Rockwell Automation Technologies Incorporated

Pavilion Technologies, Inc.

Michael Edward Tay

Maina A Macharia

Patrick David Noll

James F Bartee

Bijan Sayyar-Rodsari

Celso Axelrud

Michael Eugene Sugars

Srinivas Budaraju

Timothy Morrison

Kiew Kam

Patrick D Noll

The embodiments described herein include one embodiment that provides a method for controlling sugar crystallization. The method includes, during a crystallization process, determining supersaturation of a sugar slurry of syrup and regulating influx of syrup into the sugar slurry to promote sugar crystallization in a closed-loop manner based upon the determined supersaturation.

Sugar crystallization control system and method

The present invention provides novel techniques for controlling batch reaction processes. In particular, a parametric hybrid model may be used to parameterize inputs and outputs of batch reaction processes. The parametric hybrid model may include an empirical model, a parameter model, and a dynamic model. Critical quality parameters, which are correlated with, but not the same as, end-of-batch quality values for the batch reaction processes may be monitored during cycles of the batch reaction processes. The quality parameters may be used to generate desired batch trajectories, which may be used to control the batch reaction processes during the cycles of the batch reaction processes.

Nonlinear model predictive control of a batch reaction system

The present invention provides novel techniques for controlling the output of a distillation sub-process by controlling, using model predictive control, the temperature of steam used in the distillation sub-process, wherein the steam is generated in a milling and cooking sub-process. In particular, the present techniques are presented in the context of biofuel production, wherein the temperature of a cook tube generated in a milling and cooking sub-process may be controlled to optimize the energy utilization in the ethanol/water separation of a side stripper column, which uses the cook flash steam as an energy source. However, the present techniques may also be applied to other suitable applications, such as liquor processing, where steam generated in a different process may be used in a distillation process to help separate water from the liquor.

Cook flash temperature optimization

The present invention provides novel techniques for optimizing throughput and yield using model predictive control techniques. In particular, the present techniques are presented in the context of biofuel production, wherein the trade off between yields of biofuel production and overall system throughput may be optimized. However, the present techniques may also be applied to any other suitable applications where a batch process of the system may affect a change in a property of the produced product, such as liquor processing.

Throughput/yield optimized model predictive control

System and method for managing fermentation feed in a biofuel production process, comprising a dynamic multivariate predictive model-based controller coupled to a dynamic multivariate predictive model. The model is executable to: receive process information, including water inventory and biomass information, from the biofuel production process; receive a specified objective for the fermentation feed specifying a target biomass concentration; and generate model output comprising target values for a plurality of manipulated variables of the biofuel production process, including target flow rates of water and/or biomass contributing to the fermentation feed in accordance with the specified objective. The controller is operable to dynamically control the biofuel production process by adjusting the plurality of manipulated variables to model-determined target values to stabilize water/biomass balance in the fermentation feed in accordance with the specified objective, including the specified target biomass concentration.

Model predictive control of a fermentation feed in biofuel production

System and method for managing batch fermentation in biofuel production. An optimizer executes a nonlinear multivariate predictive model of a batch fermentation process in accordance with an end of batch objective specifying a target end of batch biofuel concentration to determine an optimal batch trajectory over a temporal control horizon specifying a biofuel and/or sugar concentration trajectory over the batch fermentation process. A nonlinear control model for the batch fermentation process that includes the temporal control horizon driven by biofuel concentration during the batch fermentation process is executed per the determined optimal batch trajectory using received process information as input, thereby generating model output including target values for manipulated variables for the batch fermentation process, including batch fermentation temperature. The batch fermentation process is controlled per the target values to produce biofuel in accordance with the determined optimal batch trajectory, to substantially optimize the end of batch biofuel yield.

Model predictive control of fermentation in biofuel production

A system and method are provided for managing batch fermentation in a biofuel production process. A nonlinear control model of yeast growth and fermentable sugar concentration for biofuel (e.g., fuel ethanol) production in a batch fermentation process (pure and/or fed-batch fermentation) of a biofuel production process is provided. Process information for the batch fermentation process is received, and the nonlinear control model executed using the process information as input to determine values of one or more fermentation process variables for the batch fermentation process, e.g., fermentation temperature and/or enzyme flow, for substantially maximizing yeast growth and achieving target fermentable sugar concentrations. The batch fermentation process is then controlled in accordance with the determined values for the one or more fermentation process variables to substantially maximize yeast growth and achieve target fermentable sugar concentrations, where substantially maximizing yeast growth and achieving target fermentable sugar concentrations substantially maximizes biofuel production in the batch fermentation process.

Nonlinear model predictive control of a biofuel fermentation process

A system and method are provided for integrated management of a biofuel distillation process and a biofuel dehydration process of a biofuel production process, comprising a dynamic multivariate model-based controller coupled to a dynamic multivariate predictive model. The model is executable to: receive distillation and dehydration process information including biofuel compositions, receive an objective for biofuel production output from the distillation and dehydration processes, e.g., target product composition, production rate, and/or feed rate, and generate model output comprising target values for a plurality of manipulated variables related to the distillation and dehydration processes in accordance with the objective. The controller is operable to dynamically control the biofuel production process by adjusting the plurality of manipulated variables to the model-determined target values in accordance with the objective for biofuel production.

Integrated model predictive control of distillation and dehydration sub-processes in a biofuel production process

System and method for managing a biofuel production process. An integrated dynamic multivariate predictive model is provided that includes a continuous process model representing a continuous process of the biofuel production process, a batch process model representing a batch process of the biofuel production process, and a continuous simulation framework. The batch process model interacts with the continuous process model as a nonlinear continuous process via the framework using bridging equations. An objective is received, as is constraint information specifying constraints for the biofuel production process, where the constraints are in terms of the framework, and process information related to the batch and continuous processes. The integrated model is executed per the objective using the process information as input, generating target values for variables related to the batch and continuous processes per the objective and subject to the constraints, and used to control the batch and continuous processes substantially optimally.

Integrated model predictive control of batch and continuous processes in a biofuel production process

A system and method are provided for controlling temperature of a batch fermenter in a biofuel production process. A nonlinear predictive integrating temperature model for a batch fermentation process is provided that is a function of fermenter level. An objective for the batch fermentation process specifying a target fermenter temperature for the batch fermentation process is received, as is process information for the batch fermentation process, including fermenter level and fermenter temperature. The nonlinear predictive integrating temperature model is executed in accordance with the objective using the process information as input to determine target values for manipulated variables for controlling fermenter temperature of the batch fermentation process. The fermenter temperature for the batch fermentation process is controlled in accordance with the target values to produce biofuel in accordance with the objective, to substantially optimize the end of batch biofuel yield.

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Georgetown, TX, United States of America

Michael Edward Tay