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In order to meet the requirements for miniaturization detection of oil shale pyrolysis process and solve the problem of low sensitivity of oil and gas detection devices, a small bionic electronic nose system was designed. Inspired by the working mode of the olfactory receptors in the mouse nasal cavity, the bionic spatial arrangement strategy of the sensor array in the electronic nose chamber was proposed and realized for the first time, the sensor array was used to simulate the distribution of mouse olfactory cells. Using 3D printing technology, a solid model of the electronic nose chamber was manufactured and a comparative test of oil shale pyrolysis gas detection was carried out. The results showed that the proposed spatial arrangement strategy of sensor array inside electronic nose chamber can realize the miniaturization of the electronic nose system, strengthen the detection sensitivity and weaken the mutual interference error. Moreover, it can enhance the recognition rate of the bionic spatial strategy layout, which is higher than the planar layout and spatial comparison layout. This bionic spatial strategy layout combining naive bayes algorithm achieves the highest recognition rate, which is 94.4%. Results obtained from the Computational Fluid Dynamics (CFD) analysis also indicate that the bionic spatial strategy layout can improve the responses of sensors.

In this study, the non-isothermal pyrolysis method was used to investigate the pyrolysis characteristics of oil shale from four areas: namely Nongan, Fuyu, Mongolia, and North Korea, with special emphasis on Fuyu oil shale. X-ray diffraction was performed to determine the mineral composition of the oil shale. The pyrolysis behaviours of the oil shale at different heating rates were determined by thermogravimetric analysis and differential thermogravimetric analysis. During pyrolysis, as the heating rate increased, the oil shale reaction zone moved to a higher temperature due to thermal hysteresis. The activation energies of the oil shale samples from four areas were obtained by the Flynn–Wall–Ozawa (FWO), Starink, and Friedman methods. The results showed that the activation energy was not stable throughout the conversion stage, and the overall trend showed an increase with an increase in temperature. The average activation energy in the second stage was 304, 307, and 342 kJ mol−1 for Fuyu oil shale; 328, 333, and 348 kJ mol−1 for Nongan oil shale; 341, 347, and 422 kJ mol−1 for North Korea oil shale; and 362, 363, and 379 kJ mol−1 for Mongolia oil shale by the FWO, Starink, and Friedman methods, respectively. The fluctuation of activation energy showed that thermal degradation in oil shale was a complicated multistep reaction, regardless of the area. The quality and characteristics of organic matter and mineral impacted the pyrolysis process and kinetic characteristics. The Sestak–Berggren method was used to fit the data from oil shale pyrolysis. The results indicated that the main mass loss phase of oil shale pyrolysis was controlled by a nucleation mechanism.

Co-pyrolysis of oil shale from different regions with spent oil shale from subcritical water extraction experiments is experimentally conducted using thermogravimetric analysis (TG). The mixture samples (oil shale/spent oil shale in blending ratio of 1:1) are heated from 30 up to 850 °C with heating rate of 5, 10 and 30 °C min−1, nitrogen flow rate of 30 mL min−1. Three different stages are identified based on TG curves of the mixture samples. The second stage which is due to the release of volatile matter is the primary reaction stage, and the mass loss discrepancies of the experimental and calculated TG profiles in the second stage are considered as a measurement of the interactions extent during the co-pyrolysis. The experimental mass loss of Nong’an and Fushun oil shale/spent oil shale mixture samples is higher than the calculated data, and the experimental mass loss of Huadian and Mudanjiang oil shale/spent oil shale mixture samples is lower than the calculated data. It is concluded that the interaction effect obviously occurred during the co-pyrolysis of oil shale and spent oil shale. In addition, the effect of kaoline and montmorillonite on the cracking of the kerogen is discussed according to the co-pyrolysis of kerogen and inorganic mineral. The release index of the kerogen, kerogen/kaolinite and kerogen/montmorillonite is 2.06 × 107, 4.58 × 107 and 1.89 × 107 % K−3 min−1, respectively. And the kinetic parameters of the samples are obtained by Kissinger–Akahira–Sunose method based on the thermogravimetric data, and the apparent activation energy of the kerogen, kerogen/kaolinite and kerogen/montmorillonite is 58.4, 25.7 and 95.9 kJ mol−1, respectively. According to the release index and the kinetic parameters of the samples, we can conclude that the kaolinite is helpful for the pyrolysis of kerogen and the montmorillonite inhibits the pyrolysis of kerogen.

Multi-objective simultaneous prediction of waterborne coating properties was studied by the neural network combined with programming. The conditions of network with one input layer, three hidden layers and one output layer were confirmed. The monomers mass of BA, MMA, St and pigments mass of TiO2 and CaCO3 were used as input data. Four properties, which were hardness, adhesion, impact resistance and reflectivity, were used as network output. After discussing the hidden layer neurons, learn rate and the number of hidden layers, the best net parameters were confirmed. The results of experiment show that multi-hidden layers was advantageous to improve the accuracy of multi-objective simultaneous prediction. 36 kinds of coating formulations were used as the training subset and 9 acrylate waterborne coatings were used as testing subset in order to predict the performance. The forecast error of hardness was 8.02% and reflectivity was 0.16%. Both forecast accuracy of adhesion and impact resistance were 100%.