Prediction of corrosion rate in gas storage injection-production string based on IAOA-KELM
The underground corrosion environment of gas storage is complicated.To prevent corrosion failure of pipe string,this study established a model based on the combination of Archimedes Optimization Algorithm(AOA)and Nuclear Extreme Learning Machine(KELM)to predict the corrosion rate of gas storage injection and production pipe string.Firstly,this study constructed an Improved Archimedes Optimization Algorithm(IAOA).In AOA,individuals are initialized with random position information,and the good-point set is applied to the initial stage of AOA to enhance the global exploration capability of the algorithm.In addition,by improving the density reduction factor and integrating the golden sine algorithm with the local search phase position update,the algorithm can avoid falling into the local optimal.After analysis,the time complexity of IAOA is the same as that of AOA,indicating that the improvement strategy does not reduce the operation efficiency.Secondly,this study used IAOA to optimize the KELM regularization coefficient(C)and kernel function parameter(γ)and then established the IAOA-KELM corrosion rate prediction model.Finally,IAOA-KELM was used to learn and predict 150 sets of corrosion data in a gas storage injection and production pipe string with MATLAB software.One hundred thirty sets of data were randomly selected as training sets for the model to learn,and the remaining 20 groups were tested on the model training results.The error comparison between the prediction results of the IAOA-KELM model and those of KELM,Particle Swarm Optimization(PSO)-KELM,and AOA-KELM was carried out.The results show that the ERMSE,EMAE,and R2 of the IAOA-KELM model are 0.65％,0.39％,and 99.83％,indicating that the prediction accuracy is better than other models.It is proved that the IAOA-KELM model can predict the corrosion rate more accurately and provide a reference for the operation and maintenance of injection-production pipe string and the health management of gas storage.
safety engineeringunderground gas storageinjection and production stringKernel Extreme Learning Machine(KELM)Improved Archimedes Optimization Algorithm(IAOA)corrosion rate
NETL
NSTL
万方数据
维普
