2results about How to "Improve system availability" patented technology

The invention provides a comprehensive intelligent control system for a submerged arc furnace. The comprehensive intelligent control system comprises an auxiliary distributed control subsystem, a Fur-DCS furnace body distributed control subsystem, an SIS control subsystem and a GDS control subsystem. When the submerged arc furnace is a semi-closed submerged arc furnace, the SIS control subsystem collects the stock bin level, the furnace bottom temperature, the furnace wall temperature and the flue gas temperature, and the SIS control subsystem controls a direct discharge scattered flue safety valve and an electric furnace transformer opening. When the submerged arc furnace is a direct-current submerged arc furnace or a closed submerged arc furnace, the SIS control subsystem collects the furnace pressure, the stock bin level, the H2 content and the O2 content, and the SIS control subsystem controls a crude gas diffusion flue safety valve and an electric furnace transformer opening. And the GDS control subsystem is used for completing leakage concentration detection, indication and alarm of combustible gas and toxic gas on site and starting and stopping of an exhaust fan of a chain workshop. The control subsystem is high in stability and reliability due to multi-aspect configuration redundancy; the overall performance of the smelting submerged arc furnace can be improved.

The invention discloses a methanol synthesis catalyst performance online prediction and health management method. The method comprises the following steps: S1, obtaining three groups of time sequence data of reactor inlet temperature, outlet gas CO2 concentration and reactor pressure; s2, the three groups of time sequence data are input into a pre-trained LSTM neural network model, an input layer of the LSTM neural network model comprises three nodes, a hidden layer of the LSTM neural network model comprises 128 nodes, an output layer of the LSTM neural network model comprises one node, and the LSTM neural network model is used for outputting a catalyst activity predicted value; and S3, when the predicted value of the catalyst activity is lower than 85.0%, triggering an early warning signal. According to the method, the catalyst performance prediction response delay and the false alarm rate are greatly reduced, the service life utilization rate of the catalyst is also improved, hardware modification is not needed, dependence on internal data of the catalyst is completely avoided, and high-benefit and zero-risk intelligent operation and maintenance are realized.