Optimization of coal blending in thermal power plants using G ray‐ T aguchi method: An operations management perspective

Abstract Coal quality variability significantly affects the efficiency, operating cost, and environmental performance of coal‐fired thermal power plants. This study aims to optimize coal blending by integrating the Taguchi Design of Experiments (DOE) with Gray Relational Analysis (GRA) for simultaneous optimization of calorific value, ash content, and fuel cost. Three factors, viz imported coal percentage, domestic coal percentage, and operational adjustment factor, were investigated using an L9 orthogonal array. Regression modeling and Analysis of Variance were employed to evaluate factor significance and validate the developed models. The calorific value and cost models exhibited coefficients of determination ( R 2 ) of 94.77% and 98.45%, respectively, while the ash content model achieved an R 2 of 89.06%. GRA identified the optimal blend, providing the best compromise among the three responses with a Gray Relational Grade of 0.6477. The proposed methodology offers a practical and statistically robust decision‐support framework for improving operational efficiency, reducing ash generation, and minimizing fuel costs in thermal power plants.