# US 11,998,927 B2 · Method and device for controlling a process within a system, in particular a grinding process in a grinding device > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,998,927 B2 - Document ID: US11998927B2 - Status: Granted - Kind: B2 - Application number: 16/757,715 - Filed: 2018-10-15 - Published: 2021-07-01 - Granted: 2024-06-04 - Earliest claimed: 2017-10-20 - Assignees: Airprocess GmbH - Applicants: Aixprocess GmbH - Inventors: Martin Weng - Web page: https://patentgrep.com/patent/US11998927B2 ## CPC - B02C 25/00: Control arrangements specially adapted for crushing or disintegrating - G05B 13/041: {in which a variable is automatically adjusted to optimise the performance} - G05B 13/042: {in which a parameter or coefficient is automatically adjusted to optimise the performance} - G05B 13/048: {using a predictor} - B02C: CRUSHING, PULVERISING, OR DISINTEGRATING IN GENERAL; MILLING GRAIN ({household tools and machines for pulverising foodstuffs, e.g. coffee and spice mills A47J42/00; pharmaceutical mortars A61J3/02; mechanical processing of refuse and garbage B03B9/06; dressing mould materials by grinding B22C5/04}; obtaining metallic powder by crushing, grinding or milling B22F9/04; {recovery of plastics by disintegrating B29B17/00; crushing raw materials in starch making C08B30/02; beaters for papermaking D21D1/02; crushing devices specially for transport in mines E21F13/002; slag crushing devices F23J1/00; fuel milling devices in combustion apparatus F23K1/00; household devices for crushing coal F24B15/02; ice disintegrating devices F25C5/02}) - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) ## Abstract A method for controlling a process within a system, in particular a grinding process in a grinding device, comprising the following steps: .detecting 1 of state variables (st) of the system; creating 2 of at least two process models (PM), each describing the effects of actuating actions (at) on the state variables (st) of the system, wherein the structure of the at least two process models (PM) differs from each other; controlling 3 of the process within the system by executing actuating actions (at) under consideration of predefined control objectives and the process model, which currently provides the best prediction for the process running in the system. A device for carrying out the method according to the invention. ## Claims ### Claim 1 A method for controlling a process within a system, in particular a grinding process in a grinding device, comprising: detecting state variables (st) of the system with the grinding device, wherein similar state variables (st) of the system are grouped together and wherein the classification of similar state variables (st) of the system into groups is self-learning and self-optimizing and compares the costs of grouping state variables (st) of the system into a group with the costs of creating a new group; creating at least two process models (PM) from each group of state variables (st) after each group of state variables (st) is created, which each describe predicted effects of actuating actions (at) on the group of state variables (st) of the system, a structure of the at least two process models (PM) differing from one another, wherein at least one of the at least two process models (PM) is created for short-term effects of the actuating actions (at) on the group of the state variables (st) of the system and at least another one of the at least two process models (PM) is created for long-term effects of the actuating actions (at) on the group of the state variables (st) of the system; controlling of the grinding process within the system by executing actuating actions (at) under consideration of predefined control objectives and a process model of the at least two process models (PM) which currently provides the best prediction for the grinding process within in the system; and wherein grouping similar state variables (st) of the system into groups reduces the process models (PM) of the state variables (st) to save resources. ### Claim 2 (depends on 1) The method according to claim 1, wherein the state variables (st) are detected by sensors of the grinding device. ### Claim 3 (depends on 1) The method according to claim 1, wherein the at least two process models (PM) are continuously adapted by the effects of actuating actions (at) on the state variables (st) of the grinding device system. ### Claim 4 (depends on 1) The method according to claim 1, wherein the at least two process models (PM) are created by a test run of the grinding device with at least an exemplary execution of actuating actions (at). ### Claim 5 (depends on 1) The method according to claim 1, wherein the at least two process models (PM) are each created by a computer-based neural network and are continuously adapted. ### Claim 6 (depends on 1) The method according to claim 1, wherein the at least two process models (PM) each take into account several assumed future effects of actuating actions (at+1) on the state variables (st+1) of the system. ### Claim 7 (depends on 1) The method according to claim 1, further comprising the steps of: creating a situation assessment (SB) by quality functions (ut), which assesses the state variables (st) with respect to the control objectives; and deciding whether a control is required at the current time to adapt the state variables (st) to the control objectives. ### Claim 8 (depends on 7) The method according to claim 7, wherein the effects of different actuating actions (at) on the state variables (st) are calculated several time steps in advance and an overall quality (Q) is evaluated. ### Claim 9 (depends on 7) The method according to claim 7, wherein the quality functions (ut) are established by statistical method. ### Claim 10 (depends on 9) The method according to claim 9, wherein the statistical method includes artificial intelligence and/or fuzzy logic. ### Claim 11 (depends on 1) The method according to claim 1, wherein the method is performed in real time to control the grinding process within the system. ### Claim 12 (depends on 1) The method according to claim 1, wherein at least one of said at least two process models (PM) is based on the method of numerical fluid mechanics. ### Claim 13 (depends on 1) A device for implementing the method according to claim 1, comprising a grinding device to be controlled with sensors for detecting condition variables (st), actuating devices for carrying out actuating actions (at) and a control device connected to the grinding device. ### Claim 14 (depends on 13) The method according to claim 13, wherein the control device comprises a computing device. ### Claim 15 (depends on 1) The method according to claim 1, wherein the state variables (st) are detected by manual and/or automatic sample evaluations. ### Claim 16 (depends on 1) The method according to claim 1, wherein the at least two process models (PM) are created by expert knowledge. ### Claim 17 (depends on 1) The method according to claim 1, wherein the at least two process models (PM) are based on or trained on evolutionary strategies, genetic algorithms, genetic programming or evolutionary programming. ### Claim 18 (depends on 17) The method according to claim 17, wherein the at least two process models (PM) are based on or trained on the evolutionary strategies, the genetic algorithms, the genetic programming or the evolutionary programming within a framework of an autonomous selection and/or optimisation process. ### Claim 19 (depends on 1) The method according to claim 1, wherein a group and a new process model are created if state variables (st) of the system are detected which are not yet classified in any group.