A method and associated system for applying an optimized processing treatment to items in an industrial treatment line are described. First, settings for a value of a working parameter in the treatment line are defined. The settings are associated with value ranges of a parameter representative of an external property of items. Then, a value of the parameter representative of the external property of the item is measured. A setting for the value of the working parameter associated with a value range from the set of value ranges comprising the measured value is compared to the current setting of the working parameter. When a difference is detected between the associated setting and the current setting, the current setting is changed to the associated setting.

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.

There is provided a method of controlling an industrial gas plant complex comprising a plurality of industrial gas plants powered by one or more renewable power sources, the method being executed by at least one hardware processor, the method comprising receiving time-dependent predicted power data for a pre-determined future time period from the one or more renewable power sources; receiving time-dependent predicted operational characteristic data for each industrial gas plant; utilizing the predicted power data and predicted characteristic data in an optimization model to generate a set of state variables for the plurality of industrial gas plants; utilizing the generated state variables to generate a set of control set points for the plurality of industrial gas plants; and sending the control set points to a control system to control the industrial gas plant complex by adjusting one or more control set points of the industrial gas plants.

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 method for tuning predictive control parameters of a building energy consumption system based on fuzzy logic: 1) constructing a controlled building energy consumption system, performing generalized predictive control on the building energy consumption system, and initializing an tuned parameter λ of a generalized predictive controller; 2) collecting the output slope y.sub.k (t), the actual output y(t), the set value y.sub.r(t) and the predicted output value ŷ(t+i) of the controlled building energy consumption system in the control process, and then taking them as fuzzy target parameters; 3) constructing a membership function for the fuzzy target parameters in step 2), and then optimally selecting the parameters of the fuzzy membership function by using a particle swarm optimization algorithm to obtain membership function parameters of each fuzzy target parameter; 4) carrying out fuzzy reasoning operation on the membership function parameters, and tuning the adjusted parameter λ by using the results of fuzzy reasoning operation.

A load-energy efficiency evaluation and monitoring method includes an actual part processing number and a theoretical part processing number of the numerical control machine tool within an evaluation period are obtained to calculate a loading performance of the numerical control machine tool. A waste time value and a standby power value of the numerical control machine tool are obtained to calculate a waste energy value of the numerical control machine tool. A single-part actual processing energy consumption is obtained and used, together with a single-part ideal processing energy consumption, to calculate the load-energy efficiency of the numerical control machine tool. A relationship model between the load-energy efficiency and the loading performance of the numerical control machine tool is built based on the obtained model of the load-energy efficiency of the numerical control machine tool and the obtained model of the loading performance of the numerical control machine tool.

A facility that includes machines, includes a camera generating facility operation data, which includes an image of any given space. A computer system comprises: a data obtaining module obtaining the facility operation data; a work identification module identifying work that is performed in the facility, based on the image included in the facility operation data; and an output module outputting time-series information indicating a flow of the work. The work identification module identifies an object included in the image; identifies the work based on information about the identified object; and generates work analysis data, which associates the identified work, a period in which the identified work has been performed, and machines that is related to the identified work with one another. The output module outputs the time-series information or statistical information by using the work analysis data.

Disclosed are an energy service system of a multi-machine production line and a control method thereof, the method includes reorganizing respective machines in the production line into three types of controllable entities: a drive system, an energy supply bus and an execution device, equipping them with a control center, and selecting a sub-drive system that is idle and is capable of completing the work stage with high energy efficiency to supply energy service for the corresponding execution device through the energy supply bus. Further disclosed is a design method of a multi-machine shared drive system of a production line, which increases the number of basic flow units of each drive unit to a maximum value one by one, and coordinates action time to form a variety of scheduling schemes, and selects a configuration scheme whose total time and total energy consumption are less as the shared drive system.

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 learned model is generated which accurately outputs power consumption by running a newly created machining program without performing simulation, and the learned model is utilized to accurately predict the power consumption. A machine learning device includes an input data acquisition unit that, in machining a workpiece with an arbitrary machine tool by running an arbitrary machining program, acquires, as input data, information relating to the machine tool, an auxiliary operation device, and the workpiece, and machining information including the machining program. A label acquisition unit acquires label data indicating power consumption information relating to the machine tool and the auxiliary operation device in the running of the machining program. A learning unit performs supervised learning using the input and label data, and generates a learned model that inputs machining information of machining to be performed and outputs the power consumption information in the machining to be performed.