Dynamic control and performance evaluation of a microcontroller-based smart industrial heat extractor involves the implementation of control strategies and the assessment of its performance under dynamic operating conditions. Performance evaluation aims to assess the effectiveness and efficiency of the microcontroller-based smart industrial heat extractor under dynamic conditions. Industrial heat extraction systems are often complex, involving multiple components, sensors, actuators, and control algorithms. Understanding and modelling the dynamic behaviour of these systems can be challenging, especially when considering factors like heat transfer rates, thermal delays, and interactions between different system elements. The effectiveness of dynamic control is significantly dependent on precise and dependable measurements from sensors. Sensors deployed in industrial settings may encounter severe environmental conditions, which can result in possible inaccuracies, drift, or even malfunctions. The objective of this research is to propose a simulated methodology for verifying the efficacy of a microcontroller-driven intelligent heat extractor utilised in industrial settings. The execution of experiments or tests within industrial environments can be a costly and time-intensive endeavour, and may entail potential hazards. The efficacy of a smart industrial heat extractor in practical industrial settings can be ascertained through the simulation of its evaluation process, thereby mitigating potential risks. The model designed and simulated in this work utilises an integrated temperature sensor to determine the ambient temperature and transmits a signal to the Arduino UNO microcontroller when the temperature sensor detects variant temperatures ranges. The evaluation is performed by comparing the behaviour and performance of the simulated system with predefined performance metrices.
Keywords: Microcontroller, Neural network, cooling system, heat extraction, sensor.
