TG–FTIR combined technology was used to study the degradation process and gas phase products of epoxy glass fiber reinforced plastic (glass fiber reinforced plastic) under the atmospheres of high purity nitrogen. The pyrolysis characteristics of epoxy glass fiber reinforced plastic were measured under different heating rates (5, 10, 15, 20 °C min−1) from 25 to 1000 °C. The thermogravimetric analyzer (TG) and differential thermogravimetric analyzer (DTG) curves show that the initial temperature, terminal temperature, and temperature of maximum weight loss rate in the pyrolysis reaction phase all move towards high temperature, as the heating rate increases. Epoxy glass fiber reinforced plastic has two stages of thermal weightlessness. The temperature range of the first stage of weight loss is 290–460 °C. The second stage is 460–1000 °C. The above two weight loss stages are caused by pyrolysis of the epoxy resin matrix, and the glass fiber will not decompose. The dynamic parameters of glass fiber reinforced plastic were obtained through the Kissinger-Akahira-Sunose (KAS), Flynn–Wall-Ozawa (FWO) and advanced Vyazovkin methods in model-free and the Coats–Redfern (CR) method in model fitting. FTIR spectrum result shows that the main components of the product gas are CO2, H2O, carbonyl components, and aromatic components during its pyrolysis.

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It is necessary to grasp the moisture content of wheat in the process of hot air drying in real time to save energy and improve quality. According to the result of the hot air drying experiment, a backpropagation (BP) neural network prediction model with a topology of 2-4-1 is established. The mean absolute error (MAE) and degree of fit (R 2 ) of the predicted results are 0.216% and 0.9804. The BP neural network model is optimized with a genetic algorithm (GA-BP) to improve accuracy. The MAE and R 2 are down to 0.069% and 0.9945. The generalization ability of the GA-BP prediction model is verified, when predicting the samples out of the training set, the MAE is 0.37% and R 2 is 0.9993. It shows that the GA-BP prediction model is of good generalization ability. This study provides a new conception for the real-time control of moisture content in hot air drying wheat. Novelty impact statement:The prediction model of wheat moisture content in the hot air drying process was established based on BP neural network algorithm and optimized by the genetic algorithm. The wheat moisture content can be accurately predicted at different hot air temperatures and drying times, which provides a new method for the real-time monitoring of moisture content in the drying process.

In order to improve recycling quality of asphalt mixtures based on microwave heating, thermoelectric coupling theory is researched. Heat conduction equation is built according to Fourier's Law. Series model, parallel model and Maxwell model are studied to solve thermal conductivity of asphalt mixtures. Radiation field model of pyramidal horn antenna is set up and mean value of electric field intensity is measured by experiment. Thermoelectric coupling model is built on the basis of dielectric property of asphalt mixtures. Numerical simulation is performed for the model. It is found that influence of asphalt-aggregate ratio on temperature field is not distinct. Average temperature of asphalt mixtures is related to microwave power and increases with microwave power linearly. The asphalt mixtures using diorite as aggregate have a strongest adsorption capability to energy. The result can simulate hot in-place regeneration process, and has important significance to improve regeneration quality of asphalt mixtures.