Predictive Model of Energy Consumption in Beer Production

Abstract

The predictive model of energy consumption is presented based on subtractive clustering and Adaptive-Network-Based Fuzzy Inference System (for short ANFIS) in the beer production. Using the subtractive clustering on the historical data of energy consumption, the limit of artificial experience is conquered while confirming the number of fuzzy rules. The parameters of the fuzzy inference system are acquired by the structure of adaptive network and hybrid on-line learning algorithm. The method can predict and guide the energy consumption of the factual production process. The reducing consumption scheme is provided based on the actual situation of the enterprise. Finally, using concrete examples verified the feasibility of this method comparing with the Radial Basis Functions (for short RBF) neural network predictive model.

Research on the Progress of Mechanized Harvesting Technologies for Garlic Mosses in China

“…Garlic cultivation in Gansu Province is mainly concentrated in Tianshui and its surrounding areas. Tianshui city is a transitional zone between temperate continental climate and subtropical climate [4]. It has sufficient sunshine and rainfall, which is extremely suitable for garlic growth.…”