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Journal of Polymer & Composites Original Research Special issue Open Access

Optimization of Liquid Metal Nanocomposites and Biogas Addition Rate Using ANN-GA

  1. S. Lalhriatpuia Department of Mechanical Engineering, Delhi Technological University
  2. Neeraj Budhraja Department of Mechanical Engineering, Delhi Technological University
  3. Kiran Pal Department of Mathematics, DITE DSEU Okhla Campus II, Delhi Skill & Entrepreneurship

Abstract

In this study, the liquid metal nanocomposites were investigated using artificial neural network (ANN) prediction capabilities for Compression Ignition (CI) engine performance. The independent input variables selected were load (20-100%), Liquid-metal nanocomposites Doped Rate (NDR, 0-50 ppm), and Biogas Flow Rate (BFR, 0.5-1.0 kg/h). The Central Composite Face-Centered Design (CCFCD) was used in conjunction with the selected input variables and output parameters to assist in the preparation of the Design of Experiment (DOE). The proportion of error for the ANN projected output responses is determined for each run in the DOE. ANN model's predictions exhibited a good coefficient of determination (R2), minimal Root Mean Square error (RMSE), and low Mean Absolute deviation (MAD), indicating accurate and reliable prediction capability. A response that was optimum according to the ANN model's optimization occurred at 74.5% load, 10.55 ppm NDR, and 0.656 kg/h BFR. The optimization response concludes that combining Liquid-metal nanocomposites and biogas contributes positively to diesel engine performances.

Keywords

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