公开(公告)号：US20220243133A1

公开(公告)日：2022-08-04

申请号：US17629435

申请日：2020-07-27

Applicant: BASF SE

Inventor： Simeon SAUER ,  Daniel KECK ,  Eric JENNE ,  Alexander BADINSKI ,  Miriam Angela Anna HAHKALA ,  Bart BLANKERS ,  Hendrik DE WINNE ,  Britta Carolin BUCK

IPC: C10G9/36 ,  C10G9/16 ,  C10G9/20 ,  F27D19/00 ,  G06Q10/04

Abstract: In order to predict the future evolution of a health-state of an equipment and/or a processing unit of a chemical production plant, e.g., a steam cracker, a computer-implemented method is provided, which builds a data-driven model for the future key performance indicator based on the key performance indicator of today, the processing condition of today, and the processing condition over a prediction horizon.

公开(公告)号：US20250067630A1

公开(公告)日：2025-02-27

申请号：US18721004

申请日：2023-01-16

Applicant: BASF SE

Inventor： Alexander BADINSKI ,  PHILLIP RUEHL ,  Martin DIERKEN ,  Sebastian STEINHAEUSER ,  Harro BONNOWITZ ,  Heiko KULINNA

IPC: G01M99/00

Abstract: Disclosed herein is a method for monitoring a plant capable of: receiving one or more educts; and executing multiple processes in which the educts are processed, where each of the processes is characterized by an associated set of process parameters; the method including: comparing a set of process parameters of interest and the remaining sets of process parameters associated with the remaining multiple executed processes to determine a similarity degree between the set of process parameters of interest and the remaining sets of process parameters; determining at least one similar process from the remaining multiple executed processes, the similar process having a similarity degree that is equal to or greater than a similarity threshold; and outputting the set of process parameters of interest together with the set of process parameters associated with the determined at least one similar process.

公开(公告)号：US20240241483A1

公开(公告)日：2024-07-18

申请号：US18559102

申请日：2022-05-27

Applicant: BASF SE

Inventor： Phillip RUEHL ,  Alexander BADINSKI ,  Gennadij HEIDEL

IPC: G05B13/02

CPC classification number: G05B13/0265

Abstract: Disclosed herein is a computer-implemented regressor for simulating, monitoring and/or controlling a batch plant. The batch plant is implemented to receive one or more educts having associated educt quality parameters (x1, x2), to process the educt(s) where the process has associated process parameters (yj), and to output a product having associated product quality parameters (Q1, Q2). The regressor includes at least two regressor units based on machine-learning principles, each regressor unit having an input for receiving input data, and an output for outputting output data. A first regressor unit is implemented to receive the educt quality parameters (xi) and to output at least one educt impact parameter (R1). And a second regressor unit is implemented to receive the educt impact parameter (R1) and l process parameters (yj) and to output at least one product quality parameter (Qj).