Research On Optimization Of Coal Blending In Coal Chemical Gasifier Based On Data Driven

The coal gasification is an important part of achieving clean production in the coal chemical industry and a core technology for achieving alternative strategies for petroleum resources.The coal blending technology in gasification production is widely used.Under the premise of ensuring the quality of coal blending,the coal blending cost is minimized.Scientific and reasonable coal blending is one of the key issues for the current coal blending enterprises to improve their competitiveness.Ordos is an important coal-producing area in Inner Mongolia Autonomous Region and even the whole country.Coal is mainly composed of high-activity low-rank coal,which is very suitable for coal gasification production.However,the distribution of coal in Ordos is sparse,bringing coal and coal blending for coal chemical gasifier.Cost management issues.Therefore,in view of the above problems,this paper takes the cost of coal blending as the optimization goal,and the gas production of gasifier is the main constraint,and studies on the optimization of coal and coal blending of coal types in coal chemical gasifiers in Erdos area.First,based on the physical and chemical properties of raw coal,combined with expert experience,a proportional coal blending model was established.Combined with the requirements of gasifier for coal quality,the constraints and range of coal blending quality are determined by association rules and cluster analysis.A linear model between coal and coal quality is constructed with constraints.Through the genetic optimization algorithm,the coal blending strategy of the lowest cost is given while the gas production of the gasifier meets the constraints.Through the multi-coal blending experiment on some coals in Ordos,the results show that compared with the traditional coal blending method,the optimized coal blending cost has decreased by 1.75%.Secondly,the big data and neural network technology are combined to construct a nonlinear process model based on the data-driven gasification of coal gasification to the gas production.The coal quality of the coal blending model is predicted.Gas volume evaluation of coal blendingeffect.The nonlinear process model was applied to two gasifiers in Hangjin Yitai.The gas production predicted by the model satisfies the allowable error and the generalization ability is good.Finally,on the basis of the above work,the human-machine interaction optimization system based on gas production accounting is designed.The operator does not need to touch the algorithm and the program.By inputting the coal quality parameters and the coal blending requirements,a better coal blending ratio can be obtained.The gas production effect is combined with the proportional coal blending model to make a fine adjustment of the ratio,so that it can improve the gas production efficiency while satisfying the constraint conditions,and assist the operator to give the best coal blending scheme.Based on the research of coal blending optimization,this paper analyzes the current situation of coal blending in enterprises and completes the optimization modeling of coal blending.The experiment gives the coal blending strategy of optimizing coal blending.Through the prediction of gas production,the coal blending results are evaluated,and the coal blending strategy is optimized to achieve the expected results.