Low-carbon Optimization Model Research And Application In Manufacturing Process

Manufacturing is the pillar industry which holds the balance of national economy. Energyis an important factor in manufacturing process. It influences the benefits as well as the socialduty of enterprises. With the lack of energy and the rising of the price in domestic andoverseas, it’s very important to carry out the research on low-carbon optimization model,especially in tire manufacturing. However, the research on low-carbon optimization modelhas just started in domestic. And the framework model of low-carbon manufacturing systemhas not been built. Therefore，to reduce energy consumption of manufacturing process in tireenterprise, we build the framework model and low-carbon optimization model ofmanufacturing system in this dissertation. And we realize an energy management systembased on the low-carbon optimization model.The main work of this dissertation can be summarized as follows:（1） A comprehensive review of low-carbon manufacturing and low-carbonoptimization model is carried out in this dissertation. Several significant development statusesare summarized, including low-carbon manufacturing, low-carbon production scheduling andlow-carbon anomaly detection. Meanwhile, the problems of the low-carbon productionscheduling model and low-carbon anomaly detection are discussed.（2） The framework model of low-carbon manufacturing system is built, includingeffect factors of energy consumption, energy consumption constitute model and operationevaluation method of the low-carbon manufacturing system. Then energy consumption of thetire production process and the key process influencing factors is discussed. Moreover, theintegrated model of the curing energy consumption data is built, and the comprehensiveenergy consumption of curing is defined and calculated. It provides theoretical support forusing the related models to solve practical problems.（3） Aiming at the energy waste problems resulting from machine idling and processwaiting in the manufacturing process of tire, a low-carbon scheduling model is built in thisdissertation. Genetic algorithm is used to validate the preciseness of the model. Then based onthe typical production process of tire, we obtain energy consumption data and processing timedata according to the actual production, and the simulating experiment is done based on it using GA. The practical experimental results show that the proposed method can reduce theenergy consumption in the production process effectively.（4） Aiming at the energy consumption anomalies resulting from leakage of steam ornitrogen, long ineffective working, improper curing parameters configuration, a low-carbonanomaly detection model based on GA-SVR is proposed. In this method, the interval ofnormal curing energy efficiency is predicted based on GA-SVR model, and all the observedvalues beyond the predicted interval are considered as energy anomaly. Compared with thethreshold comparison method, the proposed method can discover curing energy anomalies,like leakage of steam or nitrogen, long ineffective working, improper curing parametersconfiguration, and etc, effectively.（5） Based on the demand of energy management system in Fengli, we designe thestructure and the function of the energy management system. In this dissertation, we mainlydo research on the system design and realization of low-carbon scheduling and low-carbonanomaly detection. It achieves good effects in the preliminary application.