Chemical process optimization and fault diagnosis algorithm based on deep learning

With the continuous improvement of industrial production level, the demand for production process optimization is becoming increasingly urgent. To reduce economic losses and production stagnation caused by equipment failures, research on fault diagnosis technology is particularly important. The data-driven fault diagnosis technology, which can help operators detect and detect abnormal situations in a timely manner, has become one of the hotspots in the field of industrial and big data integration. This paper proposes a chemical process optimization and fault diagnosis algorithm based on deep learning (DL) to address the multi variable and strong temporal characteristics of chemical process data. This algorithm utilizes Convolutional Neural Networks (CNN) for DL and reconstruction of control and manipulation strategies, and combines Long Short Term Memory Networks (LSTM) for deep feature extraction of fault data, thereby achieving efficient fault classification. The results show that the algorithm can not only effectively optimize the chemical production process, but also achieve high precision in fault diagnosis. Through the multi-level feature extraction capability of DL, the algorithm can capture complex fault patterns that are difficult to recognize by traditional methods, providing strong support for the stable operation of chemical processes.