The present invention discloses a method for controlling a process in a process plant using a controller. The method comprises receivable associated with the process, determining a first value of at least one key performance indicator associated with the transition from the process data of the first process variable between the first steady state and the second steady state, comparing the determined first value of the at least one key performance indicator against a threshold value of the at least one key performance indicator; and determining a correction factor for modifying a set point of the process variable based on the comparison, for controlling the process.

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 for digital twinning a refuse vehicle includes a refuse vehicle, and a controller. The controller is configured to receive multiple datasets from the refuse vehicle, and generate a virtual refuse vehicle based on the multiple datasets. The virtual refuse vehicle includes a visual representation of the refuse vehicle and the multiple datasets. The controller is further configured to operate a display of a user device to provide the visual representation of the refuse vehicle and one or more of the multiple datasets to a user.

A system for digital twinning a refuse vehicle includes a refuse vehicle, and a controller. The controller is configured to receive multiple datasets from the refuse vehicle, and generate a virtual refuse vehicle based on the multiple datasets. The virtual refuse vehicle includes a visual representation of the refuse vehicle and the multiple datasets. The controller is further configured to operate a display of a user device to provide the visual representation of the refuse vehicle and one or more of the multiple datasets to a user.

A method for determining a physical model of an energy installation from a plurality of components linked together according to one or more constraints to form a tree, called tree of constraints, each component including one or more output ports, each output port being associated with a physical quantity of which the value depends on one or more variables internal to the component and/or on one or more variables external to the component, each external variable being communicated to the component through an input port. A second aspect relates to a method for controlling an electrical installation including a first phase of determining a physical model of the installation using the described method; and a second control phase during which each set point is determined as a function of a simulation carried out using the physical model obtained during the phase of determining a physical model of the energy installation.

A method for determining a physical model of an energy installation from a plurality of components linked together according to one or more constraints to form a tree, called tree of constraints, each component including one or more output ports, each output port being associated with a physical quantity of which the value depends on one or more variables internal to the component and/or on one or more variables external to the component, each external variable being communicated to the component through an input port. A second aspect relates to a method for controlling an electrical installation including a first phase of determining a physical model of the installation using the described method; and a second control phase during which each set point is determined as a function of a simulation carried out using the physical model obtained during the phase of determining a physical model of the energy installation.

A model-based control system is configured to select a desired parameter of a machinery configured to produce power and to output emissions and to select an emissions model configured to use the desired parameter as input and to output an emissions parameter. The model-based control system is additionally configured to tune the emissions model via a tuning system to derive a polynomial setpoint, and to control one or more actuators coupled to the machinery based on the polynomial setpoint.

Disclosed are methods of advanced process control (APC) for particular processes. A particular process (e.g., a photolithography or etch process) is performed on a wafer to create a pattern of features. A parameter is measured on a target feature and the value of the parameter is used for APC. However, instead of performing APC based directly on the actual parameter value, APC is performed based on an adjusted parameter value. Specifically, an offset amount (which is previously determined based on an average of a distribution of parameter values across all of the features) is applied to the actual parameter value to acquire an adjusted parameter value, which better represents the majority of features in the pattern. Performing this APC method minimizes dimension variations from pattern to pattern each time the same pattern is generated on another region of the same wafer or on a different wafer using the particular process.

Disclosed are methods of advanced process control (APC) for particular processes. A particular process (e.g., a photolithography or etch process) is performed on a wafer to create a pattern of features. A parameter is measured on a target feature and the value of the parameter is used for APC. However, instead of performing APC based directly on the actual parameter value, APC is performed based on an adjusted parameter value. Specifically, an offset amount (which is previously determined based on an average of a distribution of parameter values across all of the features) is applied to the actual parameter value to acquire an adjusted parameter value, which better represents the majority of features in the pattern. Performing this APC method minimizes dimension variations from pattern to pattern each time the same pattern is generated on another region of the same wafer or on a different wafer using the particular process.

The training of a learning agent to provide real-time control of an object is disclosed. Training of the learning agent and training of a corresponding pioneer agent are iteratively alternated. The training of the learning and pioneer agents is under the supervision of a supervisor agent. The training of the learning agent provides feedback for subsequent training of the pioneer agent. The training of the pioneer agent provides feedback for subsequent training of the learning agent. During the training, a supervisor coefficient modulates the influence of the supervisor agent. As agents are trained, the influence of the supervisor agent is decayed. The training of the learning agent, under a first level of supervisor influence, includes real-time control of the object. The subsequent training of the pioneer agent, under a reduced level of supervisor influence, includes replay of training data accumulated during the real-time control of the object.