Implementation of Plant Model of an Air Vented Dryer system using Data Driven Approach in MATLAB/Simulink

Now a days, physics-based models are extensively employed across diverse domains to accurately replicate the real-world physics of systems. These systems use mathematical equations and rules derived from physics laws. However, the deployment of physics-based models in simulation environment poses significant challenges because they are high fidelity and computationally complex models. There is a need to leverage these models in control and validation environments to ensure accuracy, stability, and optimal performance. One approach to leverage these models is through the use of Functional Mockup Interface (FMI) standards, which bridges the gap between physical modeling and control system design. But there are also challenges while deploying them in a simulation environment. To address the challenges associated with it, a new approach of data driven modeling using neural network has been studied in this paper. The developed neural network model aims to capture the dynamic behavior of the Air-vented dryer system, enabling smooth integration with MATLAB for Model-In-Loop and Hardware-In-Loop simulation. This approach helps better for simulating high fidelity systems, benefiting various industries which need dynamic system simulations.