to the target quality. A second component is to purify condenser loop water to the target quality. The choice of the technology may depend on the site specific environmental conditions determining the quality of the recirculated condenser water and may include (but not limited to) filtration allowing for meeting target quality parameters, such as ultrafiltration, microfiltration, etc. A third component is to provide protection for the condenser loop hardware, preventing or reducing rate of corrosion, fouling, and scaling by adding chemicals to water in the system. The choice of the chemistry will depend on the site specific environmental and other system operating conditions

A method for operating a water treatment plant, wherein the plant is equipped with an apparatus for injecting a gas containing oxygen into an effluent, the method comprising a phase of detecting anomalies in the operation of the apparatus, wherein the phase of detecting anomalies comprises an implementation of the following steps: providing data representative of the operating state of the apparatus; and providing a system for acquiring and processing said data.

A method for operating a water treatment plant comprises a phase of detecting anomalies in the operation of the plant, wherein the phase of detecting anomalies comprises an implementation of the following measures: providing data representative of the operating state of the plant, said data being provided by sensors installed at selected locations in the plant itself or on input or output pipes of the plant; where appropriate, providing additional data; and providing a system for acquiring and processing these data, this system being equipped with an algorithm for processing said data.

A water treatment system includes an equipment system including each water treatment device that executes a water treatment process and a control apparatus that controls each water treatment device, and a management system including a management apparatus that determines a control content of the water treatment process by using a virtual system obtained by virtualizing each water treatment device and controls the water treatment process of the equipment system based on the control content via the control apparatus of the equipment system.

A cooperative learning system usable for process monitoring in which a monitoring model is provided for each of plural processes arranged in chronological order at predetermined transition time period intervals. The system stores, in chronological order, first monitoring data in a first process, second monitoring data in a second process, and at least one monitoring result from the first process output from a first monitoring model using the first monitoring data as an input parameter. The system performs parent model learning processing for the first monitoring model using the first monitoring data and the monitoring result from the first monitoring model, and performs child model learning processing for a second monitoring model using a monitoring result from the first monitoring model at a first time as teacher data and using the second monitoring data at a second time shifted from the first time by a transition period as an input parameter.