Anaerobic digestion is a new method for treating kitchen waste, which can reduce waste,protect environment, and create clean energy. Because the concentration of volatile fatty acids (VFA) in kitchen waste anaerobic digestion process can not be measured in real time online, a soft measurement method based on deep belief network (DBN) is applied to the measurement of VFA. In this paper, extreme learning machine (ELM) is applied to the training of deep belief network. The adaptive learning rate is introduced to increase the convergence speed of the network, which are different from traditional deep belief network. The data from a real plant is classified and decomposed using Gaussian mixture model(GMM) and ensemble empirical mode analysis (EEMD) before training the network firstly. A deep belief network is used to perform numerical analysis on original data to extract features. Then the extracted features are input into extreme learning machine for training to obtain a soft measurement model.Experimental verification shows that this method is more precise than tradition methods and pure deep belief network model.
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