Accuracy Comparison of Data-Driven Modeling Techniques for a Modified Quadruple Tank Process in Energy-Efficient Control

In energy critical systems, accurate modeling of fluid dynamics is essential for designing controllers that enhance operational performance and reduce energy consumption. This study presents a data-driven modeling framework for the Modified Quadruple Tank Process (MQTP) utilizing three distinct system identification techniques: Transfer Function (TF) State-Space (SS) and Process Model (PM) to comparatively assess their accuracy in capturing the system’s dynamic behavior. Each model is evaluated based on its ability to replicate the nonlinear interactions and predict liquid level variations across the four interconnected tanks. The TF model exhibits high fidelity for the lower tanks (T1 and T2) achieving Best-fit values of 88.37% and 84.88% respectively. While its accuracy is lower for the upper tanks (T3 and T4), with a Best-fit value of 67.77%, it remains sufficient for the intended control application, staying within acceptable error thresholds. Closed-loop validation using Mean Absolute Percentage Error (MAPE) and Normalized Root Mean Square Error (NRMSE) confirms the TF model’s effectiveness in tracking liquid levels particularly in T1 and T2 (MAPE = 10.05% and 9.13%). Despite lower performance in T3 and T4, the model remains within acceptable error thresholds (MAPE ≤ 16%). The study contributes to the modeling of MQTP systems by demonstrating the viability of integrating transfer function models with real-time experimental data, addressing a critical gap in predictive modeling for complex fluid dynamics in energy applications. These findings support the development of energy-efficient control strategies through accurate system representation.