LDPE Autoclave Reactor Forecasting for Runaway Anomaly Detection

The patent replaces traditional mechanical monitoring systems with human operators with an automated deep learning-based anomaly detection system. The system uses LSTM networks and autoencoders to process sensor data from the autoclave reactor, substituting human monitoring with intelligent algorithms that can detect anomalies more reliably without requiring additional physical equipment in the reactor itself.

The patent creates a virtual model of the reactor system through deep learning algorithms that replicate normal operational patterns. The autoencoder learns to copy and reconstruct typical sensor data sequences, and deviations from this learned pattern indicate anomalies. This virtual modeling approach enables reliable anomaly detection using only existing sensor data without adding physical monitoring devices.

The patent implements a self-service approach where the deep learning system uses only the sensor data already available from the existing reactor instrumentation. The LSTM and autoencoder models are trained on historical sensor data from pressure, temperature, and other process variables that are already being collected by the reactor's standard monitoring system. No additional sensors or physical equipment are required - the system serves itself by extracting intelligence from existing data streams.

The deep learning system performs multiple functions using a single integrated model architecture. The same LSTM-autoencoder framework simultaneously handles anomaly detection, future value estimation, and pattern recognition across multiple sensor variables. This multi-functional approach reduces overall system complexity compared to implementing separate specialized systems for each function.

The patent performs preliminary action by pre-training the LSTM and autoencoder models offline using historical sensor data from the autoclave reactor. During the offline training phase, the system learns temporal patterns and relationships in the data, optimizing the model weights and architecture for accurate future value prediction. Once trained, the model can rapidly process real-time sensor data with minimal computational delay, as the heavy lifting of pattern recognition has already been accomplished during training.

The system dynamically adapts its processing based on operational conditions. The LSTM network adjusts its internal state representations to capture changing temporal patterns in the reactor data. The autoencoder dynamically reconstructs sensor sequences with varying levels of detail depending on the input characteristics, optimizing processing speed while maintaining prediction accuracy for different operational scenarios.