Design of Multi-Modal Intelligent Optimization Control System based on Control Algorithm

Background: Due to the lag in sensor detection, the traditional intelligent optimization control system faces problems such as dynamic time lag, slow response speed and low precision in actual operation. Methods: This paper studies the control system by using control algorithms. This paper designs a multimodal perception module, which integrates multiple sensors and can capture core data such as flue gas flow and slurry pH value in real time, and fuses the data through weighted average method. This paper uses fuzzy PID (Fuzzy Proportional-Integral-Derivative, FPID) control strategy to dynamically adjust PID parameters to achieve precise control of the desulfurization process. Further enhance the adaptability and optimization ability of the system, this paper combines ant colony optimization (ACO) with FPID to construct an ACO-FPID model. ACO automatically optimizes the parameters of FPID with its powerful global search capability to ensure that the system can maintain high stability and accuracy under various operating environments. Results: The mean error rate, desulfurization efficiency and average response time of ACO-FPID when regulating the pH parameters of slurry are 2.16%, 83.33% and 11 seconds, respectively. Concluding Remarks: The multi-modal intelligent optimization control system designed in this paper successfully overcomes the shortcomings of the traditional desulfurization control system and significantly improves the desulfurization efficiency and control accuracy.