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The ASTM D3230 electrochemical cell plays a pivotal role in measuring oil salt content in the southern Iranian oil fields, employing a solvent blend of 63 % butanol and 37 % methanol. However, the growing application of this method raises concerns about the substantial daily requirement of 40 L of solvents as well as the device's significant power consumption within a laboratory setting. This study analyzes a year-long dataset comprising 103 filtered samples and leverages data mining techniques to investigate interactions among descriptive statistical parameters within crude oil compositions, thereby enhancing the efficiency, operational stability, and performance monitoring of the D3230 device. Key statistical attributes, including the structure and population characteristics of the data, are thoroughly analyzed. Various statistical tests, including ANOVA, are employed to develop significant regression models that offer an evolutionary and augmented reality perspective on the statistical and experimental data from the D3230 electrochemical cell, Petrotest device, and the IP 77/79 method. This approach facilitates a better understanding of the optimal performance of salt measurement devices and the identification of comprehensive optimization functions for salt measurement methodologies. The findings indicate a remarkable accuracy in the structure and properties of the statistical data population, illustrated by a correlation coefficient of R2 = 0.9987 and a low root mean square error (RMSE) of 0.637588. The importance of quality control is further underscored by a one-tailed p-value of 0.4870219 and a critical t-value of 1.6523573. Additionally, this study evaluates the uncertainty of the data while searching for and identifying effective performance indices, thus aiming to enhance the intelligence of the D3230 salt measuring device against the dynamic backdrop of crude oil composition. The reproducibility of statistical results across various salt content measurement methods is also addressed. This research serves as a comprehensive guide through statistical process and learning, optimizing the use of methanol and butanol, and improving the operational effectiveness of the electrochemical cell device. It achieves this by elucidating optimal data set combinations and categorizing the structure and behavior patterns of statistical data in the context of crude oil dynamics and salt measurement devices. Ultimately, the study contributes to a thermodynamic balance and optimization between measurement patterns, enhancing energy efficiency and minimizing environmental impacts. This foundation is critical for the future integration of artificial intelligence in industrial laboratory applications within the Iran-Ahvaz oil fields. |
