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Real-Time Weighted Data Fusion Algorithm for Temperature Detection Based on Small-Range Sensor Network.

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  • 1College of electrical Engineering, Xinjiang University, Urumqi 830047, China. 107551600890@stu.xju.edu.cn.

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|December 27, 2018
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Summary
This summary is machine-generated.

This study introduces a novel data fusion method to improve temperature measurement accuracy in biological oxidation pretreatment for gold extraction. The new approach enhances gold yield prediction by overcoming sensor errors, achieving significant accuracy improvements.

Keywords:
distributed sensor fusioniterative operationmulti-fading factorsmall-range sensor networkweighted fading memory index

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Area of Science:

  • Metallurgical Engineering
  • Sensor Networks
  • Data Fusion

Background:

  • Biological oxidation is crucial for extracting gold from refractory ores containing arsenic and sulfur.
  • Accurate temperature measurement in oxidation tanks is vital for optimizing oxidation efficiency and gold yield.
  • Existing measurement methods are susceptible to errors from equipment interference and environmental factors.

Purpose of the Study:

  • To develop an advanced data fusion method for accurate temperature monitoring in biological oxidation pretreatment.
  • To enhance the reliability of sensor data by mitigating interference and sensor damage.
  • To improve the overall accuracy and effectiveness of gold extraction processes.

Main Methods:

  • Established a heat transfer mechanism model.
  • Designed a sensor network with a layered fusion structure utilizing shared sensors and a multi-connected approach.
  • Implemented iterative data processing, enhanced extended Kalman filtering with prior data prediction, and introduced multi-fading factors for improved accuracy.

Main Results:

  • The proposed data fusion method demonstrated significantly higher global accuracy compared to traditional single-sensor methods in both simulations and industrial experiments.
  • Simulations showed an average accuracy improvement of 55% over the traditional method.
  • Industrial equipment experiments resulted in a 37% improvement in measurement accuracy.

Conclusions:

  • The developed data fusion technique effectively addresses sensor errors and improves temperature measurement accuracy in biological oxidation pretreatment.
  • This enhanced accuracy contributes to better process control and potentially higher gold yields.
  • The method offers a robust solution for reliable industrial monitoring in challenging environments.