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Li N.,Hunan University | Li N.,Control iversity | Qiu K.,Hunan University | Qiu K.,Control iversity
Environmental Science and Technology | Year: 2013

So far, conventional processes that have been employed to delacquer the paints decorated on used beverage cans (UBCs) are less than satisfactory in economic and environmental effect. Therefore, a new method combining vacuum pyrolysis with dilute sulfuric acid leaching to delacquer the paints was investigated. The results of vacuum pyrolysis showed that the decoating rate increased with the increase of temperature and the paints were almost 100% removed from UBCs under the following conditions: temperature of 650 C, holding time of 20 min, and residual gas pressure lower than 0.1 kPa. The pyrolysis oil was mainly composed of phenol and 2-methy-phenol analyzed by GC-MS. The delacquered UBCs were subsequently leached with 5% H2SO4 for 60 s and TiO2 was recovered by calcining the residuals in muffle furnace at 450 C for 15 min. This innovative technology offers an effective method to delacquer paints from UBCs, which obtains excellent stripping effect and avoids the production of toxic substances generated in direct combustion process. Furthermore, the pyrolysis oil can be reused as chemical feedstock in other fields. © 2013 American Chemical Society. Source

Wu M.,Control iversity | Xu C.,Control iversity | She J.,Control iversity | Cao W.,Control iversity
Journal of Process Control | Year: 2012

This paper presents an integrated neural-network-based model for predicting the burn-through point (BTP) of a lead-zinc sintering process. This process features strong nonlinearity and time-varying parameters. First, experiments were carried out to establish a model of the gas temperature distribution (GTD) in the sintering machine; and based on the GTD model, a surface temperature model of the material (STMM) was established. Second, based on the STMM, a method of estimating the BTP that uses a soft-sensing technique was devised. In order to improve the estimation precision, a time-sequence-based model for predicting the BTP was built using grey system theory. Since the BTP is also affected by process parameters, a technological-parameter-based model for predicting the BTP was then built using a neural network. Finally, an integrated model for predicting the BTP was constructed by combining the time-sequence-based and the technological-parameter-based models using a fuzzy classifier. The result of actual runs shows that, compared to the manual control, the integrated prediction model reduced the variation in BTP by about 50. This guarantees the improvement of the quality and quantity of the sinter. © 2012 Elsevier Ltd. All rights reserved. Source

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