A method includes supplying a plurality of generators, each generator in electrical communication with a switchgear with each switchgear in data communication with a generator data management system. The method also includes supplying a plurality of electrically driven fracturing pumps with each electrically driven fracturing pump in data communication with pump data management system. Further, the method includes supplying a load shedding system, the load shedding system in data communication with the generator data management system and a pump control system, the pump control system in data communication with the pump data management system. The method includes determining which pumps should have speed reduced by the load shedding system and reducing the speed of the pumps determined by the load shedding system using the pump control system.

A hardware (e.g., a process and/or circuitry) and/or software-based controller for septic system grinder or chopper units monitors load to the unit. As the unit encounters debris, electrical and mechanical load on the unit increases. In some cases, a signal to stop the unit from grinding/chopping (including but not limited to a signal based a waste-water level) is received while the unit is under the increased load. The controller may override the stop signal and continue sending power to the unit to clear the unit from debris causing increased load. The override may cease when it is determined that the debris has been cleared, such as when it is determined that the increased mechanical and/or electrical load on the unit has decreased or returned to a target load, allowing the unit to stop. The unit may include self-configuration functionality, determining thresholds for signaling override, target load, etc.

A motor of an electric submersible pump (ESP) is positioned in a wellbore. Measured data is received from one or more sensors. A first deep learning model running on a motor controller of the ESP determines first operating parameters or first operating conditions for the ESP based on the measured data. The motor controller sends the first operating parameters or first operating conditions to a centralized computer system. A second deep learning model running on the centralized computer system determines second operating parameters or second operating conditions associated with the ESP based on the first operating parameters or first operating conditions. The centralized computer system sends the second operating parameters or second operating conditions to the motor controller. The motor controller adjusts operation of the motor of the ESP based on the second operating parameters or second operating conditions.

A variable displacement pump for supplying fluid to a system is described. Controlling the variable displacement pump is determined based upon inputs from a fluidic pressure sensor and an accelerometer, and includes determining a desired fluidic pressure and monitoring, via the fluidic pressure sensor, an actual fluidic pressure. A pressure error term is determined based upon a difference between the actual fluidic pressure and the desired fluidic pressure. A time-integrated pressure error term is determined based upon the pressure error term, and a g-force is determined based upon an input signal from the accelerometer. The variable displacement pump is controlled in response to the time-integrated pressure error term when the g-force is greater than a threshold g-force.

An aid device, for manually emptying contents of a tube, including: a housing, a clamp, coupled to the housing and configured to secure a position of the tube in relation to the housing, wherein the tube includes a plunger configured to slide within the tube between an inlet end of the tube and an outlet end of the tube, a pusher configured to abut the plunger, and a lever pivotally coupled to the pusher by a first rotational axis such that the lever is continuously rotatable between an open configuration and a closed configuration about the first rotational axis, thereby facilitating a continuous movement of the pusher from the open configuration to the closed configuration, wherein the clamp is configured to secure the position of the tube such that the plunger of the tube is positioned between the outlet end of the tube and the pusher.

An example system includes a fluid end having a block, a fluid inlet formed in the block, and a fluid outlet formed in the block. The system also includes an intake manifold fluidly coupled to the fluid inlet, and a fluid conduit fluidly coupled to the fluid outlet and the intake manifold. The system further includes a valve fluidly coupled to the fluid conduit, the valve configured to control fluid flow through the fluid conduit, an actuator coupled to the valve and configured to position the valve in an open position or a closed position, and a controller communicatively coupled to the actuator and configured to send one or more signals to the actuator, causing the actuator to position the valve in the open position or the closed position.

Disclosed are a hydraulic system, a control method and a working machine. The hydraulic system includes an oil tank, a working pump, a control cylinder controlling a displacement changing mechanism, a working oil line communicating with an output main path of the working pump, a load-sensing system including a load-sensing circuit outputting hydraulic pressure based on load, a load-sensing valve and a switching-control valve switchable between a first-valve-position and a second-valve-position. When differential pressure between the first and second control ports is greater than a first threshold, the output main path communicates with an input port of the control cylinder; when it is less than the first threshold, the input port communicates with an oil return port of the oil tank. Based on this, operators can switch the hydraulic system between the fixed-displacement mode and the fixed/variable-displacement mode, thereby achieving a better balance between energy conservation and performance.

Systems and methods for a compressor system are provided for selectively operating the compressor system with reduced power consumption. An output demand level of the compressor system can be monitored over time to identify a demand profile. A pressure setpoint for the compressor system can be selectively adjusted based on the demand profile, including to selectively transition between an operational pressure setpoint for an active demand profile and a reduced pressure setpoint for an inactive demand profile.

A method for operating a plurality of geographically distributed compressors, wherein the outputs of the geographically distributed compressors are coupled to a compressed air distribution system within an industrial automation environment, is provided. The method includes receiving performance data from the plurality of compressors, and receiving current environment data from a plurality of sensors within the industrial automation environment, including at least some sensors within the compressed air distribution system. The method also includes assigning a guide vane weight to each compressor based at least in part on a capacity of each compressor, identifying a target system air pressure, and processing the performance data, current environment data, guide vane weights, and target system air pressure to determine control settings for each of the plurality of compressors.

The disclosed embodiments include pump systems and methods to improve pump load predictions of pumps. The method includes determining, in a neural network, a pump load of a wellbore pump based on a physics based model of the pump load of the wellbore pump. The method also includes obtaining a measured pump load of the wellbore pump. After initiation of a pump cycle of the wellbore pump, the method further includes predicting a pump load of the wellbore pump based on the physics based model, performing a Bayesian Optimization to reduce a difference between a predicted pump load and the measured pump load to less than a threshold value, and improving a prediction of the pump load based on the Bayesian Optimization.