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A rapid and sensitive cataluminescence-based gas sensor utilizing nanosized Y2MnO5 as the sensing materials for determining dimethyl ether in air was proposed. The luminescence characteristics and the optimal conditions were investigated in detail. The gas sensor showed high selectivity for dimethyl ether at 620 nm and satisfying activity at 210°C under the optimized conditions. The linear range of cataluminescence intensity versus concentration of dimethyl ether was 5~120 mg/m3, and the detection limit (3σ) was 3 mg/m3. No or weak interference was observed while the foreign substances, such as formaldehyde, ammonia, ethanol, benzene, carbon monoxide and sulfur dioxide, were passing through the sensor under selected conditions. The gas sensor displayed good stability for continuously introducing dimethyl ether over 100 h, and allowed real-time monitoring of dimethyl ether in air.

Based on Aspen Plus software platform, a simulation of ethanol synthesis from biomass-derived synthesis gas processed on the assumption of both physical and thermodynamic equilibrium. The influences of these conditions such as temperature, reactor pressure and the H2 to (CO+CO2) mole ratio in the feed gas on CO and CO2 conversion and ethanol yield were investigated. The results showed that reaction temperature, pressure and synthesis gas composition have the most important effect on ethanol synthesis behavior. In this process, low temperature and high pressure would be advantageous for ethanol production.

Applying neutral network-sliding model control design methods to large envelope flight control law of aircraft whose model parameter varies greatly with flight condition was studied in this paper. Neural network theory is used to approximately linearize the nonlinear system and cancel the errors brought with approximate inversion, and the residual error is solved by sliding model control. So it can approximate the nonlinear model accurately, and improve robustness and anti-jamming capability of the flight control system. Simulation results show the design neural network – sliding model large envelope flight controller has excellent control performance.

In decade years, several simple methods for the automatic tuning of PID controllers have been proposed. There have been different approaches to the problem of deriving a PID-like adaptive controller. All of these can be classified into two broad categories: model-based; or expert systems. In this paper a new expert adaptive controller is proposed in which the underlying control law is a PID structure. The design is based on the fuzzy logic and the generalized predictive control theory. The proposed controller can be applied to a large class of systems which is model uncertainty or strong non-linearity. Simulation results have also been illustrated. It shows that the proposed expert PID-like controller performed well than generally used PID.

The paper proposes a method to design AANN dynamic inversion controller through online ANN compensating inversion error. It mainly aims at evident shortage of dynamic inversion controller of UAV. A single hidden layer ANN structure is constructed and the stability of the whole closed loop system is proved. Also the stable adjustment arithmetic of online ANN weight is proposed. The robustness, the adaptability to fault and the response capability to actuator delay time of the scheme are verified by simulation. It is also proved that the online ANN has improved the performance of dynamic inversion controller well. It has important reference value for designing advanced flight control systems of UAV.