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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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A steady-state target calculation method based on "point" model for integrating processes.

Qiang Pang1, Tao Zou2, Yanyan Zhang3

  • 1Dept. of Information Service & Intelligent Control, Shenyang Institute of Automation, Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China; State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China.

ISA Transactions
|December 21, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces an optimization method to improve steady-state target calculations for integrating processes by using a "point" model. The approach accurately predicts future outputs, minimizing errors and ensuring variables stay within constraints.

Keywords:
Integrating processModel uncertaintySteady-state target calculationTwo-layer model predictive control“Point” model

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Area of Science:

  • Chemical Engineering
  • Process Systems Engineering

Background:

  • Model uncertainty significantly impacts steady-state target calculations in integrating processes.
  • Accurate steady-state targets are crucial for efficient process operation.

Purpose of the Study:

  • To develop an optimization method that eliminates model uncertainty in steady-state target calculations.
  • To introduce a method for determining the feasibility of steady-state targets.

Main Methods:

  • A two-stage optimization framework was employed for integrating processes.
  • A simplified "point" model was utilized for steady-state prediction, with error compensation for the actual process.
  • A feasibility determination method for steady-state targets was developed.

Main Results:

  • The optimization method successfully restricted integrating variable outputs within defined constraints.
  • Simulation results showed minimal calculation errors between actual outputs and optimal set-points.
  • The steady-state prediction model demonstrated high accuracy in forecasting future outputs of integrating variables.

Conclusions:

  • The proposed optimization method effectively addresses model uncertainty in steady-state target calculations.
  • The developed "point" model provides accurate predictions for integrating processes.
  • The approach ensures reliable and constrained steady-state operation.