초록 ▼

A process is modeled by a dynamic model, handling time dependent relations between manipulated variables of different process sections (10A-D) and measured process output variables. Suggested input trajectories for manipulated variables for a subsequent time period are obtained by optimizing an obje

A process is modeled by a dynamic model, handling time dependent relations between manipulated variables of different process sections (10A-D) and measured process output variables. Suggested input trajectories for manipulated variables for a subsequent time period are obtained by optimizing an objective function over a prediction time period, under constraints imposed by the dynamic process model and/or preferably a production plan for the same period. The objective function comprises relations involving predictions of controlled process output variables as a function of time using the process model, based on the present measurements, preferably by a state estimation procedure. By the use of a prediction horizon, also planned future operational changes can be prepared for, reducing any induced fluctuations. In pulp and paper processes, process output variables associated with chemical additives can be used, adapting the optimization to handle chemical additives aspects.

대표청구항 ▼

The invention claimed is: 1. A method for a production process having a number of process sections the method comprising: obtaining a dynamic process model having time dependent relations between manipulated variables for said process sections and process output variables from respective process se

The invention claimed is: 1. A method for a production process having a number of process sections the method comprising: obtaining a dynamic process model having time dependent relations between manipulated variables for said process sections and process output variables from respective process section; providing external constraints for said production process for a prediction time interval; measuring a set of process output variables of said production process; estimating an initial state by using said measured process output variables; defining an objective function involving predicted controlled process output variables for said prediction time interval and said external constraints; said predicted controlled process output variables being defined by said dynamic process model based on said initial state; optimizing said objective function under constraints imposed by said dynamic process model and/or said external constraints, by adapting said manipulated variables, giving input trajectories for said manipulated variables for said prediction time interval; and operating said production process by setting said manipulated variables according to said input trajectories during a control time interval. 2. The method according to claim 1, wherein said method is a pulp and/or paper production method. 3. The method according to claim 2, wherein said controlled process output variables comprise at least one variable associated with chemical additives used for pulp and/or paper production. 4. The method according to claim 3, wherein said defining step in turn comprises the step of including an objective function term being dependent on a total amount of chemical additives in at least two of said process sections. 5. The method according to claim 3, wherein said defining step in turn comprises the step of including an objective function term being dependent on the relative distribution of different chemical forms of chemical additives between different process sections throughout the production process. 6. The method according to claim 3, wherein said defining step in turn comprises the step of including an objective function term being dependent on a difference between a concentration of at least one chemical additive in at least one of said process sections and a pre-determined set value. 7. The method according to claim 6, wherein said concentration is related to sulfidity. 8. The method according to claim 1, wherein said external constraints comprises a production plan. 9. The method according to claim 1, wherein said estimation of said initial state is also obtained by previously measured process output variables. 10. The method according to claim 9, wherein said estimation of said initial state comprises a state estimation procedure. 11. The method according to claim 10, wherein said state estimation procedure is a moving horizon state estimation. 12. The method according to claim 1, wherein said control time interval is a part of said prediction time interval. 13. The method according to claim 12, wherein said control time interval is substantially shorter than said prediction time interval. 14. The method according to claim 13, wherein said prediction time interval is more than 10 times longer than said control time interval. 15. The method according to claim 1 wherein at least a part of said measuring of process output variables is performed on-line. 16. The method according to claim 1 wherein at least one of said process output variables is selected from the list of: flow rate; flow concentration; buffer level; buffer concentration; and internal process section variable. 17. The method according to claim 1 wherein said defining step in turn comprises the step of: deriving target trajectories for said controlled process output variables under said external constraints starting from said initial state; whereby said objective function comprises deviations between said target trajectories and predicted process output variables integrated over said prediction time interval. 18. The method according to claim 17, wherein said deriving step in turn comprises the steps of: calculating ideal set-point trajectories for said controlled process output variables under constraints imposed by said production plan; and modifying said ideal set-point trajectories into said target trajectories by introducing said initial state and a smoothing with time of structures of said ideal set-point. 19. The method according to claim 17, wherein said integration over said prediction time interval further comprises a time dependent weight function. 20. The method according to claim 1 wherein said dynamic process model in turn comprises a number of section models, representing operation of a process section, connected to a number of intermediate storage models by model flows; said section models comprising said time dependent relations; and said intermediate storage models being characterized by a buffer level. 21. The method according to claim 20, wherein defining step in turn comprises the step of including an objective function term being dependent on remaining buffer capacity of a buffer preceding a bottleneck process section and on said buffer level of a buffer following a bottleneck process section. 