Furnace Temperature Control for MSWI Process Based on Neural Network Predictive Controller - 文章详情页

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To　tackle　the　challenge　of　controlling　furnace　temperature　in　municipal　solid　waste　incineration　(MSWI),　which　is　critical　for　incineration　efficiency　and　pollutant　reduction,　this　paper　introduces　a　model　predictive　control　(MPC)　strategy　utilizing　a　back　propagation　neural　network　(BPNN).　Traditional　PID　struggles　with　the　nonlinearity　and　disturbances　of　process.　Our　approach　involves　constructing　a　BPNN-based　prediction　model　to　forecast　future　furnace　temperatures.　Using　gradient　descent,　we　optimize　the　objective　function　within　a　predetermined　period,　allowing　for　real-time　adjustment　of　control　variables.　This　effectiveness　of　method　is　validated　through　experiments　with　data　from　a　Beijing　MSWI　plant,　demonstrating　enhanced　tracking　and　disturbance　management　capabilities.　©　2024　IEEE.

关键词：

Municipal solid waste Predictive control systems Gradient methods Backpropagation Neural networks Model predictive control Torsional stress Waste incineration Temperature

作者机构：

[ 1 ] [Wang, Bokang]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China
[ 2 ] [Tang, Jian]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China
[ 3 ] [Xia, Heng]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China
[ 4 ] [Tian, Hao]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China
[ 5 ] [Wang, Tianzheng]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China
[ 6 ] [Wu, Zhiwei]Northeastern University, State Key Laboratory Of Synthetical Automation For Process Industries, Shenyang, China

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