초록 ▼

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 inf

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.

대표청구항 ▼

1. A computer-implemented method for managing fermentation feed in a biofuel production process, comprising: providing a dynamic multivariate predictive model of biomass concentration and water inventory for a fermentation feed of the biofuel production process;receiving a specified objective for th

1. A computer-implemented method for managing fermentation feed in a biofuel production process, comprising: providing a dynamic multivariate predictive model of biomass concentration and water inventory for a fermentation feed of the biofuel production process;receiving a specified objective for the fermentation feed specifying a target biomass concentration and a target fermentation residual broth fraction for the fermentation feed;receiving process information, comprising water inventory information and biomass concentration information, from the biofuel production process;receiving constraint information specifying one or more constraints;executing the dynamic multivariate predictive model in accordance with the objective using the received water inventory, the one or more constraints, and biomass concentration information as input, thereby generating model output comprising target values for a plurality of manipulated variables of the biofuel production process, including target water flow rates, target biomass flow rates, and target fermentation residual broth recycle rates; andcontrolling the biofuel production process, including water flow rates, biomass flow rates, and fermentation residual broth recycle rates, in accordance with the target values for the plurality of manipulated variables. 2. The method of claim 1, wherein the dynamic multivariate predictive model specifies relationships between fermentation feed rates and equipment constraints; the constraint information further comprises one or more equipment constraints of the biofuel production process; the specified objective further comprises a target feed rate for the fermentation feed; and executing the dynamic multivariate predictive model further comprises generating target values of manipulated variables to approach and maintain the target feed rate for the fermentation feed subject to the one or more equipment constraints. 3. The method of claim 1, wherein the dynamic multivariate predictive model further specifies relationships between biomass fractional flow rates and enzyme flow rates; the process information further comprises enzyme flow rate information from the biofuel production process comprising one or more enzyme flow rates; executing the dynamic multivariate predictive model comprises executing the dynamic multivariate predictive model using the received water inventory and biomass concentration information, constraint information, and the enzyme flow rates as input; wherein the model output further comprises target enzyme flow rates; and wherein said controlling the biofuel production process further comprises controlling enzyme flow rates in accordance with the target enzyme flow rates. 4. The method of claim 3, wherein the enzyme flow rates comprise respective ratios of enzyme addition rates to biomass addition rates. 5. The method of claim 3, wherein said controlling enzyme flow rates in accordance with the target enzyme flow rates is performed to maintain the fermentation feed at a specified target feed rate. 6. The method of claim 5, wherein the dynamic multivariate predictive model further specifies at least one equipment setting as a capacity constraint, and wherein said controlling enzyme flow rates comprises controlling enzyme ratios to increase milling and cook capacity in the biofuel production process by increasing liquefaction agents to reduce slurry viscosity and allow higher flow rates within limits due to the at least one equipment setting. 7. The method of claim 6, wherein the at least one equipment setting comprises at least one valve position, and wherein the limits due to the at least one equipment setting comprise line pressure limits and/or control valve ranges. 8. The method of claim 1, wherein the dynamic multivariate predictive model comprises one or more of a linear model, a nonlinear model, a fundamental model, an empirical model, a neural network, a support vector machine, a statistical model, a rule-based model, or a fitted model. 9. The method of claim 1 wherein the objective further comprises one or more of: a target fermentation feed rate; a consistent water fraction of the fermentation feed; an operator specified objective; a predictive model specified objective; a programmable objective; a cost objective; a quality objective; an equipment maintenance objective; an equipment repair objective; an equipment replacement objective; an economic objective; a target throughput for the biofuel production process; an objective in response to emergency occurrences; a dynamic change in water inventory information; a dynamic change in biomass concentration information; and a dynamic change in one or more constraints on the biofuel production process. 10. The method of claim 1, wherein the objective for the fermentation feed is specified by one or more of a human operator and a program. 11. The method of claim 1, wherein the water inventory information includes one or more of: fluid levels for one or more water tanks; capacity limits for each of the one or more water tanks; operational status for each of the one or more water tanks; and flow rates for one or more water flows. 12. The method of claim 11, wherein the one or more water flows comprise one or more of: water flow rates to each of one or more processing units; recycled water flow rates from one or more distillation units; and recycled water flow rates from one or more stillage processing units. 13. The method of claim 1, wherein the water inventory information comprises inventory information for a plurality of water sources, comprising one or more of: one or more water sources; and/or one or more recycled water sources; and wherein the target water flow rates comprise target flow rates for one or more of: water and recycled water. 14. The method of claim 13, wherein the recycled water comprises one or more of: fermentation broth recycle water; evaporator condensate recycle water; distillation bottoms recycle water; and treated water from an anaerobic digester. 15. The method of claim 13, wherein said executing the dynamic multivariate predictive model comprises the dynamic multivariate predictive model: determining a total water flow rate for the fermentation feed in accordance with the specified objective; and partitioning the total water flow rate for the fermentation feed among the plurality of water sources to determine respective target flow rates for each of the plurality of water sources; and wherein said controlling flow rates of water inventory comprises controlling respective flow rates for each of the plurality of water sources in accordance with the respective target flow rates. 