22. The method according to claim 20, wherein defining step in turn comprises the step of including an objective function term being dependent on said buffer level of a buffer preceding a process section having a relative high probability of failure and on remaining buffer capacity of a buffer following a process section having a relative high probability of failure. 23. The method according to claim 1, wherein one or more steps of said method is performed at a location remote from said production process. 24. A production process system, comprising: a number of process sections, controllable by manipulated variables; sensors measuring a set of process output variables of said production process; processor means, connected to said sensors; process section control means, setting said manipulated variables, said process section control means being connected to said processor means; said processor means in turn comprising: means for obtaining a dynamic process model having time dependent relations between manipulated variables for said process sections and process output variables from respective process section; means for providing external constraints for a prediction time interval; means for defining an objective function involving predicted controlled process output variables for said prediction time interval and said external constraints; said predicted controlled process output variables being defined by said dynamic process model based on an estimation of an initial state obtained by said measured set of process output variables; means for optimizing said objective function under constraints imposed by said dynamic process model and/or said external constraints by adapting said manipulated variables, giving input trajectories for said manipulated variables for said prediction time interval; whereby said process section control means is arranged for setting said manipulated variables according to said input trajectories during a control time interval. 25. A system for a production process having a number of process sections, controllable by manipulated variables, comprising: processor means; and process section control means, setting said manipulated variables, said process section control means being connected to said processor means; said processor means in turn comprising: sensor input means for receiving a set of process output variables of said production process; means for obtaining a dynamic process model having time dependent relations between manipulated variables for said process sections and process output variables from respective process section; means for providing external constraints for a prediction time interval; means for defining an objective function involving predicted controlled process output variables for said prediction time interval and said external constraints; said predicted controlled process output variables being defined by said dynamic process model based on an estimation of an initial state obtained by said measured set of process output variables; means for optimizing said objective function under constraints imposed by said dynamic process model and/or said external constraints by adapting said manipulated variables, giving input trajectories for said manipulated variables for said prediction time interval; whereby said process section control means is arranged for setting said manipulated variables according to said input trajectories during a control time interval. 26. The system according to claim 25, wherein said production process system is a pulp and/or paper production process system. 27. The system according to claim 26, wherein said controlled process output variables comprise at least one variable associated with chemical additives of said production process. 28. The system according to claim 27, wherein said objective function comprises a term dependent on a total amount of chemical additives in at least two of said process sections of said production process. 29. The system according to claim 27 wherein said objective function comprises relations based on the relative distribution of different chemical species of chemical additives between different process sections throughout the production process. 30. The system according to claim 27 wherein said objective function comprises a term dependent on a difference of a concentration of at least one chemical additive in at least one of said process sections and a pre-determined set-value. 31. The system according to claim 30, wherein said concentration is related to sulfidity. 32. The system according to claim 25 wherein said external constraints comprise a production plan. 33. The system according to claim 25 wherein said estimation of said initial state is obtained also by previously measured process output variables. 34. The system according to claim 33, wherein said processor further comprises state estimation means for performing said estimation of said initial state. 35. The system according to claim 25 at least one of said process output variables of said production process system has a time constant exceeding 12 hours. 36. The system according to claim 25 said prediction time interval exceeds 12 hours. 37. The system according to claim 25 said control time interval is less than 15 minutes. 38. The system according to claim 25 wherein communication links between said processor and said production process, for allowing a remote control of said production process. 39. The system according to claim 38, wherein said communication links comprises a data communication network. 40. A computer program product comprising computer code means and/or software code portions for making a processor perform the steps of claim 1. 41. The computer program product according to claim 40 supplied via a network, such as Internet. 42. A computer readable medium containing a computer program product according to claim 40. 43. A computer program comprising computer code means and/or software code portions for making a processor perform the steps of claim 1. 44. The computer program according to claim 43 supplied via a network, such as Internet. 45. Use of a method according to claim 1 to carry out on a production process any of the operations of: monitoring, controlling, regulating, simulating, optimising, providing support for decisions, advising. 46. Use of a method according to claim 1 to carry out on a pulp and/or paper production process any of the operations of: monitoring, controlling, regulating, simulating, optimising, providing support for decisions, advising. 47. Use of a system according to claim 25 to carry out on a production process any of the operations of: monitoring, controlling, regulating, simulating, optimising, providing support for decisions, advising. 48. Use of a system according to claim 25 to carry out on a pulp and/or paper production process any of the operations of: monitoring, controlling, regulating, simulating, optimising, providing support for decisions, advising.