Mks Instruments, Inc.

Sartorius Stedim Data Analytics Ab

Christopher Peter McCready

Nicholas Trunfio

Svante Bjarne Wold

Daniel Robert Hazen

Christopher Paul Ambrozic

Techniques for predicting an amount of at least one biomaterial produced or consumed by a biological system in a bioreactor are provided. Process conditions and metabolite concentrations are measured for the biological system as a function of time. Metabolic rates for the biological system, including specific consumption rates of metabolites and specific production rates of metabolites are determined. The process conditions and the metabolic rates are provided to a hybrid system model configured to predict production of the biomaterial. The hybrid system model includes a kinetic growth model configured to estimate cell growth as a function of time and a metabolic condition model based on metabolite specific consumption or secretion rates and select process conditions, wherein the metabolic condition model is configured to classify the biological system into a metabolic state. An amount of the biomaterial based on the hybrid system model is predicted.

Computer-implemented method, computer program product and hybrid system for cell metabolism state observer

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.

Multivariate monitoring of a batch manufacturing process

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.

Controlling a manufacturing process with a multivariate model

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.

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London, Canada

Christopher Peter McCready