Abstract
Biological oxidation pretreatment, which can improve the yield of gold, is the main gold extraction technology for disposing refractory gold ore with high arsenic and sulfur. The temperature of the oxidation tank influences the oxidation efficiency between the ore pulp and bacteria, including the yield of gold. Therefore, measurement has consistently been an important subject for researchers. As an effective data processing method, data fusion has been used extensively in many fields of industrial production. However, the interference of equipment or external factors such as the diurnal temperature difference or powerful wind may constantly increase measurement errors and damage certain sensors, which may transmit error data. These problems can be solved by following a pretreatment process. First, we establish a heat transfer mechanism model. Second, we design a small-range sensor network for the pretreatment process and present a layered fusion structure of sharing sensors using a multi-connected fusion structure. Third, we introduce the idea of iterative operation in data processing. In addition, we use prior data for predicting state values twice in order to improve the effectiveness of extended Kalman filtering in one time step. This study also proposes multi-fading factors on the basis of a weighted fading memory index to adjust the prediction error covariance. Finally, the state estimation accuracy of each sensor can be used as a weighting principle for the predictive confidence of each sensor by adding a weighting factor. In this study, the performance of the proposed method is verified by simulation and compared with the traditional single-sensor method. Actual industrial measurement data are processed by the proposed method for the equipment experiment. The performance index of the simulation and the experiment shows that the proposed method has a higher global accuracy than the traditional single-sensor method. Simulation results show that the accuracy of the proposed method has a 55% improvement upon that of the traditional single-sensor method, on average. In the equipment experiment, the accuracy of the industrial measurement improved by 37% when using the proposed method.
Highlights
Data fusion has been widely used in military and civilian applications
The process of biological oxidation pretreatment uses bacteria that oxidize the mineral wrapped with gold, and expose the gold outside
To improve the global accuracy of temperature detection, this study presents a distributed sensor fusion fusion algorithm for severe environments
Summary
Data fusion has been widely used in military and civilian applications. Biological oxidation pretreatment is a novel but difficult strategy of industrial gold production [1]. The process of biological oxidation pretreatment uses bacteria that oxidize the mineral wrapped with gold, and expose the gold outside. The control process has several important factors, such as temperature, oxygen quantity, pH value, and ORP (oxidation-reduction potential). To ensure the activity of bacteria, the temperature accuracy of the measurement and control processes becomes important.
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