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Block-based approach to modelling of granulated fertilizers' quality

机译:基于块的颗粒状肥料质量建模方法

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摘要

Fertilizer manufacturing is a customer-driven industry, where the quality of a product is a key factor in order to survive the competition. However, measuring the most important feature with granulated fertilizers, flowability, is tedious, time-consuming and thus expensive. Flowability can be defined through testing the flow rate with, e.g., seed drill. Besides the chemical composition, flowability can be considered as one of the most important characteristics. There are numerous factors affecting the flowability of a granulated fertilizer, several of them related to the particle size distribution. Particle size distribution of the granulated product has to be within the customer specification, but is also a highly significant factor to the quality, especially in the presence of moisture. This can affect numerous phenomena, inter alia, agglomeration and aggregation of granules and Ostwald ripening, where the smallest crystals dissolve while the large ones grow. Chemical composition affects the crystallization process and is significant for particle shape and various physical properties. Sometimes, as in this case, the variables form a natural division into blocks. It may be of interest to observe the different effect of the blocks on the modelling task. The present approach is based on priority PLS and multi-block PLS. The data is measured from the final product and is divided into blocks between physical properties, such as granule hardness and roundness, chemical composition and particle size distribution. The goals are to find a reliable model for flowability using this data and to find the most important variables and to identify the effect of blocks to the quality.
机译:化肥制造业是一个以客户为导向的行业,产品质量是在竞争中生存的关键因素。然而,用粒状肥料测量最重要的特征,即流动性,既繁琐,费时又昂贵。流动性可以通过例如使用播种机测试流速来定义。除了化学成分外,流动性也可以认为是最重要的特性之一。影响粒状肥料流动性的因素很多,其中一些因素与粒度分布有关。造粒产品的粒度分布必须在客户要求的范围之内,但这也是质量的重要因素,尤其是在存在水分的情况下。这会影响许多现象,尤其是颗粒的团聚和聚集以及奥斯特瓦尔德熟化,其中最小的晶体溶解而大的晶体生长。化学成分会影响结晶过程,并且对于颗粒形状和各种物理性质非常重要。有时,在这种情况下,变量将自然划分为多个块。观察这些块对建模任务的不同影响可能会很有趣。本方法基于优先级PLS和多块PLS。数据是从最终产品中测得的,并分为物理性质之间的块,例如颗粒硬度和圆度,化学成分和粒度分布。目标是使用此数据找到一种可靠的流动性模型,并找到最重要的变量并确定块对质量的影响。

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