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On the Investigation of Effective Factors on Higher Heating Value of Biodiesel: Robust Modeling and Data Assessments.

BioMed research international·2021
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Gaussian process regression (GPR) models accurately estimate biodiesel density. The rational quadratic GPR model demonstrated superior performance, identifying pressure as the most influential factor.

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Area of Science:

  • Chemical Engineering
  • Materials Science
  • Data Science

Background:

  • Biodiesel density is a critical property for its application and processing.
  • Accurate prediction of biodiesel density is essential for process optimization and safety.
  • Existing methods for density estimation may lack precision or applicability across various conditions.

Purpose of the Study:

  • To develop and compare Gaussian process regression (GPR) models for estimating biodiesel density.
  • To identify the key parameters influencing biodiesel density.
  • To provide a reliable tool for engineers in the biodiesel industry.

Main Methods:

  • Four different Gaussian process regression (GPR) approaches utilizing various kernel functions were employed.
  • Models were trained and validated using experimental data correlating density with pressure, temperature, molecular weight, and normal melting point.
  • Model performance was evaluated using metrics such as Root Mean Square Error (RMSE), Mean Squared Error (MSE), Mean Relative Error (MRE), R-squared (R²), and Standard Deviation (STD).

Main Results:

  • All proposed GPR models exhibited good accuracy in estimating biodiesel density.
  • The rational quadratic GPR model achieved the best performance, with RMSE = 0.47, MSE = 0.22, MRE = 0.04, R² = 1, and STD = 0.3.
  • Pressure was identified as the most significant parameter affecting biodiesel density, with a relevancy factor of 0.59.

Conclusions:

  • Gaussian process regression offers a highly accurate method for predicting biodiesel density.
  • The rational quadratic GPR model is recommended for its superior predictive capabilities.
  • Understanding the influence of parameters like pressure is crucial for optimizing biodiesel production and utilization, contributing to environmental protection and recovery improvement.