In order to reduce the calculation cost of industrial plant evaluation, an industrial plant evaluation device 5 comprises: a reception unit 51 configured to receive an actual machine manipulated variable in process control of an industrial plant and an actual machine process variable to be controlled by the process control; an estimation unit 52 configured to determine a process variable as an estimated process variable by using a process model defining a mathematical relationship between a manipulated variable and a process variable in the process control, the process variable as the estimated process variable being obtained by substituting the actual machine manipulated variable for the manipulated variable in the process model; and a comparison unit 53 configured to compare the estimated process variable and the actual machine process variable with each other.

A method includes obtaining closed-loop data associated with operation of an industrial process controller, where the industrial process controller is configured to control at least part of an industrial process using at least one model. The method also includes generating at least one noise model associated with the industrial process controller using at least some of the closed-loop data. The method further includes filtering the closed-loop data based on the at least one noise model. In addition, the method includes generating one or more model parameters for the industrial process controller using the filtered closed-loop data.

A system and method are provided for generating minimal cut-sets for highly integrated large systems. The method includes receiving a system model (102) and a scenario (104), and obtaining a dependency array (300) from the system model (102) according to the scenario, with the dependency array (300) including at least one case (302). The method includes selecting a case (302) in the dependency array (300). The method includes querying a cut-set repository (110) to determine if a cut-set for a component (200) in the case (302) is already stored, and retrieving said cut-set; and if a cut-set is not stored, generating the minimal cut-set for the component (200). And the method includes computing a final cut-set list (112) by expanding the dependency array (300) using the cut-set for the component (200). The method improves the efficiency of cut-set generation applied to manufactured systems with great number of components.

In one embodiment, a model predictive control system for an industrial process includes a processor to execute an optimization module to determine manipulated variables for the process over a control horizon based on simulations performed using an objective function with an optimized process model and to control the process using the manipulated variables, to execute model modules including mathematical representations of a response or parameters of the process. The implementation details of the model modules are hidden from and inaccessible to the optimization module. The processor executes unified access modules (UAM). A first UAM interfaces between a first subset of the model modules and the optimization module and adapts output of the first subset for the optimization module, and a second UAM interfaces between a second subset of the model modules and the first subset and adapts output of the second subset for the first subset.

Embodiments presented herein provide techniques for generating and optimizing a plan in a manufacturing environment. The techniques begins by receiving a plurality of demands for a plan, wherein each demand of the plurality of demands has parameters specifying a set of operations, a due date, user specified business logic and priority. The demands are ranked based on the parameters and the user specified business logic. The plurality of demands is broken into sets of demands based on the a predefined number and the demand rank. The demands in a first set of demands are optimized to generate a strategy for fulfilling the demands in the first set of demands. One or more constraints are applied to the first set of demands to ensure the first set of demands is fulfilled in preference to the remaining sets of demands.

In order to reduce the calculation cost of industrial plant evaluation, an industrial plant evaluation device 5 comprises: a reception unit 51 configured to receive an actual machine manipulated variable in process control of an industrial plant and an actual machine process variable to be controlled by the process control; an estimation unit 52 configured to determine a process variable as an estimated process variable by using a process model defining a mathematical relationship between a manipulated variable and a process variable in the process control, the process variable as the estimated process variable being obtained by substituting the actual machine manipulated variable for the manipulated variable in the process model; and a comparison unit 53 configured to compare the estimated process variable and the actual machine process variable with each other.

Embodiments presented herein provide techniques for generating and optimizing a plan in a manufacturing environment. The techniques begins by receiving a plurality of demands for a plan, wherein each demand of the plurality of demands has parameters specifying a set of operations, a due date, user specified business logic and priority. The demands are ranked based on the parameters and the user specified business logic. The plurality of demands is broken into sets of demands based on the a predefined number and the demand rank. The demands in a first set of demands are optimized to generate a strategy for fulfilling the demands in the first set of demands. One or more constraints are applied to the first set of demands to ensure the first set of demands is fulfilled in preference to the remaining sets of demands.

A manufacturing execution system (MES) for providing an indication of the performance of the line. The MES includes a configuration module for modeling entities and lines containing the entities. The MES also includes a runtime module configured to determine the entities on the line whose production can be extrapolated to evaluate the performance of the line. In some cases, the MES determines which of the entities on the line limits the performance of the line; in other cases, the MES determines which of the entities has a production amount that best represents that of the line. The MES is operatively connected to field inputs associated with the entities that provide production data for the entities. Using the production data for the entities chosen to represent the line, the MES generates production metrics representative of the performance of the line and displays them to MES users.

A method includes obtaining closed-loop data associated with operation of an industrial process controller, where the industrial process controller is configured to control at least part of an industrial process using at least one model. The method also includes generating at least one noise model associated with the industrial process controller using at least some of the closed-loop data. The method further includes filtering the closed-loop data based on the at least one noise model. In addition, the method includes generating one or more model parameters for the industrial process controller using the filtered closed-loop data.

In one embodiment, a model predictive control system for an industrial process includes a processor to execute an optimization module to determine manipulated variables for the process over a control horizon based on simulations performed using an objective function with an optimized process model and to control the process using the manipulated variables, to execute model modules including mathematical representations of a response or parameters of the process. The implementation details of the model modules are hidden from and inaccessible to the optimization module. The processor executes unified access modules (UAM). A first UAM interfaces between a first subset of the model modules and the optimization module and adapts output of the first subset for the optimization module, and a second UAM interfaces between a second subset of the model modules and the first subset and adapts output of the second subset for the first subset.