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热风炉在农副产品及工业中的应用!

2019-06-18 来源:http://www.zhongyanjixie.com 发布人:admin 浏览量:
  热风炉设备广泛应用于农副产品和工业。随着农副产品和工业生产的逐年普及,热风炉生产厂家生产了一系列环保型热风炉设备。
  Hot blast stove equipment is widely used in agricultural and sideline products and industry. With the popularization of agricultural by-products and industrial production year by year, a series of environmentally friendly hot stove equipment have been produced.
  分析了高炉炼铁技术的现状和挑战,提出了高炉炼铁技术的发展目标。阐述了高温富氧喷煤对高炉炼铁的意义和作用。对高风温富氧喷煤的关键技术进行了分析和探讨。提高气温、提高富氧率、增加喷煤量是降低燃料消耗、节约生产成本、实现可持续发展的重要保证。在高风温、低燃料比冶炼条件下,目前的高炉炼铁技术具有广阔的发展前景。
  The present situation and challenges of blast furnace ironmaking technology are analyzed, and the development goals of blast furnace ironmaking technology are put forward. The significance and effect of high temperature oxygen enriched coal injection on blast furnace ironmaking are described. The key technology of high-temperature and oxygen-enriched coal injection is analyzed and discussed. Increasing air temperature, oxygen enrichment rate and coal injection rate are important guarantees for reducing fuel consumption, saving production costs and achieving sustainable development. Under the conditions of high blast temperature and low fuel ratio, the current blast furnace ironmaking technology has broad prospects for development.
  在我国,粮食等农副产品的安全储存是一个重要问题。将其水分降至安全水分是安全贮存的关键技术之一。然而,目前常用的各种类型的热风炉都存在着环境污染、热效率低等缺点。为此,在理论和实验的指导下,研制了一种新型的粮食及农副产品烘干热风炉。采用生物质燃料洁净燃烧技术和高传热强化技术,具有污染低、效率高、寿命长等优点。
  In China, the safe storage of grain and other agricultural and sideline products is an important issue. Reducing its moisture content to safe moisture is one of the key technologies for safe storage. However, all kinds of hot blast stoves commonly used at present have some shortcomings, such as environmental pollution and low thermal efficiency. Therefore, under the guidance of theory and experiment, a new type of hot blast stove for drying grain and agricultural by-products was developed. The clean combustion technology of biomass fuel and high heat transfer enhancement technology have the advantages of low pollution, high efficiency and long service life.
热风炉生产厂家
  本文论述了高风量热风道系统的设计思想。结合某顶燃式热风炉热风管道系统的设计实践,重点阐述了“无应力”和“低应力”两种典型热风管道系统设计的特点。认为热风管道,设计合理,应遵循“低压力、有序的约束控制位移、合理的耐火材料设计”的原则,从分析传热分析,应力分析和位移,管道、拉杆、轴承和波纹管配置和耐火材料匹配,同时监测主轴承和耐火材料的生产和施工质量,从根本上保证了热风管的稳定性,使用寿命长。
  In this paper, the design idea of high air volume hot air duct system is discussed. Combining with the design practice of a top-fired hot air stove hot air pipeline system, the design characteristics of two typical hot air pipeline systems, i.e. "stress-free" and "low stress", are emphatically expounded. It is considered that the design of hot air pipeline is reasonable and should follow the principle of "low pressure, orderly restraint, displacement control and reasonable refractory design". From the analysis of heat transfer, stress analysis and displacement, pipe, tie rod, bearing and bellows configuration and refractory matching, the production and construction quality of main bearing and refractory material should be monitored, so as to ensure the stability and service life of hot air pipeline fundamentally. Long life.
  在钢铁生产过程中,副产气占钢铁企业总能耗的40%。热风炉是副产煤气系统的主要用户之一。针对现有预测模型提前期短的问题,建立了基于时间序列的BP神经网络预测模型,在保证预测精度高的前提下,将提前期延长至30min。以现场采集的热风炉煤气数据为数据样本,发现训练样本为2000,预测样本为30时预测效果较好,平均误差绝对值可达4.04%。通过对不同预测模型的比较,表明该模型适用于热风炉用气量的中期预测。
  In the process of iron and steel production, by-product gas accounts for 40% of the total energy consumption of iron and steel enterprises. Hot blast stove is one of the main users of byproduct gas system. Aiming at the problem of short lead time of existing forecasting models, a BP neural network forecasting model based on time series is established, which can extend the lead time to 30 minutes on the premise of guaranteeing high forecasting accuracy. Taking the hot blast stove gas data collected on site as data sample, it is found that the training sample is 2000 and the prediction sample is 30, the prediction effect is better, and the average absolute error can reach 4.04%. The comparison of different prediction models shows that the model is suitable for medium-term prediction of hot blast stove gas consumption.
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