Development of machine learning model for determination of contamination length in a multi-product pipeline

Abstract

Batch transfer results in contamination over the length of travel of the fluids in product pipelines. Mathematical models have been in use. Machine learning with Python, due to higher efficiency was applied to determine contamination length. Six models were developed and the best with an accuracy of 97.4% and RMSE score of 262.5 was developed. It predicts with higher precision and also accurately ranks the input variables in order of their influence on transmix length. The distance of travel had the highest influence on the amount of contamination in a pipeline, followed by Reynolds number and pipe diameter.