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Electricity consumption and coal consumption are two important indicators in the cement calcination process. Modeling predictions of cement energy consumption support efforts aimed at understanding energy use and energy conservation. However, due to the three characteristics of cement: time-varying delay, non-linearity and uncertainty, it is very difficult to establish accurate energy consumption prediction models. To solve the above problems, a multiple-index energy consumption prediction model based on sliding window deep belief network (SW-DBN) is proposed in this paper. Specifically, to avoid studying complex problem of time-varying delay, the sliding window method is introduced to deep belief network, which combines the previous and current variable data into time series data. As a result, all temporal information related to the energy consumption data is fed to the input layer of deep belief network. Then deep belief network is utilized to establish the multiple-index energy consumption prediction model on the temporal information, which is capable of predicting electricity consumption and coal consumption simultaneously. Experimental results show that the proposed model obtains improvement for multiple-index energy consumption prediction model in cement calcination process.

The objective of this study is to simulate the dynamic bone adaptation, and to apply this method on patients to predict the effect of different biomechanical factors on cementless Total Hip Arthroplasty (THA) results. The results show that the factors considered and their combination had obvious effects on the bone remodeling results., L'objectif de cette étude est de simuler l'adaptation dynamique osseuse et d'appliquer cette méthode sur des patients pour prédire l'effet de facteurs biomécaniques sur les arthroplasties de hanche sans ciment. Les résultats montrent que les facteurs considérés et leur combinaison ont des effets évidents sur le remodelage osseux.