16. The method of claim 15, wherein said partitioning the total water flow rate for the fermentation feed among the plurality of water sources to determine respective target flow rates for each of the plurality of water sources comprises performing said partitioning subject to one or more constraints regarding the plurality of water sources. 17. The method of claim 16, wherein the one or more constraints regarding the plurality of water sources comprise one or more of: limits on tank levels; limits on tank fill or emptying rates; percent impurity constraints; constraints on impurity type; and constraints on water transport. 18. The method of claim 17, wherein the constraints on water transport comprise one or more of: operational status of one or more water transport elements; and operational limits on one or more water transport elements. 19. The method of claim 1, wherein said biomass concentration information comprises one or more of: feed rates for each mill, amp limits for each mill, water flow rates, stream density, temperature and/or biomass concentration limit. 20. The method of claim 1, wherein the biomass concentration information is provided by another model with inputs comprising one or more of: measured temperature of the biomass; mill output; mill energy use; and water input to the fermentation feed. 21. The method of claim 20, wherein at least a portion of the water input to the fermentation feed is determined by indirect measurements related to the water input. 22. The method of claim 20, wherein the other model is a dynamic multivariate predictive model that includes time-response of biomass density to changes in one or more inputs to the biofuel production process. 23. The method of claim 20, wherein the one or more inputs to the biofuel production process are dynamically filtered based on the time-response and provided as inputs to the other model, and wherein one or more controllers for controlling biomass concentration are configured in accordance with the time-response. 24. The method of claim 1, wherein the one or more constraints comprise one or more of: mill amperage limit that limits fermentation feed rate and/or cook flow rate; thin stillage tank level limits that limit fermentation residual broth recycle rate; slurry water tank level limits that limit water addition rates; pressure limits that limit pump capabilities, safety or processing rates; back-end processing rate limits that limit fermentation fill rate; and/or cook temperature limits that limit fermentation fill rate; water constraints; biomass constraints; feed constraints; equipment constraints; capacity constraints; temperature constraints; pressure constraints; energy constraints; market constraints; economic constraints; or operator imposed constraints. 25. The method of claim 24, wherein the operator constraints comprise one or more of operating limits for pumps; operational status of pumps; tank capacities; operating limits for tank pressures; operational status of tanks; operating limits for valve pressures; operating limits for valve temperatures; and operating limits for pipe pressures. 26. The method of claim 1, wherein said constraint information comprises dynamic constraint information. 27. The method of claim 1, wherein said executing the dynamic multivariate predictive model in accordance with the objective comprises: an optimizer executing the dynamic multivariate predictive model in an iterative manner to determine target values for the plurality of manipulated variables that satisfy the specified objective subject to one or more constraints. 28. The method of claim 1, wherein said controlling flow rates of water inventory and/or biomass concentration comprises: operating one or more flow controllers coupled to the dynamic multivariate predictive model, wherein the one or more flow controllers control one or more of: mill speed; pump speeds for one or more biomass feeds; and pump speeds for one or more water feeds. 29. A system for managing fermentation feed in a biofuel production process, comprising: a dynamic multivariate predictive model of biomass concentration and water inventory for a fermentation feed of the biofuel production processa dynamic multivariate predictive model-based controller coupled to the dynamic multivariate predictive model; andprogram instructions that, when executed, cause controller to: receive process information, comprising water inventory and biomass information, from the biofuel production process;receive a specified objective for the fermentation feed specifying a target biomass concentration and a target fermentation residual broth fraction for the fermentation feed;receive constraint information specifying one or more constraintsexecute the dynamic multivariate predictive model, in accordance with the objective using the received water inventory, the one or more constraints, and biomass concentration information as input, thereby generating model output comprising target values for a plurality of manipulated variables of the biofuel production process, including target flow rates of water and biomass, and target fermentation residual broth recycle rates; andcontrol the biofuel production process, including water flow rates, biomass flow rates, and fermentation residual broth recycle rates in accordance with the target values for the plurality of manipulated variables. 30. A non-transitory computer-accessible memory medium for use in managing fermentation feed in a biofuel production process, comprising: a dynamic multivariate predictive model of biomass concentration and water inventory for a fermentation feed of the biofuel production process; andprogram instructions that, when executed, cause a controller to: receive process information, comprising water inventory and biomass information, from the biofuel production process;receive a specified objective for the fermentation feed specifying a target biomass concentration and a target fermentation residual broth fraction for the fermentation feed;receive constraint information specifying one or more constraints;execute the dynamic multivariate predictive model in accordance with the objective using the received water inventory, the one or more constraints, and biomass concentration information as input, thereby generating model output comprising target values for a plurality of manipulated variables of the biofuel production process, including target flow rates of water and biomass, and target fermentation residual broth recycle rates; andcontrol the biofuel production process, including water flow rates, biomass flow rates, and fermentation residual broth recycle rates in accordance with the target values for the plurality of manipulated variables.