Abstract: A computer implemented method for monitoring a bioprocess comprising a cell culture in a bioreactor is provided. The method including the steps of: obtaining measurements of the amount of biomass and the amount of one or more metabolites in the bioreactor as a function of bioprocess maturity, using the measurements to determining one or more metabolic condition variables; using a pre-trained multivariate model to determine the value of one or more latent variables as a function of bioprocess maturity, wherein the multivariate model is a linear model that uses process variables including the metabolic condition variables as predictor variables and maturity as a response variable; comparing the value(s) of the one or more latent variables to one or more predetermined values as a function of maturity; and determining on the basis of the comparison whether the bioprocess is operating normally.

Abstract: Methods for monitoring, controlling and simulating a bioprocess comprising a cell culture in a bioreactor are provided. The methods comprise obtaining values of one or more process conditions for the bioprocess at one or more maturities, and determining the specific transport rates of one or more metabolites in the cell culture using the values obtained as input to a machine learning model trained to predict the specific transport rates of the one or more metabolites at a latest maturity of the one or more maturities or a later maturity based at least in part on the values of one or more process conditions for the bioprocess at the one or more preceding maturities. The methods further comprise predicting one or more features of the bioprocess based at least in part on the determined specific transport rates. Systems, computer readable media and methods for providing tools to implement such methods are also provided.

Abstract: A method, controller, and system for monitoring a manufacturing process are described. Measured values of multiple variables, including dependent variables, manipulated variables, or both, are received. Future values of the manipulated variables, future values of the dependent variables, or both, are predicted. A multivariate analysis is performed on a combination of (1) the measured values of the variables and (2) the future values of the manipulated variables, the future values of the dependent variables, or both, to generate multivariate statistics.

Abstract: A method, controller, and system for controlling a manufacturing process (batch-type or continuous-type) with a multivariate model are described. Dependent variable data and manipulated variable data are received. Dependent variable data represents values of uncontrolled process parameters from a plurality of sensors. Manipulated variable data represents controlled or setpoint values of controllable process parameters of a plurality of process tools. A predicted operational value, multivariate statistic, or both are determined based on the received data, and operating parameters of the manufacturing process are determined based on the predicted score, multivariate statistic, or both.

Abstract: Described are methods, systems, and a computer-readable storage medium for controlling a discrete-type manufacturing process (e.g., an injection molding process) with a multivariate model. Data representing process parameters, operating parameters, or both of the manufacturing process are received. The received data is compared with a multivariate model that approximates the manufacturing process to provide a result. Upon the result of the comparing satisfying a condition, one or more values for a set of operating parameters for the manufacturing process are determined. When the one or more determined values for the set of operating parameters satisfies a criterion, at least one operating parameter of the manufacturing process is updated.

Abstract: A method, controller, and system for monitoring a manufacturing process are described. Measured values of multiple variables, including dependent variables, manipulated variables, or both, are received. Future values of the manipulated variables, future values of the dependent variables, or both, are predicted. A multivariate analysis is performed on a combination of (1) the measured values of the variables and (2) the future values of the manipulated variables, the future values of the dependent variables, or both, to generate multivariate statistics.

Abstract: Described are methods, systems, and a computer-readable storage medium for controlling a discrete-type manufacturing process (e.g., an injection molding process) with a multivariate model. Data representing process parameters, operating parameters, or both of the manufacturing process are received. The received data is compared with a multivariate model that approximates the manufacturing process to provide a result. Upon the result of the comparing satisfying a condition, one or more values for a set of operating parameters for the manufacturing process are determined. When the one or more determined values for the set of operating parameters satisfies a criterion, at least one operating parameter of the manufacturing process is updated.

Abstract: A method, controller, and system for controlling a manufacturing process (batch-type or continuous-type) with a multivariate model are described. Dependent variable data and manipulated variable data are received. Dependent variable data represents values of uncontrolled process parameters from a plurality of sensors. Manipulated variable data represents controlled or setpoint values of controllable process parameters of a plurality of process tools. A predicted operational value, multivariate statistic, or both are determined based on the received data, and operating parameters of the manufacturing process are determined based on the predicted score, multivariate statistic, or both.