In the stainless steel molecular distillation system, PLC (Programmable Logic Controller) achieves automated control of the distillation process through precise control algorithms and logic design, ensuring separation efficiency and product quality. The core control strategies can be divided into the following two categories:
1、 Basic control algorithm: achieving precise control of core parameters
PID control algorithm: For key parameters such as distillation temperature and pressure, PLC adopts PID algorithm to achieve closed-loop control. For example, in evaporation temperature regulation, PLC collects real-time data through temperature sensors, compares it with the set value, and dynamically adjusts the power of the heating element. The proportional (Kp), integral (Ki), and derivative (Kd) parameters of the PID algorithm need to be optimized according to the system characteristics to balance response speed and stability. A certain multi-stage molecular distillation device uses fuzzy self-tuning PID control to control the temperature fluctuation range within ± 0.5 ℃, which saves 12% energy compared to traditional PID control.
ON/OFF control algorithm: used for start stop control of simple actuators, such as vacuum pumps, condenser fans, etc. When the system pressure is below the set threshold, the PLC triggers an ON signal to start the vacuum pump; Automatically shut down after pressure recovery to avoid energy waste.
2、 Advanced Logic Control: Ensuring System Security and Process Coherence
State machine control algorithm: Define the states of "preheating evaporation condensation discharge" in the distillation process, and implement sequential control through conditional transition logic. For example, when the evaporation temperature reaches the set value and the pressure stabilizes, the PLC automatically switches to the condensing state, starts the film scraping motor, and adjusts the condensate flow rate.
Fuzzy logic control: In response to the nonlinear characteristics of molecular distillation, PLC can integrate a fuzzy control module. For example, when the feed flow rate fluctuates, the fuzzy controller dynamically adjusts the heating power and film scraping speed based on the flow error (large/medium/small) and change rate (fast/slow) to quickly restore the system to steady state. A certain case shows that fuzzy control reduces the standard deviation of product purity by 40%.
Safety interlock logic: PLC builds multiple safety protections through hardware redundancy and software interlocking. For example, when the temperature exceeds the limit or the pressure is abnormal, the PLC immediately cuts off the heating power, closes the feed valve, and triggers an audible and visual alarm; At the same time, fault codes are displayed through HMI (Human Machine Interface) to guide operators in quickly locating the problem.
3、 System integration and optimization
PLC connects temperature sensors, pressure transmitters, flow meters and other devices through fieldbus (such as Profibus, EtherCAT) to achieve high-speed data acquisition and transmission. Combined with the upper computer software, trend analysis can be conducted on historical data to optimize control parameters. For example, by analyzing distillation data from different batches and adjusting PID parameters, the system response time can be shortened by 30% and energy consumption can be reduced by 8%.
The control algorithm and logic design of PLC in stainless steel molecular distillation system need to balance accuracy, stability, and safety. By using algorithms such as PID and fuzzy control to achieve precise control of core parameters, combined with logic such as state machines and safety interlocks to ensure process continuity, an efficient and reliable automation control system is ultimately constructed.