The single volume and multi volume liquid level control experiments, by comparing simplified and complex systems, intuitively reveal the dynamic characteristics and coupling effects of industrial processes, providing key experimental basis for understanding the behavioral laws of actual industrial systems.
The single volume liquid level control experiment focuses on the dynamic characteristics of the liquid level in a single container, and observes the response curve of the liquid level (output) by adjusting the inlet flow rate (input). Experiments have shown that a single volume system can be simplified as a typical first-order inertia element, and its dynamic characteristics are manifested as a smooth transition of liquid level with flow rate changes, without significant overshoot or oscillation. By adjusting the proportional (P), proportional integral (PI), and proportional integral derivative (PID) control parameters, the impact of control strategies on system response speed and steady-state error can be further analyzed. For example, proportional control can respond quickly but has steady-state errors, integral control can eliminate errors but may cause overshoot, and differential control improves system stability by predicting the trend of deviation changes. Single volume experiments lay the foundation for understanding the dynamic behavior of single variable control systems.
The multi volume liquid level control experiment simulates complex industrial scenarios with multiple variables and strong coupling by connecting multiple water tanks in series and introducing coupling effects between liquid levels. In the experiment, changes in the liquid level of the upstream water tank will affect the downstream water tank through water flow transmission, forming dynamic coupling. This coupling leads to an increase in system order (such as second-order for dual capacity systems and third-order for triple capacity systems), significantly complicating the dynamic characteristics, manifested as response delay, increased overshoot, and prolonged adjustment time. For example, PID controllers are significant in single capacity systems, but may fail in three capacity systems due to coupling effects. Decoupling control strategies (such as fuzzy decoupling and feedforward compensation) are needed to decompose the multivariable system into independent control loops to restore control performance. The multi volume experiment revealed the essence of the interaction between variables in industrial processes, emphasizing the profound impact of coupling effects on the dynamic characteristics of the system.