A model predictive control system is used to optimize energy cost in a variable refrigerant flow (VRF) system. The VRF system includes an outdoor subsystem and a plurality of indoor subsystems. The model predictive control system includes a high-level model predictive controller (MPC) and a plurality of low-level indoor MPCs. The high-level MPC performs a high-level optimization to generate an optimal indoor subsystem load profile for each of the plurality of indoor subsystems. The optimal indoor subsystem load profiles optimize energy cost. Each of the low-level indoor MPCs performs a low-level optimization to generate optimal indoor setpoints for one or more indoor VRF units of the corresponding indoor subsystem. The indoor setpoints can include temperature setpoints and/or refrigerant flow setpoints for the indoor VRF units.

A system and method for cleaning of heat exchanger tubes including an assembly, an indexer, and a communication device provided with specialized software and programming. The indexer includes orthogonally arranged first and second arms. A trolley and sensors are provided on the indexer arms. One or more lances are provided on the trolley to deliver water jets into the openings. Sensors measure displacement as the trolley is moved relative to the heat exchanger's face plate. An operator controls the system from a distance away using the communication device. During setup, the pattern of the face plate is learned and mapped utilizing information from the sensors as one of the inputs. This information is utilized to help navigate the face plate during a subsequent cleaning operation. A kit for retrofitting existing X-Y indexers is also disclosed.

A flow meter, and related system and method are provided. The flow meter includes a coupler, a support member, an image sensor, a valve, and one or more processors. The coupler is adapted to couple to a drip chamber. The support member is operatively coupled to the coupler. The image sensor has a field of view and is operatively coupled to the support member. The image sensor is positioned to view the drip chamber within the field of view. The one or more processors are operatively coupled to the image sensor to receive image data therefrom and to the actuator to actuate the valve. The one or more processors are configured to estimate a flow of fluid through the drip chamber and to actuate the valve to control the flow of fluid through the drip chamber to achieve a target flow rate.

Object

Provided is an electromedical device control system and the like capable of improving the accuracy of determining a blocked state of a flow path.

Solving means

An electromedical device control system according to an embodiment of the present disclosure includes a determination unit that performs predetermined determination based on a pressure value of a fluid supplied to a flow path of an electromedical device, a pressure threshold set according to a setting value of a flow rate of the fluid, and an amount of change over time in the pressure value. The determination unit determines whether the amount of change over time in the pressure value is a positive value when determining that the pressure value is equal to or greater than the pressure threshold.

A method for controlling a filling process, wherein a predetermined filling quantity of a medium is filled into a container, the flow rate of the medium flowing into the container is measured as a time series of measured values for the instantaneous flow rate and a filling quantity already filled is estimated from the time series, wherein at least one current measured value of the time series is corrected on the basis of at least one earlier measured value of an earlier time series of measured values of the flow rate of an earlier filling process.

A multiple fluid model tool for training and/or refining of fluid models using disparate and/or aggregated machine data is presented. For example, a system includes a modeling component, a machine learning component, a three-dimensional design component and a data collection component. The modeling component generates a three-dimensional model of a mechanical device based on a library of stored data elements. The machine learning component predicts one or more characteristics of the mechanical device based on a machine learning process associated with the three-dimensional model. The three-dimensional design component provides a three-dimensional design environment associated with the three-dimensional model. The three-dimensional design environment renders physics modeling data of the mechanical device on the three-dimensional model based on the one or more characteristics of the mechanical device. The data collection component collects machine data via a communication network to update the three-dimensional model associated with the three-dimensional design environment.

A control system for an operable system such as a flow control system or temperature control system. The system operates in a control loop to regularly update a model with respect at least one optimizable input variable based on the detected variables. The model provides prediction of use of the input variables in all possible operation points or paths of the system variables which achieve an output setpoint. In some example embodiments, the control loop is performed during initial setup and subsequent operation of the one or more operable elements in the operable system. The control system is self-learning in that at least some of the initial and subsequent parameters of the system are determined automatically during runtime.

A flow sensor includes a fluid chamber configured to receive a fluid. A diaphragm assembly is configured to be displaced whenever the fluid within the fluid chamber is displaced. A transducer assembly is configured to monitor the displacement of the diaphragm assembly and generate a signal based, at least in part, upon the quantity of fluid displaced within the fluid chamber.

A mattress assembly includes: multiple gas-filled chambers each having a top surface, the top surfaces of the chambers collectively composing a top surface of the mattress assembly; multiple composite sensors each associated with a corresponding chamber. Each composite sensor includes a pressure sensor to measure a pressure at a wall of the corresponding chamber and a temperature sensor to measure a temperature of the corresponding chamber. The assembly includes chamber regulators each in communication with a corresponding chamber, each regulator configured to pump gas at a first, higher temperature and gas at a second temperature to the corresponding chamber. The assembly includes a controller in communication with the composite sensors and chamber regulators, programmed to: receive, from composite sensor(s), state data including pressure and temperature information for the chamber corresponding to the composite sensor; determine, based upon the received state data, information about a patient's position relative to the corresponding chamber; and based on the patient's position, control the chamber regulator for the chamber to modify a pressure and/or a temperature of the chamber.

A dehumidification system includes a compressor, a primary evaporator, a primary condenser, a secondary evaporator, and a secondary condenser. The secondary evaporator receives an inlet airflow and outputs a first airflow to the primary evaporator. The primary evaporator receives the first airflow and outputs a second airflow to the secondary condenser. The secondary condenser receives the second airflow and outputs a third airflow to the primary condenser. The primary condenser receives the third airflow and outputs a dehumidified airflow. The compressor receives a flow of refrigerant from the primary evaporator and provides the flow of refrigerant to the primary condenser.