An autoclave is important equipment commonly applied in the field of hydrometallurgy,which has a risk of hazardous gas leaks.Additionally,this leak leads to the pressure unstable in the autoclave,potentially causing explosions that threaten both the equipment and production safety.To address this issue,a high frequency high-order spatial interaction recognition algorithm based on the sound of high-pressure autoclave leaks is proposed.This algorithm is used to monitor the sounds associated with autoclave leaks,thus timely detect leaks,and make a decision to ensure equipment and production safety.Firstly,the low-frequency noise interference of the recognition results is eliminated through a high-pass filter.Then,a recursive gated convolutional block is used to achieve the in-teraction of high-frequency components in high-order spatial dimensions.Finally,a fully convolutional layer is utilized to recognize the sound of autoclave leaks.Experimental results demonstrate that the proposed algorithm achieves good recognition results for the leak-age of the autoclave,with an average confidence level of 0.93.The recognition accuracy can reach up to 99.5％,with a confidence threshold of 0.65.During the processing speed,the algorithm can recognize 60 audio files per second,with each file lasting 5 sec-onds,meeting its real-time requirement.
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