Dynamic Parameter Optimization Method for Carbon Capture and Conversion Process in Power Plants Based on Machine Learning : Parameter optimization for carbon capture and conversion process in power plants

The carbon capture and conversion system in power plants involves multiple subsystems, multiple reactors, and complex mass transfer and reaction processes. In order to reduce energy consumption and improve efficiency, a machine learning based dynamic parameter optimization method for the carbon capture and conversion process in power plants is proposed. Based on the carbon capture system process flow, machine learning technology is used to collect and prepare historical data related to the carbon capture and conversion process of power plants, and process and convert the data. Based on the prepared data and features, a machine learning method is used to establish a model and train it. The gradient descent method is selected to update the model parameters, achieving dynamic parameter optimization of the carbon capture and conversion process in power plants. The experimental results show that the proposed method can significantly reduce energy consumption, achieve low cost and high efficiency, and provide strong technical support for improving the automation and effectiveness of carbon capture and conversion processes in power plants.