Research On Algorithm Of Data Fusion Technology In Temperature Monitoring Of Multi-Stage Oxidation Tank

The process of extracting gold by biological oxidation is to use bacteria to oxidize refractory gold ores with high sulfur and arsenic to decompose the inclusions on the surface of gold to achieve gold extraction.Therefore,in the actual production process of the gold extraction process,ensuring the activity of bacteria to the maximum will directly affect the efficiency of gold extraction.It is crucial to keep the oxidation tank's pulp temperature at the optimum strain breeding temperature.First of all,the first consideration is how to reasonably and effectively monitor the oxidation tank's internal temperature state and achieve a better global monitoring effect.This research project's completion is of great significance to the subsequent establishment of the oxidation tank temperature control decision-making system to ensure gold mineral energy.However,gold deposits are generally located in remote mountain areas at high altitudes.The temperature difference between day and night in northern areas is usually prominent in winter,coupled with wind and snow,which makes the temperature field inside the oxidation tank in an alternating state.Most biological oxidation gold extraction plants still use traditional temperature measurement methods with certain limitations and operational defects,and the oxidation tank involved in the pretreatment process is generally significant.Manual hand-held sensors or fixed-point sensors can only measure pulp temperature in a small range without considering the extreme external environment's influence.Therefore,the universal temperature estimation technique could not actually reflect the genuine temperature change in the oxidation tank.Concerning illustration An multi-field provision method,sensor information combination innovation could successfully take care of mash temperature estimation in the tank.Therefore,given the problems existing in the temperature monitoring of the oxidation tank in a biological oxidation gold extraction plant in a high altitude and cold area of Xinjiang,a temperature monitoring scheme based on sensor data fusion technology has been proposed in this paper.First of all,the principle of heat transfer in the oxidation tank is analyzed,and according to the spatial location of each oxidation tank and extreme environmental interference factors,a cluster hierarchical multi-connection sensor network fusion framework is designed.The network structure is divided into three layers,and the bottom sensor collects the pulp temperature data,which is spirally distributed in the inner wall of the oxidation tank.Each cluster head node sensor acts as the middle layer to complete the local measurement fusion estimation.Those combination focal point will be taken concerning illustration the upper layer to understand those worldwide state combination estimation.Secondly,in the information transforming and only those lowest part temperature sensor,the universal unscented Kalman channel(UKF)calculation will be improved,and the blurring memory grid may be included with enhance the capacity to distinguish nonlinear demonstrating errors.Furthermore,the sequential measurement fusion estimator(SOFE)algorithm is embedded in the measurement update to improve local measurement fusion performance.Finally,in the global state fusion estimation,the sequential analysis and inverse covariance intersection-global state fusion estimation(SICI-GSFE)algorithm are proposed by combining sequential analysis with inverse covariance intersection.Through those computation Furthermore simulation,the Outcomes indicate that it can diminish those outside interference,successfully incorporate every last one of temperature state estimation,what's more enhance the precision of the global temperature state estimation.