Abstract
At present, the recovery of low-quality waste heat is a major problem in energy utilization. To solve this problem and improve energy efficiency, this research group designed a low-quality waste heat power generation device with a roots-type power machine as the core component. However, the power generation device produces a large hysteresis in power generation regulation. While the hardware can be improved, the design of the measurement and control system is also critical. In view of the problems existing in low-quality waste heat power generation devices, this research group introduced an internal model controller into the control system and designed an internal model controller and filter through the analysis of each module. In addition, to improve the performance of the controller, this research group applied the deep learning method to optimize the control system and used the prediction function of the deep learning method to further improve the stability of the device. The simulation and experimental results show that the control strategy can make this device for the recovery of low-quality waste heat respond quickly to fluctuations in the gas source and improve the hysteresis problem.
Highlights
IntroductionA. RESEARCH BACKGROUND With rapid economic and social development, energy consumption has increased rapidly, and method for its production and utilization have been continuously improved
This paper proposes an internal model control strategy based on deep learning prediction, which greatly improves the tracking and antiinterference performance of the waste heat recovery system
Based on deep learning methods, combined with many time series historical data monitored by sensors and comprehensively considering the influence of various factors on the change of the roots-type power engine speed, the research group established an Long- short-term memory (LSTM) prediction model to predict the rootstype power machine speed
Summary
A. RESEARCH BACKGROUND With rapid economic and social development, energy consumption has increased rapidly, and method for its production and utilization have been continuously improved. The impact of energy production and consumption methods on the environment has become increasingly prominent. Energy and environmental issues have attracted increasing attention. In the current industrial production process, a large amount of industrial waste heat energy is dissipated into the environment in the form of gas, resulting in a huge waste of resources. If these thermal energy resources can be recycled, they can play a positive role in solving the current energy shortage problem
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