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The thesis put forward a new global variable prediction model whose theories, structure of modeling and operation were discussed. Applying BP neural network in solid waste dry fermentation garage environment control, the reliability and stability of the new control method was proved. KEYWORD: Solid Waste Dry Fermentation Garage; Environment Control; Neural Network; Global Variable International Conference on Industrial Technology and Management Science (ITMS 2015) © 2015. The authors Published by Atlantis Press 161 to enable compliance with the environmental factors established by the requirements. 2.2 Solid waste dry fermentation garage control system requirements Solid waste dry fermentation garage intelligent control system uses artificial intelligence model to generate the control program, so that all actuators common coordination to achieve optimal control. Intelligent control is required to achieve the necessary basis of the following: 1) Control model’s exactness.Fermentation to a certain period of solid waste fermentation garage, one of the garage environment will affect the regulation of solid waste fermentation, on the other hand due to transpiration garage materials sink and create new environmental factors affect the garage, thereby generating a feedback mechanism in the conventional dry fermentation environment control system does not consider the effect of this feedback mechanism. Also exist among the dry fermentation garage environment, strong couplings and the elements, certain elements of change will have an impact on other elements. Therefore, intelligent control systems need to create accurate models, achieve regulatory approach which can save resources and improve productivity. 2) Detection of large amounts of data. Accurate modeling need to collect large amounts of data (including time and space), the data collected by the sensors are to provide support for modeling. Solid waste fermentation achieve intelligent control model garage, not only need to consider the value of the internal fermentation treasurer parameters such as temperature, PH value, ORP, etc., also need to consider the external environment of the garage fermentation parameters, as well as local real-time weather conditions and prediction of short-term weather conditions. 3 SOLID WASTE REPOSITORY OVERALL STRUCTURE OF DRY FERMENTATION Using two small-scale experiments solid waste dry fermentation garage, a single database volume 75m 3 , garage roof insulation plastic, dry fermentation garage is divided into 10 regional monitoring the thermal region shown in Figure 1. 1.heating system 2.solid waste fermentation area 3.top of dry fermentation garage 4.external environment of garage solid waste solid waste heating coil heating coil Figure 1. Experiment temperature heat area schematic diagram Taking into account the coupling of dry fermented garage of the parameters, the dry fermentation garage is divided into four regions. Each locale is a collection of data related to the sensor, the sensor data acquisition and category type as shown in Table 1 (experiment considering only the temperature, PH value, ORP, gas production ,but COD and other factors are not discussed. ) Table 1. The regional of dry fermentation garage data acquisition’s type and quantity. area Data acquisition category Number of Temperature sensors Number of PH meter Number of ORP meter 1 Heating system 1 0 0 2 Solid waste fermentation area 10 1 1 3 Top of dry fermentation garage 1 0 0 4 External environment of garage 1 0 0 4 SOLID WASTE DRY FERMENTATION GARAGE CONTROL PROGRAM In the dry fermentation garage, the main factors affecting the degradation and solid waste management costs are inside temperature, PH value, such as these two parameters can accurately predict and control in this forecast, we can achieve the best control effect, the control program shown in Figure2. ideal value Intelligent control model Predictive value Global variables predictive model output Regional sensor data in garage Each actuator current work status Temperature forecasts outside the garage Actuators Weather conditions outside the garage Online weather forecast Solid waste fermentation model Figure 2. Control structure

Environment Control Method in Solid Waste Dry Fermentation Garage based on Global Variable Prediction Model