Enhanced Prediction and Optimization of Oil-Water Emulsion Stability Through Application of Machine Learning and Explainable Artificial Intelligence on TFBG Sensor Data

In the petroleum industry, crude oil is extracted in the form of an oil-water emulsion (OWE). Understanding this emulsion is crucial for further processing and utilization. This research investigates the use of experimental data from a tilted fiber Bragg grating (TFBG) sensor to characterize and quantify the stability of OWE while focusing on the impact analysis of parameters using machine learning (ML) and explainable artificial intelligence (XAI) techniques. The dataset consisting of experimental TFBG spectra (wavelength range: 1250-1650 nm) included parameters such as revolutions per minute (RPM) of the rotator, surfactant concentration (C-s), and area measurements (indicating OWE stability). Applying interpolation techniques, the dataset was augmented for effective training and testing of ML models. The results indicated that the random forest model enabled the highest R-2 value of 99.2% on the test data. Then, XAI techniques (namely, shapley additive explanations and local interpretable model-agnostic explanations) were applied (from both global and local interpretations viewpoints) to determine the contribution of each feature (i.e., RPM and C-s). It was found that C-s has a significantly greater impact on OWE stability than RPM. Wavelength (lambda) was subsequently included in the ML and XAI analyses. The results again confirmed that C-s remained the most significant factor in determining OWE stability, with reasonably lesser impacts of lambda and RPM. This study provides valuable insights into designing procedures and optimizing parameters for OWE stability during crude oil processing.