Hexun Cao ¹ Weiming Liu ^2*

• ¹ Linyi Central Hospital, Yishui, China
• ² Linyi Polytechnic School, Linyi, China

In the modern rubber and plastic industry, temperature control becomes a key factor affecting product quality and production efficiency. However, the existing temperature control system generally has the problems of temperature lag and high energy consumption. Aiming at the poor control effect of the current temperature control system, the study introduces fuzzy neural network and improved particle swarm optimization algorithm. This method is combined with mechatronics technology to design and realize a set of efficient and accurate circulating temperature control system in rubber and plastic industry. The revised algorithm fared well in both circumstances, according to the testing data. Compared with the comparison algorithm, the improved algorithm was significantly optimized in terms of tuning time (Scenario 1: 0.513s, Scenario 2: 0.521s), overshooting amount (Scenario 1: 2.5%, Scenario 2: 2.3%), steady state error (Scenario 1: 0.005, Scenario 2: 0.004), and absolute integration error (Scenario 1: 0.008, Scenario 2: 0.007). Simulation analysis revealed that the proposed temperature control system had stronger anti-interference capability and lower energy consumption (0.6h energy consumption: 1086.5J, energy-saving rate: 38.7%). The outcomes revealed that the circulating temperature control system in rubber and plastic industry outperformed the comparison system in terms of temperature control performance, anti-interference ability and energy consumption. The study not only improves the accuracy and efficiency of the temperature control system in rubber and plastic industry, but also promotes the promotion of green and sustainable production methods. This is crucial for advancing the high-quality growth of the plastic and rubber industries.