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公开(公告)号：US20230350395A1
公开(公告)日：2023-11-02
申请号：US18026692
申请日：2021-09-16
Applicant: BASF SE
Inventor： Christian-Andreas Winkler , Hans Rudolph , Michael Hartmann , Markus Rautenstrauch , Yuan En Huang , Sebastian Wandernoth , Nataliya Yakut
IPC: G05B19/418
CPC classification number: G05B19/41875 , G05B2219/32368
Abstract: The present teachings relate to a method for controlling a production process, for manufacturing a chemical product, comprising: providing an upstream object identifier comprising input material data and at least one desired performance parameter related to the chemical product; determining a set of process and/or operation parameters based on the upstream object identifier and the at least one desired performance parameter; determining zone-specific control settings for each of the equipment zones based on the determined set of process and/or operation parameters and historical data; providing the zone-specific control settings for controlling the production of the chemical product related to the upstream object identifier. The present teachings also relate to a system for controlling a production process, a use of the control settings, and a software product for implementing the method steps disclosed herein.
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公开(公告)号：US20230390725A1
公开(公告)日：2023-12-07
申请号：US18266955
申请日：2021-12-10
Applicant: BASF SE
Inventor： Christian Andreas Winkler , Hans Rudolph , Michael Hartmann , Markus Rautenstrauch , Yuan En Huang , Sebastian Wandernoth , Nataliya Yakut
IPC: B01J19/00
CPC classification number: B01J19/0033 , B01J19/004 , B01J2219/00243 , B01J2219/00207 , B01J2219/0024 , B01J2219/0022
Abstract: The present teachings relate to a method for monitoring and/or controlling a production process for manufacturing at least one industrial product at an industrial plant comprising at least one equipment by processing at least one input, the method comprising: receiving, via an input inter-face, real-time process data from the equipment; determining, via the computing unit, a subset of the real-time process data; providing as output data the subset of the real-time process data. The present teachings also relate to a system, a use, and a software product.
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公开(公告)号：US20240192661A1
公开(公告)日：2024-06-13
申请号：US18268689
申请日：2021-12-17
Applicant: BASF SE
Inventor： Christian Andreas Winkler , Hans Rudolph , Michael Hartmann , Markus Rautenstrauch , Yuan En Huang , Sebastian Wandernoth , Nataliya Yakut
IPC: G05B19/4155
CPC classification number: G05B19/4155 , G05B2219/32287
Abstract: The present teachings relate to a method for improving a production process for manufacturing a chemical product at an industrial plant comprising at least one equipment and one or more computing units, and the product being manufactured by processing at least one input material, which method comprises: providing at least one desired performance parameter related to the chemical product, determining a set of control settings for controlling the production of the chemical product: wherein the control settings are determined using a scorer module configured to select at least one historical object identifier from a memory storage, wherein the historical object identifier has appended to it historical process parameters and/or operational settings that were used for manufacturing past one or more chemical products. The present teachings also relate to a system for improving the production process, a use and a software program.
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公开(公告)号：US20240024839A1
公开(公告)日：2024-01-25
申请号：US18026686
申请日：2021-09-16
Applicant: BASF SE
Inventor： Christian-Andreas Winkler , Hans Rudolph , Michael Hartmann , Markus Rautenstrauch , Yuan En Huang , Sebastian Wandernoth , Nataliya Yakut
IPC: B01J19/00
CPC classification number: B01J19/0033 , B01J2219/00198 , B01J2219/002 , B01J2219/00209 , B01J2219/00218 , B01J2219/00243
Abstract: The present teachings relate to a method for monitoring a production process for manufacturing a chemical product at an industrial plant, the method comprising: providing an up-stream object identifier comprising input material data, receiving real-time process data from one or more of the equipment zones; determining a subset of the real-time process data based on the upstream object identifier and a zone presence signal; computing at least one zone-specific performance parameter of the chemical product related to the up-stream object identifier based on the subset of the real-time process data and historical data; appending, to the upstream object identifier, the at least one zone-specific performance parameter. The present teachings also relate to a system for monitoring a production process, a dataset, use, a method for generating the dataset and a software program for the same.
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公开(公告)号：US20230409015A1
公开(公告)日：2023-12-21
申请号：US18266964
申请日：2021-12-10
Applicant: BASF SE
Inventor： Christian Andreas Winkler , Hans Rudolph , Michael Hartmann , Markus Rautenstrauch , Yuan En Huang , Sebastian Wandernoth , Nataliya Yakut
IPC: G05B19/418
CPC classification number: G05B19/41865
Abstract: The present teachings relate to a method for improving a production process for manufacturing a chemical product at an industrial plant comprising at least one equipment and one or more computing units, and the product being manufactured by processing at least one input material, which method comprises: receiving real-time process data from the equipment; determining a subset of the real-time process data; computing at least one state related to the input material and/or the equipment. The present teachings also relate to a system for improving the production process, a use, and a software program.
