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Tobacco Factory Energy Balance And Prediction Research

Posted on:2013-01-29Degree:MasterType:Thesis
Country:ChinaCandidate:Y CaoFull Text:PDF
GTID:2219330374465298Subject:Measuring and Testing Technology and Instruments
Abstract/Summary:PDF Full Text Request
Energy as the essential substance of life and production are very important to our lives and production. China's energy resources are abundant, but the population is large, the per capita amount of energy is less, can not reach the world average,wan yuan output value of energy consumption is higher than the world average. In the "12th Five-Year Plan", the state energy conservation as an important task. In particular, the energy saving is more important, not only to respond to national call, and can reduce consumption and costs, save money, improve the economic efficiency of enterprises.Tobacco companies as a very important enterprise, the energy saving is also very important. In order to achieve the purpose of energy conservation, the establishment of a reasonable and effective energy management system, full use of corporate power center of the existing station house has been equipped with automatic control system to achieve interconnection and interoperability of a variety of information and data sharing network platform mode to achieve the best economic benefits for enterprises.Cigarette factory's energy monitoring system for water, steam, gas, electricity and other media to run the system to develop a monitoring program, integration and improvement of monitoring system, boilers, desulfurization and dust removal, cooling, compressed air, vacuum, water supply, water substations and other process monitoring configuration screen, real-time monitoring of all major equipment and pipe network, the record of the main process parameters, query, and alarm.Introduced the first energy management system; research situation and its meaning; Second, the overall design of energy management systems were introduced; Again, the use of Matlab software analysis and a regression model, exponential smoothing models, gray prediction, BP neural network to predict the relevant parameters in the energy management systems, forecasting; and particle swarm optimization BP neural network prediction; last raised the idea of combinatorial prediction; Finally, the energy model of system were introduced, and lists the energy efficiency of analysis method.
Keywords/Search Tags:Energy management, Tobacco factory, The energy model, Energy flowdiagram, Energy efficiency analysis
PDF Full Text Request
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