Improvements of the cyclone separator performance by down-comer tubes.

Enhancement of fine particle (PM2.5) separation is important for cyclone separators to reduce any extra purification process required at the outlet. Therefore, the present experimental research was performed to explore the performance of cyclone separators modified with down-comer tubes at solid loading rates from 0 to 8.0 g/m(3) with a 10 m/s inlet velocity. The study proved the effectiveness of down-comer tubes in reducing the particle re-entrainment and increasing the finer separation with acceptable pressure drops, which was pronounced at low solid loading conditions. The experimental results were compared with theories of Smolik and Muschelknautz. Theories were acceptable for certain ranges, and theory breakdown was mainly due to the neglect of particle agglomeration, re-entrainment and the reduction of swirling energy, as well as the increase of wall friction due to presence of particles.

Chaotic attitude oscillations of a satellite filled with a rotating ellipsoidal mass of liquid subject to gravity-gradient torques.

The Hamiltonian equations of a liquid-filled satellite subject to gravity-gradient torques in terms of the generalized Deprit variables are established for applying the Melnikov theory formally. The heteroclinic orbits of the torque-free symmetric liquid-filled satellite are found. A criterion for the heteroclinic transversal intersections at the onset of chaotic attitude is formulated using Melnikov's integral. The results from the Melnikov theory are crosschecked with simulation.

Using improved neural network model to analyze RSP, NOx and NO2 levels in urban air in Mong Kok, Hong Kong.

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of air pollutant parameters becomes an important and popular topic in environmental science. Airborne pollution is a serious, and will be a major problem in Hong Kong within the next few years. In Hong Kong, Respirable Suspended Particulate (RSP) and Nitrogen Oxides NOx and NO2 are major air pollutants due to the dominant diesel fuel usage by public transportation and heavy vehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are of significance to public and the city image. The multi-layer perceptron (MLP) neural network is regarded as a reliable and cost-effective method to achieve such tasks. The works presented here involve developing an improved neural network model, which combines the principal component analysis (PCA) technique and the radial basis function (RBF) network, and forecasting the pollutant levels and tendencies based in the recorded data. In the study, the PCA is firstly used to reduce and orthogonalize the original input variables (data), these treated variables are then used as new input vectors in RBF neural network model established for forecasting the pollutant tendencies. Comparing with the general neural network models, the proposed model possesses simpler network architecture, faster training speed, and more satisfactory predicting performance. This improved model is evaluated by using hourly time series of RSP, NOx and NO2 concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP. NOx and NO2 concentrations with the actual data of these pollutants recorded at the monitory station, the effectiveness of the proposed model has been proven. Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and has advantages over the traditional neural network methods.

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization.

Air pollution has emerged as an imminent issue in modern society. Prediction of pollutant levels is an important research topic in atmospheric environment today. For fulfilling such prediction, the use of neural network (NN), and in particular the multi-layer perceptrons, has presented to be a cost-effective technique superior to traditional statistical methods. But their training, usually with back-propagation (BP) algorithm or other gradient algorithms, is often with certain drawbacks, such as: 1) very slow convergence, and 2) easily getting stuck in a local minimum. In this paper, a newly developed method, particle swarm optimization (PSO) model, is adopted to train perceptrons, to predict pollutant levels, and as a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective by predicting some real air-quality problems.