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A Virtual Combustion Sensor Based on Ion Current for Lean-Burn Natural Gas Engine.

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A novel sensor uses ion current to monitor marine natural gas engine combustion in real-time without engine modification. Neural network models accurately predict key combustion parameters, achieving high accuracy with low errors.

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Area of Science:

  • Marine Engineering
  • Combustion Science
  • Sensor Technology

Background:

  • Real-time monitoring of marine natural gas engine combustion is crucial for efficiency and emissions.
  • Existing methods often require engine structural modifications or lack comprehensive parameter detection.
  • Ion current signals offer a non-invasive approach to infer combustion characteristics.

Purpose of the Study:

  • To design and validate an innovative sensor for real-time detection of key combustion parameters in marine natural gas engines.
  • To establish the correlation between ion current signals and critical combustion phases (chemical and thermal).
  • To develop and evaluate neural network models for online prediction of combustion parameters using ion current data.

Main Methods:

  • Acquisition of ion current data across a wide engine operating range.
  • Analysis of correlations between ion current parameters and combustion parameters (load, excess air coefficient, ignition timing).
  • Development of four neural network models (BP and RBF, with and without thermal phase consideration) for online sensing.
  • Validation of model predictions against experimental data.

Main Results:

  • Ion current is highly correlated with marine natural gas engine combustion phases and parameters.
  • Neural network models effectively predict combustion parameters using extracted ion current features.
  • The BP (with thermal) model showed highest accuracy for phase and amplitude parameters.
  • The RBF (with thermal) model demonstrated highest accuracy for emission parameters.
  • Mean Absolute Percentage Errors (MAPE) were generally below 0.25, indicating high prediction accuracy.

Conclusions:

  • The developed ion current-based virtual sensor provides accurate, real-time monitoring of marine natural gas engine combustion.
  • The sensor eliminates the need for engine structural modifications, offering broad applicability.
  • Neural network models, particularly BP (with thermal) and RBF (with thermal), are effective for online combustion parameter prediction.