The technology herein developed arises from the need to monitor and automate the entire deep hole drilling, drilling and milling processes, in cutting machines, in order to not only optimize the relevant task performance but also to increase the useful life of the cutting tools involved. An operating system and method are disclosed that allow controlling the entire deep hole drilling, drilling and milling processes, acting directly and automatically on the control of the cutting parameters, such as for example drilling feed and cooling adjustment, by means of collecting and real-time analyzing of data from sensors arranged in said cutting machine.

In a method for generating a switching sequence in an industrial plant which includes components for transferring the plant to an energy-saving state, a state model of the plant is used with regard to the components and information about a task-specific action schema with regard to the components. An extended state model is determined by supplementing the state model with the task-specific action schema using circuit-relevant variables. The switching sequence is generated from a start state to a desired target state by calculating proceeding from the target state back to the start state in the extended state model, and the switching sequence is executed.

A device and method are disclosed for optimizing self-power consumption. The device may sense one or more operating conditions of the device. The device may further select one or more operating parameters associated with at least one of the one or more operating conditions. The device may also estimate a power consumption associated with executing an algorithm to generate at least one updated value for at least one of the one or more operating parameters as well as estimate a power savings associated with operating using the updated value. The device may compare the estimated power consumption to the estimated power savings and determine whether to execute the algorithm based on the comparing.

An optimization system for a central plant includes a processing circuit configured to receive load prediction data indicating building energy loads and utility rate data indicating a price of one or more resources consumed by equipment of the central plant to serve the building energy loads. The optimization system includes a high level optimization module configured to generate an objective function that expresses a total monetary cost of operating the central plant over an optimization period as a function of the utility rate data and an amount of the one or more resources consumed by the central plant equipment. The optimization system includes a demand charge module configured to modify the objective function to account for a demand charge indicating a cost associated with maximum power consumption during a demand charge period. The high level optimization module is configured to optimize the objective function over the demand charge period.

A controller for equipment that operate to provide heating or cooling to a building or campus includes a processing circuit configured to obtain utility rate data indicating a price of resources consumed by the equipment to serve energy loads of the building or campus, obtain an objective function that expresses a total monetary cost of operating the equipment over an optimization period as a function of the utility rate data and an amount of the resources consumed by the equipment, determine a relationship between resource consumption and load production of the equipment, optimize the objective function over the optimization subject to a constraint based on the relationship between the resource consumption and the load production of the equipment to determine a distribution of the load production across the equipment, and operate the equipment to achieve the distribution.

A device and method are disclosed for optimizing self-power consumption. The device may sense one or more operating conditions of the device. The device may further select one or more operating parameters associated with at least one of the one or more operating conditions. The device may also estimate a power consumption associated with executing an algorithm to generate at least one updated value for at least one of the one or more operating parameters as well as estimate a power savings associated with operating using the updated value. The device may compare the estimated power consumption to the estimated power savings and determine whether to execute the algorithm based on the comparing.

A planning tool used to facilitate more efficient design of a central plant is configured to provide an advanced user interface. The user interface includes a symbol palette with selectable symbols that represent resource suppliers, subplants, energy loads, and resource storage devices associated with a central plant. The user interface allows users to drag these symbols onto a workspace and form various connections between the symbols. The user interface provides feedback to the user and prevents improper connections by evaluating user inputs according to a set of rules. The rules define valid relationships between the resource suppliers, subplants, energy loads, and resource storage devices. The user interface also allows users to specify equipment contained within the subplants. After a central plant model is created via the user interface, users can simulate operation of a central plant according to the model for planning, budgeting, and/or design considerations.

An energy-saving control device for a rolling line, the energy-saving control device capable of finding rolling conditions for minimizing energy consumption for the rolling line while ensuring a quality of a product. The energy-saving control device for a rolling line includes: an energy consumption estimating unit that calculates energy consumption for the rolling line based on rolling conditions for the rolling line; and an energy consumption optimizing unit that changes a rolling condition other than a target temperature of a material to be rolled, as a manipulation item so that the energy consumption calculated by the energy consumption estimating unit is reduced while a quality of a product formed by rolling the material to be rolled is ensured.

A device and method are disclosed for optimizing self-power consumption. The device may sense one or more operating conditions of the device. The device may further select one or more operating parameters associated with at least one of the one or more operating conditions. The device may also estimate a power consumption associated with executing an algorithm to generate at least one updated value for at least one of the one or more operating parameters as well as estimate a power savings associated with operating using the updated value. The device may compare the estimated power consumption to the estimated power savings and determine whether to execute the algorithm based on the comparing.

An optimization system for a central plant includes a processing circuit configured to receive load prediction data indicating building energy loads and utility rate data indicating a price of one or more resources consumed by equipment of the central plant to serve the building energy loads. The optimization system includes a high level optimization module configured to generate an objective function that expresses a total monetary cost of operating the central plant over an optimization period as a function of the utility rate data and an amount of the one or more resources consumed by the central plant equipment. The optimization system includes a demand charge module configured to modify the objective function to account for a demand charge indicating a cost associated with maximum power consumption during a demand charge period. The high level optimization module is configured to optimize the objective function over the demand charge period.