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烟气制酸生产流程的能耗及节能潜力分析研究

Research and Analysis on Energy Consumption and Energy Saving Potential of Metallurgical Sulfuric Acid Production Process

【作者】 孙平平

【导师】 时章明;

【作者基本信息】 中南大学 , 热能工程, 2011, 硕士

【摘要】 冶炼烟气制酸广泛存在于有色行业,由于其本身属于冶炼烟气回收范畴,且耗能较低,所以多年来系统节能研究接近空白。然而近几年烟气作为制酸原料的比例大幅提高、产量也不断攀升,使得烟气制酸乃至硫酸行业的节能降耗工作显得越发重要。本文以某厂烟气制酸系统为研究对象,对其进行系统节能研究。在热力学第一定律的基础上,建立了该系统的热平衡模型,并进行了热平衡分析和讨论。结果表明:进出系统的冷却水热量损失为85816.66 MJ/h,占系统热支出的60.26%;除冷却水外,成品酸、污酸以及排空烟气带出热量共占系统总热损失的15.01%。采用e-p分析法计算得出,实际吨酸能耗为32.33kgce,基准吨酸能耗为14.85 kgce,节能潜力为17.48 kgce/t-H2SO4,其中因工序能耗上升引起的能耗增量为16.33 kgce,占能耗总增量的93%;因折合比不同使吨酸能耗升高1.15 kgce,占7%。采用基准物流图研究法分析各股物流对工序能耗和工序折合比的影响,进而得出对吨酸能耗的影响。结果表明:输出含S元素物流,增大该工序的工序能耗及上游折合比,从而增加吨酸能耗,主要表现为烟气损失项和污酸排出项;含S元素物流由吸收工序(下游)返回干燥工序(上游),增加吸收的工序能耗以及转化、干燥工序折合比,从而增加吨酸能耗;含S元素物流由干燥工序输入吸收工序,虽增大干燥工序能耗,但降低干燥、转化工序的折合比,综合可以降低吨酸能耗。利用Visio Basic 6.0开发设计了系统能耗分析的计算软件,主要理论基础是基准物流图研究法,可广泛应用于生产流程的能耗分析。本文最后建立了硫酸系统能耗BP神经网络预测模型,并应用此预测模型预测了制酸系统能耗,预测结果具有较高的可信度。

【Abstract】 Metallurgical sulfuric acid production exists extensively in the non-ferrous industry. All the researches about this production are belong to the field of flue gas recycling, and none of them are related to the system energy saving. However, with the increasing of the industrial output and consuming of flue gas for the acid production, the research on energy saving for the metallurgical sulfuric acid production and the sulfuric acid industry has been increasing substantially in recent years. In this paper, the system energy saving of a sulfuric acid plant was investigated.Based on first law of thermodynamics, the thermal equilibrium model was established. The results showed that:The heat loss of system cooling water was 85816.66 MJ/h which accounts for 60.26% of the total. The total heat loss for finished products, waste acid and exhaust gas of evacuation was 13391.33 MJ/h which accounts for 15.01% of the total.The main reasons that the overall energy intensity of a sulfuric acid production plant were analyzed by e-p method. It was shown from the result that the actual energy intensity of final product was 32.33 kgce and the standard energy intensity of final product was 14.85 kgce, so the energy-saving potential for enterprise was 17.48 kgce. The increment due to the increment of unit process energy intensity and products ratio was 16.33 kgce and 1.15 kgce, which takes up 93 percent and 7 percent of the overall energy saving respectively.The practical material flow diagram and the standard materials flow diagram (SMFD) ware constructed accordingly. The influences of materials flow on energy intensity were analyzed quantitatively. The following results can be obtained from the analysis:1) Outputting sulfur-containing materials from an intermediate process to surroundings increases the energy intensity of this process and product ratios of upstream processes, hence increase the energy intensity of final product, the larger the ordinal number of the unit process, the greater will be the increase. The main materials flows are loss of flue gas project and waste acid project.2) Returning sulfur-containing from absorption section (precipitation process) to former process such as drying section increases not only precipitation process energy intensity but also the product ratios of processes between absorption and drying preparation, hence increase the energy intensity of final product, the longer the returning distance, the greater will be the increase.3) Inputting sulfur-containing materials from drying section to absorption section, although increases precipitation process energy intensity, reduces the product ratios of processes between absorption and drying preparation, hence increase the energy intensity of final product.A calculation software about energy consumption analysis was designed and developed according to the theoretical basis of SMFD methodology. The calculation software can be widely used in the energy consumption analysis of production processes.BP neural network prediction model was established, which was used to predict the energy consumption of sulfuric acid factory system. There was little error between predictive value and actual value.

  • 【网络出版投稿人】 中南大学
  • 【网络出版年期】2012年 01期
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