Embodiments determine historical geographic travel data of a vehicle over a contaminant exposure time period that is inclusive of a present time; identify a contaminant that is likely located on an external surface of the vehicle as a function of historic meteorological and terrain data of the historical geographic travel data of a vehicle over a contaminant exposure time period; select a cleaning solution that is appropriate for use in cleaning the identified contaminant from the external surface of the vehicle; and drive a vehicle wash system nozzle to use the selected cleaning solution to wash the identified contaminant from the external surface of the vehicle.

Provided is a method for controlling a resonance enhanced rotary drill comprising a drilling module and a control system for controlling one or more drilling parameters of the drilling module, which method comprises: (a) employing one or more initial characteristics of the material being drilled, and/or one or more initial drilling parameters to control the drilling module; (b) measuring one or more current drilling parameters to obtain one or more measured drilling parameters; (c) employing the one or more measured drilling parameters as an input in the control system, in order to obtain an output from the control system, which output comprises one or more calculated characteristics of the material being drilled; (d) employing the one or more calculated characteristics of the material being drilled, and/or the one or more measured drilling parameters, as an input in the control system, in order to obtain an output from the control system, which output comprises one or more calculated drilling parameters; (e) optionally applying the one or more calculated drilling parameters to the drilling module; (f) optionally repeating steps (b), (c) (d) and (e).

A motor control apparatus including a controller that controls a servo motor or a spindle motor and includes a switching determining part that determines a switching condition of the controller based on axis position information on a motor related to control of the motor control apparatus, a machine learning part that adjusts one or more parameters for the controller by machine learning for each switching condition, and a parameter holding part that holds the parameter adjusted by the machine learning part for each switching condition. The switching determining part, when determining the switching condition after adjustment of the parameter, uses the adjusted parameter corresponding to the switching condition in the controller. The apparatus enables changing, and automatic adjustment, of a parameter or controller to be used depending on a switching condition of the parameter related to axis position information or a switching condition of the controller using the parameter.

The invention provides control systems and methodologies for controlling a process having computer-controlled equipment, which provide for optimized process performance according to one or more performance criteria, such as efficiency, component life expectancy, safety, emissions, noise, vibration, operational cost, or the like. More particularly, the subject invention provides for employing machine diagnostic and/or prognostic information in connection with optimizing an overall business operation over a time horizon.

Laundry appliances use machine learning models and/or personalization to provide better treatments. As one example, a laundry appliance has a chamber in which laundry items are placed for treatment. Sensor(s) are positioned to sense contents of the chamber or to sense the laundry items as they are loaded into the chamber. The machine learning model uses data from these sensors to determine various attributes of the laundry items and/or the treatment, such as the type of fabric and/or how dirty the items are, and the treatment process is controlled accordingly. Personalized data, such as an individual's preferences for laundry treatments or his sensitivities and allergies, may also be used to personalize the treatment process.

The invention provides a diagnostic arrangement, which utilizes pre-processed measurement data, ML values and explanation values. By using all these values/data it is possible to analyse phenomenon, events, and behaviour of the process in such a way that a number of aspects can be taken into account.

The present invention relates to a system for controlling the flow rate of a platform comprising at least one controller, wherein the at least one controller uses Fuzzy-PID logic; at least one processor, wherein the at least one processor includes at least one artificial neural network model; wherein the at least one artificial neural network model has at least two inputs, wherein the at least two inputs comprise choke valve opening percentage data of the at least one well and head pressure data upstream of the choke valve of the at least one well; and wherein the at least one artificial neural network model has an output, wherein the at least one output is the gas flow rate or the oil flow rate produced by the platform; wherein the controller feeds the at least one artificial neural network model with the choke valve opening percentage data of the at least one well and head pressure data upstream of the choke valve of the at least one well.

Disclosed is a real-time decision support system for facilitating optimization of in a distributed environment. A data capturing module for capturing data from a plurality of data sources at a predefined interval of time. The data is captured pertaining to a specific domain and a specific geographical area. A forecasting module for forecasting demand of energy to be consumed by energy utilities in the specific domain and the geographical area upon analyzing the data. A position gap determination module for determining a position gap indicating a difference between the demand of energy and supply of energy. An energy optimization module for identifying at least one energy pool, from a plurality of energy pools, for retrieving a deficit of the demand of energy and providing the deficit of the demand of energy retrieved from the at least one energy pool in order to bridge the position gap.

A method or system for adaptive vehicle spacing, including determining a current state of a vehicle based on sensor data captured by sensors of the vehicle; for each possible action in a set of possible actions: (i) predicting based on the current vehicle state a future state for the vehicle, and (ii) predicting, based on the current vehicle state a first zone future safety value corresponding to a first safety zone of the vehicle; and selecting, based on the predicted future states and first zone future safety values for each of the possible actions in the set, a vehicle action.

A method of increasing reliability for a reactor effluent air cooler (REAC). The method includes providing a process facility computer communicatively coupled to at least one REAC including an air condenser with a plurality of field devices coupled thereto. The process facility computer includes a processor connected to a memory device storing a REAC predictive maintenance model. The REAC predictive maintenance model implements retrieving history data including at least one historical REAC value from the memory and receiving real-time data from the field devices including at least one real-time REAC value. A digital twin calculates a current reliability value for the REAC using the historical REAC data and the real-time REAC value. The method further includes comparing the current reliability value to a predetermined reliability value and generating an alert indicating that the REAC needs current maintenance whenever the current reliability value is less than the predetermined reliability value.