Optimization of wet deacidification control of waste incineration flue gas based on LSTM prediction feedback
Absorbent flow control is key part in wet deacidification system control.A predictive method on the control of absorption liquid flow based on LSTM neural network algorithm was proposed to suppress the unstableness in flow control caused by long pH feedback delay in existing control systems.The concentrations of SO2 at the inlet and outlet of deacidification system were used as feedforward signal for absorption liquid flow control.By modeling and analysis of actual operation data,the error between predicted flow rate of absorption liquid and actual value under stable load change and the concentration of sulfur dioxide are within 4.5%～6.3%,and the predicted flow of absorption liquid is close to original flow,which meets the demand of stable discharge of outlet SO2 gas concentration.Under unstable load conditions,the absorption liquid flow rate increased by an average of 1.7%compared with original flow control method,the fluctuation range of outlet sulfur dioxide concentration value decreased by 23.4%,the total sulfur dioxide emission decreased by 8.77%and the stability and efficiency of desulfurization were significantly improved.
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