Research And Evaluation Of Recycling Process Of Electronic Wastes

With the rapid progress of modern technologies, all kinds of electronic products have become an indispensable part of daily life. However, behind the development and popularization of the electronic products, our society will be confronted with the environment pressure of more and more electrical wastes. The basic recycling process and efficient evaluation of the electronic wastes are investigated by the development of e-recycling model in this paper, for the reference and guidance of application and development of the recycle techniques of electronic waste. Two models are proposed to evaluate the recycle process of the electronic wastes.Based on the essence of recycle equipment and technology, the recycle process can be summed to be a purification process,. A general e-recycling model can be built, after analyzing and discussing the purification rate, original impurity content, unremovable impurity content, output value and input expenses. This model predicts the relationship of the above-mentioned five parameters generally. It can also estimate the maximum profit and optimal process time for the recycle corporation in analyzing those parameters. In addition, a conclusion that improve the purification efficiency or minimize the unremovable impurity content can improve the final profit is drawn.An artificial neural network e-recycling model is developed by using the toolbox of neural network in MATLAB. Artificial neural network model can forecast and choose the correct e-recycling operation, has the advantages in written, input, forecast and its result, saves a lot of building and testing time of the model, and deals with enormous data. In a word, the artificial neural network e-recycling model can provide a new method for the prediction and controlling of the e-recycling process.The method of optimizing the input data to the model and extending the model are discussed for the development of the artificial neural network model for e-recycling. The steps of optimizing the input data to the model and further extending the model are analyzed. The advantages of the artificial neural network e-recycling model in forecast control of the recycle process of the electronic wastes are discussed.