A novel approach to oil-water mixture analysis: Microstrip antenna-based sensor combined with GMDH neural network

Microstrip antenna sensors are gaining recognition in the oil industry for their compact design, cost-effectiveness, and versatility in measuring complex fluid mixtures. This study presents a novel microstrip antenna-based sensor featuring a quasi-circular resonator, designed specifically for measuring the volumetric content of oil in oil-water biphasic mixtures. Operating at three distinct resonance frequencies (5.88 GHz, 6.96 GHz, and 8.1 GHz) and housed within a 3D-printed PLA cylindrical structure, the sensor accurately analyzes spectral responses for 21 different volumetric oil-water ratios within a fixed volume of 20 mL. To address the challenge of reproducibility often associated with microstrip sensors, each mixture was tested five times and the data was fed into a Group Method of Data Handling (GMDH)-type neural network. This approach enhances the neural network's resistance to minor input variations, ensuring more reliable predictions. The network successfully predicted the volumetric oil content with an Root Mean Square Error (RMSE) of less than 0.93 and an R2 value of 0.99. With an average sensitivity of 4.26 MHz/εr, the sensor demonstrates high effectiveness in fluid characterization, particularly in oil industry applications where precise, real-time monitoring is essential. This study not only advances microstrip antenna sensor technology but also highlights its practical utility in enhancing operational efficiency and decision-making processes within the oil sector. •Innovative Sensor Design: Introduction of a microstrip antenna sensor equipped with a quasi-circular resonator, designed to accurately measure volumetric water-oil content in biphasic mixtures, showcasing its capability to operate effectively at distinct resonance frequencies.•Advanced Data Analysis Techniques: Utilization of a GMDH-type neural network to enhance measurement accuracy amidst environmental noise, enabling precise estimation of volumetric percentages with high reliability.•High Sensitivity and Performance: The sensor demonstrated an average sensitivity of 4.26 MHz/εr, making it highly effective for applications in the oil industry where precise fluid characterization is crucial.•Robust Experimental Setup: Detailed description of the experimental setup using a PLA cylindrical structure and 3D printing technology, which facilitated accurate assessments of fluid interactions within the sensor environment.•Impactful Results and Implications: The study confirmed the sen