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Patent Agency Ranking
公开(公告)号：US20230341838A1
公开(公告)日：2023-10-26
申请号：US18026690
申请日：2021-09-16
Applicant: BASF SE
Inventor： Christian-Andreas Winkler , Hans Rudolph , Michael Hartmann , Markus Rautenstrauch , Yuan En Huang , Sebastian Wandernoth , Nataliya Yakut
IPC: G05B19/418
CPC classification number: G05B19/418
Abstract: The present teachings relate to a method for controlling a downstream production process for manufacturing a chemical product using at least one precursor material, the method comprising: providing a set of downstream control settings for controlling the production of the chemical product, wherein the downstream control settings are determined based on: a downstream object identifier; the downstream object identifier comprising precursor data; at least one desired downstream performance parameter related to the chemical product; downstream historical data; and wherein the set of downstream control settings is usable for manufacturing the chemical product at the downstream industrial plant. The present teachings also relate to a system, a use and a software product.
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公开(公告)号：US20240061403A1
公开(公告)日：2024-02-22
申请号：US18266967
申请日：2021-12-10
Applicant: BASF SE
Inventor： Christian Andreas Winkler , Hans Rudolph , Michael Hartmann , Markus Rautenstrauch , Yuan En Huang , Sebastian Wandernoth , Nataliya Yakut
IPC: G05B19/4155
CPC classification number: G05B19/4155 , G05B2219/32287
Abstract: The present teachings relate to a method for improving a production process for manufacturing a chemical product using at least one input material at an industrial plant, the industrial plant comprising a plurality of physically separated equipment zones, the method comprising: providing, via an interface, an upstream object identifier comprising input material data; receiving, at a computing unit, real-time process data from one or more of the equipment zones; determining, via the computing unit, a subset of the real-time process data based on the upstream object identifier and a zone presence signal; computing, via the computing unit, at least one zone-specific performance parameter of the chemical product related to the upstream object identifier based on the subset of the real-time process data and historical data; determining, in response to at least one of the performance parameters, a target equipment zone where the input material and/or chemical product is to be sent. The present teachings also relate to a system, and a software program.
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公开(公告)号：US11860617B2
公开(公告)日：2024-01-02
申请号：US17779737
申请日：2020-11-25
Applicant: BASF SE
Inventor： Nataliya Yakut , Simeon Sauer , Mihail Bogojeski , Franziska Horn , Klaus-Robert Mueller
IPC: G05B23/02
CPC classification number: G05B23/0283 , G05B23/0254
Abstract: By accurately predicting industrial aging processes (IAP), such as the slow deactivation of a catalyst in a chemical plant, it is possible to schedule maintenance events further in advance, thereby ensuring a cost-efficient and reliable operation of the plant. So far, these degradation processes were usually described by mechanistic models or simple empirical prediction models. In order to accurately predict IAP, data-driven models are proposed, comparing some traditional stateless models (linear and kernel ridge regression, as well as feed-forward neural networks) to more complex stateful recurrent neural networks (echo state networks and long short-term memory networks). Additionally, variations of the stateful models are discussed. In particular, stateful models using mechanistical pre-knowledge about the degradation dynamics (hybrid models). Stateful models and their variations may be more suitable for generating near perfect predictions when they are trained on a large enough dataset, while hybrid models may be more suitable for generalizing better given smaller datasets with changing conditions.
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公开(公告)号：US20230045548A1
公开(公告)日：2023-02-09
申请号：US17793728
申请日：2021-01-19
Applicant: BASF SE , TECHNISCHE UNIVERSITÄT BERLIN
Inventor： Nataliya Yakut , Mihail Bogojeski , Klaus-Robert Mueller
Abstract: The present invention relates to training predictive data-driven model for predicting an industrial time dependent process. A data driven generative model is introduced for modelling and generating complex sequential data comprising multiple modalities, by learning a joint time-dependent representation of the different modalities. The model may be configured to handle any combination of missing modalities, which enables conditional generation based on known modalities, providing a high degree of control over the properties of the generated sequences.
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公开(公告)号：US20230028276A1
公开(公告)日：2023-01-26
申请号：US17779737
申请日：2020-11-25
Applicant: BASF SE
Inventor： Nataliya Yakut , Simeon Sauer , Mihail Bogojeski , Franziska Horn , Klaus-Robert Mueller
IPC: G05B23/02
Abstract: By accurately predicting industrial aging processes (IAP), such as the slow deactivation of a catalyst in a chemical plant, it is possible to schedule maintenance events further in advance, thereby ensuring a cost-efficient and reliable operation of the plant. So far, these degradation processes were usually described by mechanistic models or simple empirical prediction models. In order to accurately predict IAP, data-driven models are proposed, comparing some traditional stateless models (linear and kernel ridge regression, as well as feed-forward neural networks) to more complex stateful recurrent neural networks (echo state networks and long short-term memory networks). Additionally, variations of the stateful models are discussed. In particular, stateful models using mechanistical pre-knowledge about the degradation dynamics (hybrid models). Stateful models and their variations may be more suitable for generating near perfect predictions when they are trained on a large enough dataset, while hybrid models may be more suitable for generalizing better given smaller datasets with changing conditions.
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