A moderate temperature dry desulfurization process at 600-800 degrees C was studied in a pilot-scale circulating fluidized bed flue gas desulfurization (CFB-FGD) experimental facility. The desulfurization efficiency was investigated for various operating parameters, such as bed temperature, CO2 concentration, and solids concentration. In addition, structural improvements in key parts of the CFB-FGD system, i.e., the cyclone separator and the distributor, were made to improve the desulfurization efficiency and flow resistance. The experimental results show that the desulfurization efficiency increased rapidly with increasing temperature above 600 degrees C due to enhanced gas diffusion and the shift of the equilibrium for the carbonate reaction. The sorbent sulfated gradually after quick carbonation of the sorbent with a long particle residence time necessary to realize a high desulfurization ratio. A reduced solids concentration in the bed reduced the particle residence time and the desulfurization efficiency. A single-stage cyclone separator produced no improvement in the desulfurization efficiency compared with a two-stage cyclone separator. Compared with a wind cap distributor, a large hole distributor reduced the flow resistance which reduced the desulfurization efficiency due to the reduced bed pressure drop and worsened bed fluidization. The desulfurization efficiency can be improved by increasing the collection efficiency of fine particles to prolong their residence time and by improving the solids concentration distribution to increase the gas-solid contact surface area.

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A series of experiments in a circulating fluidized bed (CFB) pilot plant has explored a new dry desulfurization process, using the NO x in the flue gas and a new sorbent that has been prepared from fly ash and lime. Various desulfurization operating parameters were tested for the thermodynamic, chemical, and dynamic states for temperatures of 523−673 K. The NO x increased the calcium conversion ratio in the desulfurization process. In addition, with the NO x , the desulfurization byproduct was determined to be mostly CaSO4, instead of CaSO3, as a result of the chain reaction caused by the NO x . Therefore, the NO x in the flue gas can improve the efficiency of the dry desulfurization process.

Pedestrian dead reckoning (PDR) can be used for continuous position estimation when satellite or other radio signals are not available, and the accuracy of the stride length measurement is important. Current stride length estimation algorithms, including linear and nonlinear models, consider a few variable factors, and some rely on high precision and high cost equipment. This paper puts forward a stride length estimation algorithm based on a back propagation artificial neural network (BP-ANN), using a consumer-grade inertial measurement unit (IMU); it then discusses various factors in the algorithm. The experimental results indicate that the error of the proposed algorithm in estimating the stride length is approximately 2%, which is smaller than that of the frequency and nonlinear models. Compared with the latter two models, the proposed algorithm does not need to determine individual parameters in advance if the trained neural net is effective. It can, thus, be concluded that this algorithm shows superior performance in estimating pedestrian stride length.

MEMS (Micro Electro Mechanical System) gyroscopes have been widely applied to various fields, but MEMS gyroscope random drift has nonlinear and non-stationary characteristics. It has attracted much attention to model and compensate the random drift because it can improve the precision of inertial devices. This paper has proposed to use wavelet filtering to reduce noise in the original data of MEMS gyroscopes, then reconstruct the random drift data with PSR (phase space reconstruction), and establish the model for the reconstructed data by LSSVM (least squares support vector machine), of which the parameters were optimized using CPSO (chaotic particle swarm optimization). Comparing the effect of modeling the MEMS gyroscope random drift with BP-ANN (back propagation artificial neural network) and the proposed method, the results showed that the latter had a better prediction accuracy. Using the compensation of three groups of MEMS gyroscope random drift data, the standard deviation of three groups of experimental data dropped from 0.00354°/normals, 0.00412°/normals, and 0.00328°/normals to 0.00065°/normals, 0.00072°/normals and 0.00061°/normals, respectively, which demonstrated that the proposed method can reduce the influence of MEMS gyroscope random drift and verified the effectiveness of this method for modeling MEMS gyroscope random drift.