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A Soft Sensor Approach Based on an Echo State Network Optimized by Improved Genetic Algorithm.

Ruoyu Huang1,2, Zetao Li1, Bin Cao3

  • 1The Electrical Engineering College, Guizhou University, Guiyang 550025, China.

Sensors (Basel, Switzerland)
|September 9, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an improved genetic algorithm-optimized echo state network (IGA-ESN) for soft sensor development. This data-driven approach enhances fault diagnosis and operational safety by accurately estimating unmeasurable industrial process variables.

Keywords:
alumina concentrationaluminum reduction cellecho state network (ESN)genetic algorithm (GA)soft sensor

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Area of Science:

  • Industrial process monitoring
  • Data-driven modeling
  • Artificial intelligence applications

Background:

  • Accurate measurement of state variables is critical for industrial process fault diagnosis and safety.
  • Direct measurement is often infeasible due to economic, technological, environmental, or spatial constraints.
  • Soft sensors offer a solution by estimating unmeasurable variables from measurable ones.

Purpose of the Study:

  • To develop a novel data-driven soft sensor approach using an echo state network (ESN).
  • To enhance the prediction performance of the ESN through optimization using an improved genetic algorithm (IGA).
  • To validate the efficacy of the proposed IGA-ESN soft sensor model in a real-world industrial application.

Main Methods:

  • Established a data-driven model (DDM) using an ESN to link secondary and dominant variables.
  • Optimized ESN parameters with an IGA, incorporating an immigration strategy and adaptive crossover/mutation operators.
  • Developed an improved genetic algorithm-echo state network (IGA-ESN) model.
  • Validated the model by estimating alumina concentration in an aluminum reduction cell.

Main Results:

  • The IGA effectively optimized ESN parameters, improving prediction performance.
  • Adaptive operators and immigration strategy enhanced algorithm convergence speed and avoided local optima.
  • The IGA-ESN model demonstrated high efficiency in estimating alumina concentration.
  • Experimental results showed a significant reduction in estimation error compared to traditional methods.

Conclusions:

  • The proposed IGA-ESN soft sensor is an efficient and effective method for estimating unmeasurable variables in industrial processes.
  • This approach significantly improves fault diagnosis and operational safety evaluations.
  • The method offers a robust solution for overcoming limitations in direct variable measurement.