Embodiments of the present disclosure provide for improved plantwide optimization with multistage pre-blending. Some embodiments generate a virtual representation of a plant that aggregates multiple stage components of the plant, process at least the virtual representation via a single-stage optimization process, cause production based on the results of the single-stage optimization process, generate an indicator of whether quality data for a preblended product, and cause routing, further processing, and/or the like, based on the specification satisfaction indicator. In this regard, some such embodiments optimize operation of a processing plant in circumstances where multistage blending is performed, such as due to expected and/or unexpected errors in operation for producing a particular final product.

A computer-implemented method for optimizing carbon emissions associated with an operation of a processing plant is provided. The processing plant includes a plurality of assets and the computer-implemented method includes identifying a carbon output value associated with operating each asset of the plurality of assets of the processing plant and identifying an impact value associated with each asset of the plurality of assets. The method also includes generating an optimized set of transformation actions corresponding to the plurality of assets utilizing a multi-optimization model. The multi-optimization model is based at least in part on the carbon output value and the impact value.

Embodiments generate improved optimizations of processing unit(s) associated with a particular dynamic control scheme (e.g., an MPC architecture). Embodiments perform the improved optimizations based on default assumptions associated with the scheme, for example status operation of a particular mode. Some embodiments generate default operating condition data associated with a particular timestamp interval, the default operating condition data at a first value representing a default assumption based on a first operational mode, identify first operating condition data associated with the first operational mode, representing the first value at particular subinterval(s) of the particular timestamp interval, generating intermediary operating condition data by removing the first operating condition data from the default operating condition data, generating adjusted operating condition data by including second operating condition data associated with a second subinterval of the subinterval of the particular timestamp interval, and optimizing based at least in part on the adjusted operating condition data.

A plurality of test data sets include: a first design data set specifying a design variant of a product; and first target values, which quantify target variables of the design variant which are to be optimized and ranked. Furthermore, a plurality of design evaluation modules for predicting target values on the basis of design data sets is provided. For each of the design evaluation modules, a second ranking of the first design data sets with respect to the predicted target values and a deviation of the second ranking from the first ranking are then determined. One design evaluation module is then selected in accordance with the determined deviations. Furthermore, a plurality of second design data sets is generated, and are predicted by the selected design evaluation module. A target-value-optimized design data set is then derived from the second design data sets and is output for the manufacturing of the product.

A method includes: storing, at a mobile robot, a local repository of self-assigned task definitions; determining, at a processor of the mobile robot, that a local activity metric associated with tasks assigned to the mobile robot by a central server meets an idle criterion; in response to determining that the local activity metric meets the idle criterion, selecting, by the processor, one of the self-assigned task definitions from the local repository; and initiating execution of a self-assigned task corresponding to the selected self-assigned task definition at the mobile robot.

A method for selecting optimum operation performance criteria for a metal working process. The method includes the step of developing a process model relating process parameters for the operation with performance variables for said operation, wherein the process parameters and performance variables are retrievable via integrated multiple data sources, and selecting at least one optimization technique to define a function, said function including process parameters. Moreover, the method includes generating the function for optimization by using acceptable tolerances of a product to be machined as a basis to define ranges for performance variables along with ranges for process parameters, and applying the at least one optimization technique to said function, whereby optimum operation performance criteria are calculated for the process model including process parameters and performance variables to obtain a set of requirements to be used for controlling the metal working process.

A method (600) for configuring one or more components of a process plant includes receiving (602) a change request (226) and receiving (604) a system dependency graph (228) corresponding to the process plant. The method (600) further includes selecting (606) a subset of components (230) among the plurality of components based on configuration of the process plant and identifying (608) a subset of nodes (232) among the plurality of nodes by traversing a path in the system dependent graph (228). The method (600) also includes computing (610) an impact parameter (234) value based on a traversed path and computing (612) a plurality of change parameter values (236) based on the traversed path. The method (600) further includes determining (614) an effort estimate (210) based on the impact parameter (234) value and the plurality of change parameter values (236) using a machine learning technique.

A production sequence optimizing method and a production sequence optimizing system that can reduce downtime caused by a setup operation. The production sequence optimizing method classifies a plurality of production programs which are continuously executed using a production line into a plurality of groups which share resources used for production and optimizes an execution order of the plurality of production programs. The production sequence optimizing method includes: a resource number counting step of counting the number of all resources held in stock; and a resource allocation step of allocating the resources to each of the groups on the basis of the number of all resources, the number of resources required for an N-th group, and the number of resources required for an (N+1)-th group.

A method determines an optimized production schedule of a production line including a hybrid multi-cluster tool formed by a plurality of single-arm tools and dual-arm tools interconnected with each other. The method includes determining time for individual operations of a robotic arm and a processing module in the plurality of single-arm tools and dual-arm tools; determining robot waiting time of the single-arm tools and dual-arm tools based on the time for individual operations and different connection relationships of the plurality of single-arm tools and dual-arm tools; determining whether the optimized production schedule exists using the determined waiting time, wherein the optimized production schedule only exists if the hybrid multi-cluster tool is process-dominant where the robot activity time of the plurality of single-arm tools and dual-arm tools is substantially shorter than processing time at the processing module; and determining the optimized production schedule if the optimized production schedule exists.

A controller is provided for building equipment including a plurality of devices that operate in parallel to affect an environmental condition of a building. The controller includes one or more processing circuits including one or more processors and memory. The memory store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include lowering an upper bound or raising a lower bound of one or more constraints based on a minimum off schedule or a minimum on schedule for the building equipment, performing an optimization of an objective function subject to the one or more constraints to generate control decisions for the building equipment, and operating the building equipment in accordance with the control decisions to affect the environmental condition of the building.