Pavement dynamic monitoring data processing based on wavelet decomposition and reconfiguration methods

Early damage to asphalt pavements generally occurs due to the increasing traffic flow and the loads of vehicles, coupled with alternating high- and low-temperature cycles, freeze–thaw cycles, ultraviolet radiation, and other harsh environments. Several types of distress, such as rutting, cracking, and other damage, deteriorate the serviceability of asphalt pavements and shorten the road service life. Thus, the long-term structural mechanical response of asphalt pavements under the influence of loads and the environment is crucial data for the road sector, which provides guidance about road maintenance. Effectively processing the pavement dynamic monitoring data is a prerequisite to obtain the dynamic response of asphalt pavement structures. However, the dynamic monitoring data of pavements are often characterized by transient weak signals with strong noises, making it challenging to extract their essential characteristics. In this study, wavelet decomposition and reconstruction methods were applied to reduce the noise of pavement dynamic response data. The parameters of the signal-to-noise ratio (SNR) and root mean square error (RMSE) were introduced to compare and analyze the effect of the decomposition of two different wavelet functions: the symlet (sym) wavelet function and the Daubechies (db) wavelet function. The results showed that both the sym and db wavelet functions can effectively obtain the average similarity information and the detailed information of the dynamic response signals of the pavement, the SNR after the sym wavelet fixed-threshold denoising process is relatively higher, and the RMSE is smaller than that of the db wavelet. Thus, wavelet transformation exhibits good localization properties in both the time and frequency domains for processing pavement dynamic monitoring data, making it a suitable approach for handling massive pavement dynamic monitoring data

Influence of Inorganic Additives on Pyrolysis of Pine Bark

This paper investigated the effect of inorganic additives on pine bark pyrolysis using a thermogravimetry instrument. Both thermogravimetry (TG) and differential thermogravimetry (DTG) were performed to a final temperature of 600 degrees C with heating rates of 10, 20, and 50 degrees C/min, respectively, and a nitrogen flow rate of 50 mL/min. Six types of inorganic additives at different loading were tested. The pyrolysis kinetics data of the samples were fitted to the Coats-Redfern model. The results showed that the pyrolysis behavior and kinetics are significantly altered by the additives and are a strong function of the characteristics and concentrations of the additives

Oxidative torrefaction of biomass residues and densification of torrefied sawdust to pellets

Oxidative torrefaction of sawdust with a carrier gas containing 3-6% O-2 was investigated in a TG and a fluidized bed reactor, with the properties of the torrefied sawdust and pellets compared with traditional torrefaction without any O-2, as well as the dry raw material. It is found that the oxidative torrefaction process produced torrefied sawdust and pellets of similar properties as normally torrefied sawdust and corresponding pellets, especially on the density, energy consumption for pelletization, higher heating value and energy yield. For moisture absorption and hardness of the torrefied pellets, the oxidative torrefaction process showed slightly poor but negligible performance. Therefore, it is feasible to use oxygen laden combustion flue gases as the carrier gas for torrefaction of biomass. Besides, torrefied sawdust can be made into dense and strong pellets of high hydrophobicity at a higher die temperature than normally used in the production of traditional control pellets. (C) 2012 Elsevier Ltd. All rights reserved