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Application of Machine Learning Technique to Predict Crude Distillation Column Inlet Temperature / Furnace Coil Outlet Temperature in Order to Maximize Distillate Yield and to Minimize Fuel Firing in Furnaces
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 174 - Number 25 |
| Year of Publication: 2021 |
| Authors: Debdatta Kundu, Tejas Khanolkar, Tirth Shah, Sarvesh Bangad |
10.5120/ijca2021921163
|
Debdatta Kundu, Tejas Khanolkar, Tirth Shah, Sarvesh Bangad . Application of Machine Learning Technique to Predict Crude Distillation Column Inlet Temperature / Furnace Coil Outlet Temperature in Order to Maximize Distillate Yield and to Minimize Fuel Firing in Furnaces. International Journal of Computer Applications. 174, 25 ( Mar 2021), 28-33. DOI=10.5120/ijca2021921163
Abstract
The optimization of furnace firing in a Crude Distillation Unit (CDU) helps refineries to save fuel and to boost up refinery margin by increasing distillate yield. The paper is focused on development of a suitable model to predict Crude Distillation Column inlet temperature / furnace coil outlet temperature (COT) of a petroleum refinery by using Machine Learning techniques. Different regression algorithms are used to fit the given data and error functions are computed for the different models. Their performance is then compared to select the best performing model. The models are developed based on actual operating data from the Crude Distillation Unit of an existing petrochemical refinery and the outputs are tested to predict the optimum range of COT and Random Forest Regressor is found to be the best model for predicting optimum Furnace COT values of a Crude Distillation Column based on the given features.
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