Research On Optimization Of Design Scheme Of Road Electric Heating Method For Deicing Or Snow Melting

In recent years,China’s transportation has developed rapidly,the level of motorization has been significantly improved,and the number of cars in large and small cities across the country has surged.This has brought rapid GDP growth and also brought challenges,such as heavy snowfall in winter in the north,frequent traffic accidents,and road sections accidents caused by snow increase year by year.This paper analyzes the various factors that affect the effect of snow melting on cement concrete pavement with carbon fiber heating cable,and on this basis,the prediction function of BP neural network is used to solve the design problem of road snow melting heating scheme in practical engineering applications and gives detailed steps.The research background of this paper is the cement concrete road in the snowfall area in winter.The main results of the research include the following aspects:(1)Based on the basic theory of heat transfer,a heat transfer model is established to analyze and derive the calculation formula for the heat consumption required for snow melting or deicing in this experiment,and analyze the influence mechanism of the heat transfer process of various factors.(2)By conducting independent orthogonal tests and using mathematical methods to reduce test data errors,it provides reliable and accurate data support for predicting snow melting time and power consumption under specific engineering conditions.Control wind speed and electric power separately as a single influencing factor,and quantitatively analyze the influence of a single factor on the temperature rise trend.The comparison test with and without preheating and with and without insulation layer was used to analyze its effect on the final snow melting and ice melting effect.(3)Establish a BP neural network to predict the snow melting time and warm-up time,and compare the prediction results with the experimental results to demonstrate the accuracy of the BP neural network prediction.The average relative error of the snow melting prediction test is 2.97%and the average relative error of the preheat prediction test It is 4.85%,and the prediction accuracy is high.In this paper,the MIV algorithm is also used to evaluate the single factor influence ability,and the result of the influence degree ranking is:electric power>buried depth>snow depth>temperature>wind speed>line spacing.In this paper,the optimized design of high-efficiency and energy-saving heating scheme can be obtained in the specific project area.Taking Wuhan as an example,the design of 240W/m~2 of electric power can achieve the purpose of efficiently melting snow and melting ice and saving electrical energy.It is predicted that the pavement snow melting and ice melting system will have a warm-up time of 1.18 hours.