公开(公告)号：US20220284391A1

公开(公告)日：2022-09-08

申请号：US17632609

申请日：2020-09-10

Applicant: BASF SE

Inventor： Stefan Fischer ,  Jesper Nielsen ,  Simeon Sauer ,  Grit Baier

IPC: G06Q10/08 ,  G06Q50/28 ,  G01K3/04

Abstract: The present invention relates to a computer-implemented method for activity based enzyme formulation management of an enzyme formulation comprising (i) receiving input data, preferably via an input unit (10), of at least one storage segment data defined by at least temperature and storage duration and an initial enzyme activity value of said enzyme Predicted degradation formulation; (ii) determining, specifically calculating, a remaining activity value of the enzyme formulation based on the storage segment data and the initial enzyme activity value via a processing unit (20); (iii) providing a remaining activity value for the enzyme formulation, preferably via an output unit (30), and (iv) managing said enzyme formulation based on the remaining activity value of step (iii), said managing preferably comprising at least one of—providing a dosage recommendation based on the remaining activity value of the enzyme formulation, preferably via an output unit (30);—providing a residual shelf life indicator for said enzyme formulation based on the remaining activity value of the enzyme formulation; —automated adjustment of a dosage of the enzyme formulation by controlling of a dosing equipment; and/or—eliciting an order of a batch of enzyme formulation if the remaining activity value is indicative of the total enzyme activity in the enzyme formulation being below a pre- determined threshold value. The present invention also relates to an apparatus for activity based enzyme formulation management of an enzyme formulation, comprising: —an input unit (10) configured to receive a data input, preferably a user interface, wherein the data input comprises storage segment data defined by at least temperature and duration and an initial enzyme activity value of said enzyme formulation; —a processing unit (20), preferably a processing unit comprising at least one processor, configured, specifically by programming, to determine, specifically to calculate, a remaining activity value of the enzyme formulation based on the storage segment data and the initial enzyme activity value; and—an output unit (30) configured to output the remaining activity value for the enzyme formulation to the user and/or to a data interface.; and to a system comprising said apparatus. The present invention further relates to methods, computer programs, data carriers, and uses related to the aforesaid method, apparatus, and system.

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.

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.