Research on the application of deep learning algorithms in robot intelligent laboratory system
CHEN Liang
LI ning
ZHANG Lan
ZHANG Lei
Abstract：Aiming at the problems of low efficiency,excessive manual intervention,insufficient data reliability and lack of equipment trend early warning in traditional coal quality testing,this paper designs and studies a robot intelli-gent testing system integrating deep learning technology to fill the"last mile"of fuel control in coal-fired power plants.This system can achieve fully automatic detection of core coal quality indicators such as calorific value,total sulfur,internal moisture,ash content,volatile matter and carbon,hydrogen and nitrogen elements.Through physical isola-tion of"normal temperature zone-high-temperature zone",independent robot control and a three-level standard coal verification mechanism of"before test-during test-after test",it ensures the detection accuracy and data authentici-ty.At the same time,it realizes the real-time transmission of detection data without touching the ground,avoiding the risk of corruption.To break through the bottlenecks of fixed standard coal sample insertion and manual trend judg-ment in the traditional system,deep learning technology was innovatively introduced:an intelligent standard coal sample insertion model was constructed based on LSTM(Long Short Term Memory network),integrating historical e-quipment data,real-time operating parameters and environmental information to achieve"on-demand sample inser-tion".The trend early warning module is designed based on the CNN-LSTM(Convolutional Neural Network-Long Short Term Memory)fusion model to capture the long-term degradation trend of device performance.The experimental results show that,compared with the traditional fixed-period sample insertion,the average daily consumption of standard coal in the intelligent sample insertion mode is reduced by 28.7%,and the equipment drift error rate drops from 8.3%to 1.7%.The trend early warning module can provide early warnings for indicators such as total sulfur and calorific value by 6 to 8 days,with an accuracy rate exceeding 88%.After deep learning correction,the detection deviations of total sulfur and calorific value were reduced to±0.018%and±0.03 MJ/kg respectively.This system has achieved a leap from"automation"to"intelligence"in coal quality detection,providing reliable technical sup-port for the efficient control of fuel in coal-fired power plants.
Keywords：intelligent robotic laboratory systemcoal quality detectiondeep learningCNN-LSTM fusion modelintelligent standard coal samplingequipment trend early warning
Publication Date：2026-01-31
Pages：10( 81-90 )