Amine Foaming Modelling and Reduction Using Binary Logistic Regression

Purpose – Foaming is one of the main causes of operational shutdowns in natural gas
processing plants in general and sweetening processes in specific. This study is carried out to investigate the main factors impacting foaming at natural gas processing plant and provide proposals for foaming reduction
Design/methodology/approach – Historical data of foaming events in a chemical plant will be analyzed using binary logistic regression to determine critical factors affecting foaming and propose ways to control such undesired phenomenon
Findings – Focusing on potential causes, a more proactive and less expensive approach is considered instead of antifoam injection reactive approach. Inlet gas pressure, heat stable salts, total suspended solids, and iron impurities are the most significant contributing factors to foaming. Maintaining inlet pressure below 1045 psi, heat stable salts below 2.7%, suspended solids below 10 g/l, and iron concentrate below 9 ppm will substantially reduce amine foaming.
Research limitations/implications– Findings and analysis is applicable to the chemical plant under study, different processes require repeating same analysis
Practical implications– Based on plant under study historical records, the minimum time required to bring the plant back on during the shutdown is around 20 minutes and foaming incidents occurred 121 times during the period of February 2009 to December 2013. As a result, the total income loss during this period can be estimated as more than $700,000. Optimal settings of factors under study according to recommendations above is expected to result in 85% reduction of foaming events which represent a significant improvement over the current performance.

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Foaming, Binary Logistic regression, Sweetening, Sour gas.