López Lilao Ana, Sanfélix Forner Vicenta, Mallol Gasch Gustavo, Monfort Gimeno Eliseo
a Instituto de Tecnología Cerámica , AICE, Universitat Jaume I , Castellón , Spain.
J Occup Environ Hyg. 2017 Dec;14(12):975-985. doi: 10.1080/15459624.2017.1358818.
A wide variety of raw materials, involving more than 20 samples of quartzes, feldspars, nephelines, carbonates, dolomites, sands, zircons, and alumina, were selected and characterised. Dustiness, i.e., a materials' tendency to generate dust on handling, was determined using the continuous drop method. These raw materials were selected to encompass a wide range of particle sizes (1.6-294 µm) and true densities (2650-4680 kg/m). The dustiness of the raw materials, i.e., their tendency to generate dust on handling, was determined using the continuous drop method. The influence of some key material parameters (particle size distribution, flowability, and specific surface area) on dustiness was assessed. In this regard, dustiness was found to be significantly affected by particle size distribution. Data analysis enabled development of a model for predicting the dustiness of the studied materials, assuming that dustiness depended on the particle fraction susceptible to emission and on the bulk material's susceptibility to release these particles. On the one hand, the developed model allows the dustiness mechanisms to be better understood. In this regard, it may be noted that relative emission increased with mean particle size. However, this did not necessarily imply that dustiness did, because dustiness also depended on the fraction of particles susceptible to be emitted. On the other hand, the developed model enables dustiness to be estimated using just the particle size distribution data. The quality of the fits was quite good and the fact that only particle size distribution data are needed facilitates industrial application, since these data are usually known by raw materials managers, thus making additional tests unnecessary. This model may therefore be deemed a key tool in drawing up efficient preventive and/or corrective measures to reduce dust emissions during bulk powder processing, both inside and outside industrial facilities. It is recommended, however, to use the developed model only if particle size, true density, moisture content, and shape lie within the studied ranges.
选取了多种原材料，包括20多种石英、长石、霞石、碳酸盐、白云石、沙子、锆石和氧化铝样本，并对其进行了表征。采用连续滴流法测定了粉尘度，即材料在处理过程中产生粉尘的倾向。选择这些原材料是为了涵盖广泛的粒径范围（1.6 - 294微米）和真密度范围（2650 - 4680千克/立方米）。采用连续滴流法测定了原材料在处理过程中产生粉尘的倾向，即其粉尘度。评估了一些关键材料参数（粒度分布、流动性和比表面积）对粉尘度的影响。在这方面，发现粉尘度受粒度分布的显著影响。数据分析建立了一个预测所研究材料粉尘度的模型，假设粉尘度取决于易排放颗粒的分数以及散装材料释放这些颗粒的敏感性。一方面，所建立的模型有助于更好地理解粉尘度机制。在这方面，可以注意到相对排放量随平均粒径增加。然而，这并不一定意味着粉尘度也增加，因为粉尘度还取决于易排放颗粒的分数。另一方面，所建立的模型仅使用粒度分布数据就能估算粉尘度。拟合质量相当好，而且仅需要粒度分布数据这一事实便于工业应用，因为这些数据通常是原材料管理人员所熟知的，因此无需进行额外测试。因此，该模型可被视为制定有效预防和/或纠正措施以减少工业设施内外散装粉末加工过程中粉尘排放的关键工具。然而，建议仅在粒径、真密度、含水量和形状处于所研究范围内时使用所建立的模型。
