Most halogenated organic compounds (HOCs) are toxic and persistent, and their efficient destruction is currently a challenge. Here, we proposed a sulfite/UV (253.7 nm) process to eliminate HOCs. Monochloroacetic acid (MCAA) was selected as the target compound and was degraded rapidly in the sulfite/UV process. The degradation kinetics were accelerated proportionally to the increased sulfite concentration, while the significant enhancement by increasing pH only occurred in a pH range of 6.0-8.7. The degradation proceeded via a reductive dechlorination mechanism induced by hydrated electron (e(aq)(-)), and complete dechlorination was readily achieved with almost all the chlorine atoms in MCAA released as chloride ions. Mass balance (C and Cl) studies showed that acetate, succinate, sulfoacetate, and chloride ions were the major products, and a degradation pathway was proposed. The dual roles of pH were not only to regulate the S(IV) species distribution but also to control the interconversion between e(aq)(-) and H(•). Effective quantum efficiency (Φ) for the formation of e(aq)(-) in the process was determined to be 0.116 ± 0.002 mol/einstein. The present study may provide a promising alternative for complete dehalogenation of most HOCs and reductive detoxification of numerous toxicants.

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Machinery fault diagnosis is pretty vital in modern manufacturing industry since an early detection can avoid some dangerous situations. Among various diagnosis methods, data-driven approaches are gaining popularity with the widespread development of data analysis techniques. In this research, an effective deep learning method known as stacked autoencoders (SAEs) is proposed to solve gearbox fault diagnosis. The proposed method can directly extract salient features from frequency-domain signals and eliminate the exhausted use of handcrafted features. Furthermore, to reduce the overfitting problem in training process and improve the performance for small training set, dropout technique and ReLU activation function are introduced into SAEs. Two gearbox datasets are employed to conform the effectiveness of the proposed method; the result indicates that the proposed method can not only achieve significant improvement but also is superior to the raw SAEs and some other traditional methods.

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