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In order to investigate the effects of different predrilled-hole parameters on the mechanical behavior of micro-piles, Pseudo-static tests on five predrilled-hole micro-piles were carried out under unidirectional cyclic displacement load. The displacement-load curves and distribution of bending moment and displacement are analyzed. The test results indicated that the smaller the stiffness of predrilled-hole filler is, the better the deformability of specimen is. The stiffness of predrilled-hole filler has significant influence on the displacement and bending moment of pile body. Compared with the shallow predrilled-hole specimen, the deep predrilled-hole specimen has lower bearing capacity, but its deformability and bending moment are larger. The larger the diameter of predrilled-hole is, the larger the bending moment of specimen is. The larger diameter of predrilled-hole has significant effect on displacement of specimen. The deformability of predrilled-hole specimen is improved when the depth and diameter of predrilled-hole exceed 3 times of pile diameter.

This paper presented a comprehensive approach of on-line monitoring and optimization for the soot-blowing system in coal-fired power plant boilers. Due to ash fouling monitoring of heat transfer surfaces of the boiler based on dynamic mass and energy balance method, on-line calculation of boiler thermal efficiency and soft measurements for gas flow rate at the double breezing tail and steam flow rate of reheaters, the performance of boiler's soot-blowing system can be evaluated on-line. And then, the suitable soot-blowing strategies are developed for soot-blowing frequencies and duration optimization. Monitoring results and analysis of an experimental application are given to illustrate the proposed methodologies. The methods proposed in this paper can be applied to state assessment and guidance of soot-blowing operation of boiler's soot-blowing system in order to achieve the purpose of energy conservation, without demanding special instruments or complex computing systems.

This paper investigates factors that influences the unburned carbon content in fly ash and selects the optical factors from the original characteristics by means of minimal-redundancy-maximal-relevance criterion (mRMR). And on this basis, this paper proposes a novel model called clustering least squares support vector machine (CLS-SVM) to predict the unburned carbon content in fly ash. In this CLS-SVM model, a fuzzy c-means cluster algorithm (FCM) is adopted to decompose the original data into three different sub data sets. Taking advantage of both theory of clustering algorithm and advanced statistical learning methodology, CLS-SVM models are built specifically for each different sub data sets. Then the CLS-SVM models are developed to predict the key parameter — unburned carbon content, which is verified through operation data of a 300MW generating unit.

Furnace exit gas temperature (FEGT) is the key parameter in the furnace ash fouling monitoring system. Since the standard least squares support vector machine (LSSVM) is not suitable for online identification and control of FEGT, a novel CM-LSSVM-PLS method is proposed to predict FEGT in this paper. In the process of CM-LSSVM-PLS method, c-means cluster (CM) algorithm is used to partition the training data into several different subsets by considering the characteristics of operational data. Submodels are subsequently developed in the individual subsets based on LSSVM method. Partial least squares algorithm (PLS) is employed as the combination strategy. The online updating algorithm is then applied to the CM-LSSVM-PLS model. The proposed online model is verified through operation data of a 300MW generating unit. The simulation results show that the proposed online updating model is effective for online FEGT forecasting.