Abstract: Methods and apparatus to implement predictive analytics for continuous processes are disclosed. An example apparatus includes a virtual batch unit controller to implement a sampling batch on a virtual batch unit. The sampling batch corresponds to a discrete period of time of a continuous control system process. The virtual batch unit includes input and output parameters corresponding to parameters associated with the continuous control system process. The example apparatus further includes a sampling batch analyzer to generate predictive analytic information indicative of a predicted quality of an output of the continuous control system process at an end of the discrete period of time based on an analysis of the sampling batch relative to an analytical model.

Abstract: Methods and apparatus to implement predictive analytics for continuous processes are disclosed. An example apparatus includes a virtual batch unit controller to implement a sampling batch on a virtual batch unit. The sampling batch corresponds to a discrete period of time of a continuous control system process. The virtual batch unit includes input and output parameters corresponding to parameters associated with the continuous control system process. The example apparatus further includes a sampling batch analyzer to generate predictive analytic information indicative of a predicted quality of an output of the continuous control system process at an end of the discrete period of time based on an analysis of the sampling batch relative to an analytical model.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to setup single-use equipment/processes. An example disclosed herein includes configuring a control interface via an object-oriented programming interface, the object-oriented programming interface including an object class representative of a single-use component in the single-use process. The example method also includes activating a single-use object in the control interface by detecting all ports associated with the single-use component are connected, and verifying the single-use component.

Abstract: Methods and apparatus for using analytical/statistical modeling to perform continued process verification (CPV) are described. Example methods include determining distribution characteristics for a plurality of parameters based on a first historical batch dataset measured while manufacturing a first batch at a first time, and generating a model batch based on the distribution characteristics of the plurality of parameters. Example methods also include generating a first set of simulated batch datasets corresponding to a first set of simulated batches by, for each one of the first set of simulated batches: generating values for the plurality of parameters based on the model batch, and determining a quality prediction based on the generated values. The example methods also include generating a model based on the first set of simulated batch datasets and the first historical batch dataset. The model is to be implemented to monitor a subsequent manufacture of a second batch.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to setup single-use equipment/processes. An example disclosed herein includes configuring a control interface via an object-oriented programming interface, the object-oriented programming interface including an object class representative of a single-use component in the single-use process. The example method also includes activating a single-use object in the control interface by detecting all ports associated with the single-use component are connected, and verifying the single-use component.

Abstract: Methods and apparatus are disclosed to provide for using analytical/statistical modeling for continued process verification (CPV). Example disclosed methods include determining distribution characteristics for a plurality of parameters based on a first historical batch dataset measured while manufacturing a first batch at a first time, and generating a model batch based on the distribution characteristics of the plurality of parameters. Example methods also include generating a first set of simulated batch datasets corresponding to a first set of simulated batches by, for each one of the first set of simulated batches: generating values for the plurality of parameters based on the model batch, and determining a quality prediction based on the generated values. The example disclosed methods also include generating a model based on the first set of simulated batch datasets and the first historical batch dataset. The model to be implemented to monitor a subsequent manufacture of a second batch.