公开(公告)号：US20250099937A1

公开(公告)日：2025-03-27

申请号：US18294753

申请日：2022-07-27

Applicant: BASF SE

Inventor： Satya Swarup SAMAL ,  Olga WALZ ,  Hayder SCHNEIDER

IPC: B01J19/00 ,  G05B13/02

Abstract: The present invention relates to a computer-implemented method for monitoring and/or controlling a chemical plant. Specifically, the present invention relates to a computer-implemented method for monitoring and/or controlling a physical-chemical process in a chemical plant comprising: (a) receiving sensor data related to the physical-chemical process, (b) determining at least one physical-chemical parameter by providing the sensor data to a plant model, wherein the plant model comprises a mechanistic model containing at least two equations each representing a part of the physical-chemical process and a data-driven model associated to the mechanistic model, wherein the total number of scalars as output parameters from the data-driven model is lower than the number of equations of the mechanistic model, and (c) outputting the at least one physical-chemical parameter determined by the plant model.

公开(公告)号：US20240160161A1

公开(公告)日：2024-05-16

申请号：US18282296

申请日：2022-03-11

Applicant: BASF SE

Inventor： Satya Swarup SAMAL ,  Hayder SCHNEIDER ,  Robert PACK

IPC: G05B13/04 ,  G05B13/02

CPC classification number: G05B13/042 ,  G05B13/0265

Abstract: The present teachings relate to a method for modeling an industrial plant comprising a plurality of equipment, the method comprising: providing a plant level model of the industrial plant; wherein the plant level model has been generated via a topology generator by automatically selecting and interconnecting equipment models from a model library; obtaining, using a model trainer, a trained plant level model; wherein the trained plant level model is obtained from the plant level model by training at least some of the equipment models in the plant level model using one or more historical datasets; wherein the trained plant level model is usable for computing at least one performance parameter via a model executor. The present teachings also relate to a framework, a software product, a use of the model and a use of the performance parameter.