Siemens Energy has filed a patent for a method and system to model industrial processes, incorporating closed-loop feedback. The system uses a hybrid neural network model and first-principle dynamic model to predict outputs based on measured inputs. The model includes memoryless nonlinear blocks and a control block for controller parameters. GlobalData’s report on Siemens Energy gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Method and system for modelling industrial processes using neural networks
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The patent application (Publication Number: US20240036532A1) describes a computer-implemented method for modeling an industrial process, specifically a closed-loop process involving a controller. The method involves measuring at least one input for the industrial process at a given time and predicting the output at a subsequent time using a hybrid neural network model. This model incorporates both neural network and first-principle models, including ordinary differential equations to define the rate of change over time. The method also includes a control block to integrate controller parameters into the model, allowing for accurate predictions based on training input and output observations.
Furthermore, the patent application includes additional claims such as incorporating setpoints and disturbances into the modeling process, utilizing a Hammerstein-Wiener model structure for memoryless nonlinear blocks, and repeating the measuring and predicting steps for multiple iterations. The method is adaptable for various industrial processes, such as gravity separation processes for oil, water, and gas, or controlling robotic arms. Additionally, the application covers a non-transitory computer-readable medium with processor control code for implementing the method and a data processing system with sensors, a controller, and a processor to carry out the modeling process efficiently. Overall, the patent application outlines a comprehensive approach to modeling industrial processes using a hybrid neural network model, combining neural network and first-principle models for accurate predictions and control.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
