20240036533. Method for Identifying a Process Model for Model-Based, Predictive Multivariable Control of a Process Installation simplified abstract (Siemens Aktiengesellschaft)

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Method for Identifying a Process Model for Model-Based, Predictive Multivariable Control of a Process Installation

Organization Name

Siemens Aktiengesellschaft

Inventor(s)

Bernd-Markus Pfeiffer of Uttenreuth (DE)

Method for Identifying a Process Model for Model-Based, Predictive Multivariable Control of a Process Installation - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240036533 titled 'Method for Identifying a Process Model for Model-Based, Predictive Multivariable Control of a Process Installation

Simplified Explanation

The abstract of the patent application describes a computer-implemented method for automatically identifying a process model for a model-based, predictive multivariable control of a process installation. The method involves referencing previously defined controlled variables, manipulated variables, and disturbance variables for the control of the process installation.

  • The patent application is for a computer-based method for identifying a process model for controlling a process installation.
  • The method utilizes a model-based, predictive multivariable control approach.
  • The process model is automatically identified by referencing pre-defined controlled variables, manipulated variables, and disturbance variables.
  • The identified process model is used for controlling the process installation.

Potential Applications:

  • Industrial process control systems
  • Manufacturing plants
  • Chemical processing plants
  • Power generation facilities

Problems Solved by this Technology:

  • Manual identification of process models can be time-consuming and prone to errors.
  • Traditional control methods may not effectively handle multivariable control in complex processes.
  • Lack of accurate process models can lead to inefficient control and suboptimal performance.

Benefits of this Technology:

  • Automation of process model identification saves time and reduces errors.
  • Model-based, predictive multivariable control improves the efficiency and performance of process installations.
  • Accurate process models enable better control and optimization of complex processes.


Original Abstract Submitted

a computer-implemented method for the automated identification of a process model for a model-based, predictive multivariable control of a process installation, wherein reference is made to previously defined controlled variables, manipulated variables and disturbance variables for the model-based, predictive multivariable control of the process installation.

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