Rockwell Automation Technologies Incorporated

Brian Kent Stephenson

L Paul Collete, Iii

David G Hoch

Maina A Macharia

Patrick David Noll

Lina M Rueda Velandia

Lina M Rueda-Velandia

System and method for determining total inventory of batch and continuous biomass inventories in a biofuel production process. Measured biomass inventory values are received, including batch inventories from multiple batch fermenters and at least one continuous beer well inventory. The values are premised on a controlled vapor pressure of the batch fermenters and beer well(s), where the pressure fluctuates in an uncontrolled manner. A measured vapor pressure for the batch fermenters and beerwell(s) is received, and pressure compensated inventory values determined based on the measured inventory and pressure values. Measured biomass input flow to the batch fermenters and output flow from the beerwell(s) are received, and are premised on a constant biomass temperature and density, where biomass temperature and density fluctuate in an uncontrolled manner. The total inventory of batch and continuous inventories is determined based on the measured flows and the pressure compensated inventory values, and stored.

Determining total inventory of batch and continuous inventories in a biofuel production process

The present invention provides novel techniques for validating laboratory data values for properties of interest of products produced by a process system. In particular, samples of the product may be sent to a laboratory testing facility, where laboratory testing procedures may be used to obtain the laboratory data values for the property of interest. The laboratory data values may be sent to a control system which includes a laboratory data validation module. The laboratory data validation module may be capable of validating the laboratory data values of the property of interest by comparing the laboratory data values of the property of interest with predicted values generated by a model. The model may be created using inputs such as laboratory and measured data values of the property of interest as well as laboratory and measured data values of other properties of the product. In particular, the laboratory data validation module may, in certain embodiments, include a laboratory data validation model, which may aid the validation of the laboratory data values of the property of interest.

Process controller having a laboratory data validation model and methods for using the same

A technique is disclosed for optimizing a quality parameter in a process that is not directly measurable online using conventional measurement devices. The technique includes the use of a first inferential model to predict a value for the parameter based upon other process variables. A second inferential model predicts a residual component of the process parameter based off non-controllable residual variables of the process. The inferential model outputs are combined to produce a composite predicted value which may be further adjusted by an actual prediction error determined via comparison with an offline measurement. The adjusted predicted value is provided to a dynamic predictive model which may be adapted to implement control actions to drive or maintain the quality parameter at a target set point. The technique may further consider cost optimization factors and production reliability factors in order to produce a product meeting the target quality set point or range while considering production requirements and minimizing overall costs.

System and method for optimizing a paper manufacturing process

A technique is disclosed for asymmetrically controlling a process parameter based upon the direction of a prediction error between a predicted value determined using an inferential model and a laboratory measurement of the parameter. The present technique provides for the adaptive biasing of the predicted value based upon the direction of the prediction error. In one embodiment, a biasing factor may be determined by filtering the prediction error, such that the prediction error is emphasized more heavily in the biasing factor if the prediction error is in a less tolerable direction and emphasized less heavily if the prediction error is in the opposite direction. The biasing factor may further be determined as a function of a previous biasing factor computed during the process. Asymmetric control of the process parameter may be performed by controlling the parameter using model predictive control techniques based on the biased predicted values of the parameter.

Asymmetrical process parameter control system and method

One or more techniques are provided for the sharing of variable trajectories between multiple controllers in a control system. In general, the present technique is based upon the sharing of predicted desired variable trajectories between multiple controllers requiring a common input variable. In one embodiment, a common input variable is shared between two controllers, wherein one controller is tuned to have dominant control with regard to the shared variable. Each controller determines and communicates their own desired trajectory for the shared variable to the other controller and in doing so, the constraints associated with each controller are shared and honored by the other controller. Based on the communicated trajectories, the dominant controller may exercise control over the shared variable, thus improving overall process control.

Model predictive control with variable trajectory sharing

Validation of laboratory test data based on predicted values of property-of-interest

Asymetrical process parameter control system and method

System and method for determining total mill flow (TMF) in a biofuel production process. Measured slurry flow and density values from a process fed by mill(s), backset, and at least one water source, are received. The slurry includes biomass solids and water. Measured backset flow and density values, and a backset composition value are received, as well as a slurry composition value indicating % biomass solids of the slurry is received. A quantity of biomass solids and/or water of the slurry determined based on the slurry flow, density, and composition, and a quantity of the biomass solids and/or water of the backset determined based on the backset flow and density, backset composition, and a system filter characterizing time delays and lags between measurements of the backset flow and biomass slurry flow. Total mill flow is determined based on the quantities of biomass solids and/or water of the slurry and backset.

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Georgetown, TX, United States of America

Brian Kent Stephenson