Statistical Characterization and Process Control Assessment of Key Operational Parameters in Applied Engineering Systems
DOI:
https://doi.org/10.38032/jea.2025.03.004Keywords:
Statistical Process Control, Distribution Fitting, Chemical Purity, Control Charts, Specific Optical Rotation, Water ContentAbstract
Ensuring consistent raw material quality is a significant challenge in chemical manufacturing, particularly for medicinal compounds where safety and efficacy are paramount. In these situations, a unique methodology known as Statistical Process Control (SPC) come into play. This study provides statistical process control analysis of four critical operational parameters for most the raw chemical compounds, especially in the medicinal chemistry— Specific Optical Rotation (SOR), Water Content (WC), RI, and Chromatographic Purity (CP)—derived from a dataset of 26 observations in an applied engineering context. The methodology encompasses descriptive statistics, rigorous distribution identification using Goodness-of-Fit tests, and process stability assessment via Individual- Moving Range (I-MR) and Exponentially Weighted Moving Average (EWMA) control charts. Descriptive statistics revealed diverse data characteristics, notably the high positive skewness (2.623) and kurtosis (9.386) of WC (Mean ± Standard Deviation: 0.177±0.106987) and the presence of negative values for SOR (Mean: -0.1, Min: -2, Max: 2). Distribution fitting identified Logistic and Normal as the most suitable for SOR, while RI demonstrated a best fit for normal distribution with Johnson Transformation. WC and CP exhibited significant non-normality and challenges in fitting standard distributions, often accompanied by warnings regarding convergence or parameter estimation stability. Crucially, control chart analysis identified significant out-of-control conditions for SOR, WC, and RI, indicating inherent process instability. CP, conversely, demonstrated stability with the optimized EWMA chart. The findings underscore the necessity of tailored statistical approaches for diverse data characteristics in quality control. Implementation of Statistical Process Control should not be underestimated in the chemical manufacturing industry, notably in the developing nations.
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