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Design And Implementation Of Advanced Control System For Gasification Process Of HT-L Gasifier

Posted on:2024-05-23Degree:MasterType:Thesis
Country:ChinaCandidate:X N XieFull Text:PDF
GTID:2531306932460914Subject:Control Science and Engineering
Abstract/Summary:PDF Full Text Request
Coal is one of the largest reserves of fossil energy in China,the combustion efficiency of coal is low and pollution is high,so improving the coal utilization rate has become a hot topic in the current energy field.As one of the important technologies to improve coal utilization rate,the core of coal gasification technology is the gasifier.The gasification process is the core production process of the gasifier.The gasification process has the characteristics of large noise,strong disturbance,nonlinearity and large time lag.It is difficult to achieve satisfactory control results using traditional control strategies.The research work in this dissertation aims to improve the control quality of the gasification process,increase the production efficiency of the gasifier,and achieve energy saving and consumption reduction.The research work in this dissertation takes the HT-L gasifier as the research subject,designs and implements the advanced control system for the gasification process of the HT-L gasifier.This dissertation mainly accomplishes the following research work.1.According to the process and equipment characteristics of the HT-L gasifier,the control requirements of the gasifier are analyzed,and the overall gasifier control scheme consisting of advanced control loop level and control process parameter optimization level is designed according to the control requirements.2.At the gasifier advanced control loop level,design and implementation of the oxygen-coal ratio control loop,the oxygen flow control loop and the effective gas flow control loop are completed respectively.In the oxygen-coal ratio control loop,an Adaptive Kalman Filter feedforward PID controller is designed to overcome the large noise and strong disturbance characteristics of the oxygen-coal ratio object,and the automatic control of oxygen-coal ratio is realized.In the oxygen flow control loop,the improved adaptive dead-zone inverse control is proposed based on the adaptive dead-zone inverse control,an improved adaptive dead-zone inverse feedforward controller is designed to overcome the dead-zone nonlinear and strong disturbance characteristics of the oxygen flow object,and the automatic control of oxygen flow is realized.In the effective gas flow control loop,an effective gas flow controller based on Generalized Predictive Control is designed to overcome the large time lag characteristics of the effective gas flow object,and the automatic control of the effective gas flow is realized.3.At the gasifier control process parameter optimization level,a Radom Forest based variable selection algorithm is proposed to select the variables related to the gasifier production rate,and the Gated Recurrent Unit neural network is used to establish the gasifier production rate model.The improved Particle Swarm Optimization algorithm based on Particle Swarm Optimization is proposed,and the real-time solution of the optimal oxygen-coal ratio setpoint is completed.The gasifier production rate is improved and energy saving is achieved.4.According to the production site software/hardware conditions,the implementation and application of the HT-L gasifier advanced control system are completed.After the gasifier advanced control system was put into operation,the control quality of the gasifier is improved significantly.Compared with manual control,the fluctuation of oxygen-coal ratio is reduced by 26.0%,the fluctuation of oxygen flow is reduced by 28.7%and the fluctuation of effective gas flow is reduced by 16.4%.The gasifier production rate is improved significantly,the energy saving effect is remarkable,and the overall coal consumption is reduced by about 3%.
Keywords/Search Tags:HT-L Gasifier, Advanced Process Control, Adaptive Kalman Filter, Improved Adaptive Dead-zone Inverse Control, Generalized Predictive Control, Improved Particle Swarm Optimization
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