Innovative composite machine learning approach for biodiesel production in public vehicles

Abstract

Predictive modeling is revolutionised by the use of artificial intelligence (AI) in this paper. AdaBoost regression, a potent algorithm for machine learning, is utilised. It excels at handling complex relationships between input and output variables and making accurate predictions. AdaBoost regression is preferred due to its reliability and ability to identify informative patterns. It achieves high precision and performance despite using a small number of regressors. AdaBoost regression proves to be exceptionally effective in the optimization of process parameters. By utilizing historical data and training the model, the optimal settings for improving process outcomes are determined. Improvements in yield, increased conversion rates, and resource optimization are a few of the valuable insights and recommendations. AdaBoost regression handles multiple input variables, including blend composition, speed, temperature, and duration, allowing for extensive modeling and analysis. Variables such as viscosity, oxidation stability, flash point, and density are included in the predictions. AdaBoost regression is a reliable tool that is well-known in a variety of industries, including finance, healthcare, and manufacturing. This paper emphasises its high accuracy and dependability for making informed decisions, optimizing operations, and attaining superior performance. In conclusion, this paper demonstrates the transformative power of AI in predictive modeling through AdaBoost regression. It plays a crucial role in optimizing process parameters and driving success across a variety of applications due to its capacity to handle complex relationships and provide accurate predictions.