手法を比較

	ロジン・ラムラー分布	ボンド・ワーク・インデックス	浮選速度論
分野	鉱山工学	鉱山工学	鉱山工学
系統	Process / pipeline	Process / pipeline	Process / pipeline
提唱年	1933	1952	1935
提唱者	Paul Rosin and Erich Rammler	Fred C. Bond	Garcia-Zuniga
種類	Empirical probability distribution for crushed material fineness	Empirical method for grinding energy estimation	First-order kinetic model for flotation recovery
原典	Rosin, P., & Rammler, E. (1933). The laws governing the fineness of powdered coal. Journal of the Institute of Fuel, 7, 29-36.	Bond, F. C. (1952). The third theory of comminution. Transactions of the American Institute of Mining and Metallurgical Engineers, 193, 484-494.	Garcia-Zuniga, H. (1935). Uber eine neue Methode, zur Berechnung der Flotationsausbeute. Zeitschrift fur Praktische Geologie, 43(2), 12-19.
別名	Rosin-Rammler Model, RRS Distribution, Weibull Distribution (particle size)	Bond Work Index, BWI, Bond Index Test	Batch Flotation Model, Flotation Rate Constants, Kinetic Flotation Analysis
関連	3	3	3
概要	The Rosin-Rammler Distribution, introduced by Paul Rosin and Erich Rammler in 1933, is an empirical probability distribution that describes the particle size distribution of ground or crushed materials. It characterizes fineness by two parameters: the characteristic size (d-prime) and the uniformity index (n). This distribution is remarkably accurate for mineral processing streams and is ubiquitous in comminution engineering.	The Bond Work Index, introduced by Fred C. Bond in 1952, is an empirical parameter that characterizes the resistance of an ore to grinding in a tumbling mill. It is defined as the kilowatt-hours per short ton (kWh/st) of electrical energy required to reduce a coarse ore from theoretically infinite size to 80% passing 100 micrometers. The Bond Index is foundational in mineral processing plant design and cost estimation worldwide.	Flotation kinetics is the study of how recovery of minerals from ore changes over time during flotation. The Garcia-Zuniga model, introduced in 1935, describes recovery as a first-order kinetic process with rate constant k and maximum recoverable fraction R∞. This simple model underpins flotation cell design and process optimization, enabling engineers to predict flotation performance from batch tests and scale results to industrial circuits.