Intelligent Optimization Of Current Efficiency In Aluminum Electrolysis Process Based On Data Drive

Energy has become a strategic issue in social and economic development.Current efficiency based on aluminum electrolysis process can't be real-time,comprehensive detection,etc.,resulting in process industry production process is difficult to digital,is difficult to establish accurate model,difficult to control the problem of optimal operation,put forward a kind of aluminum electrolytic current efficiency based on data-driven intelligent optimization strategy,aiming at different state of the electrolyzer,the intelligent optimization operation parameters is implemented,the cell control system and staff to provide a set of reliable decision variables,to achieve the purpose of improving current efficiency.The simulation software is used to simulate the aluminum electrolysis process and verify the effectiveness of the optimization model.This paper is mainly divided into the following parts:In order to obtain real-time and accurate current efficiency,a prediction model of aluminum electrolysis current efficiency was established based on hybrid whale simulated annealing algorithm(WOASA)optimized nuclear limit learning machine(KELM).In order to avoid the adverse effect of data redundancy on the prediction model,the principal component analysis method was used to reduce the dimension of the data to obtain the model input variables,and the density based algorithm was used to eliminate the abnormal points in the original data to ensure the data quality.When the electrolytic cell is in different electrolytic states,the real-time control strategy is different,the cell condition classification model is established,the cell condition is accurately judged,and the reference is provided for the optimization of current efficiency.Slot of aluminium electrolytic process is electrolytic temperature stability is the most important characteristic parameter,combined with the stability of the electrolytic cell indifferent electrolytic temperature area groove,groove conditions for temperature criterion for electrolyzer cell condition classification,through the analysis of aluminium electrolytic process,get the main operation parameters affecting the electrolytic temperature,based on probabilistic neural network(PNN)process of aluminium electrolytic cell of classification model.Based on current efficiency prediction model and slot of classification model,aluminum electrolytic process operation parameter optimization model is established,the technological conditions for production constraints,to aim at its highest state of current efficiency and cell optimal,a hybrid simulated annealing algorithm(WOASA)whale to find the optimal values of current efficiency and the solution temperature and the corresponding production technology parameters.In current efficiency is lower than the threshold is 92% of the cases,the cause of the low current efficiency of back analysis,find the cause of low current efficiency,targeted to adjust operating parameters set value,the current efficiency control within the scope of the target,make the average current efficiency of electrolyzer,intelligent decision-making,achieve the goal of energy saving.Finally,the method proposed in this paper is verified on MATLAB.The experiment shows that the current efficiency can be kept in a relatively high state by using the parameter adjustment mechanism,which can provide reference for the design of aluminum electrolysis control system.