**3. Material and methods**

from environmentally friendly and efficient methods for extraction of rare earth elements from secondary sources (ENVIREE)—ERA-NET ERA-MIN-funded research project [1]. Recently, rare earth elements (REEs) have received increased attention due to their importance in many high-tech and clean energy applications, although very limited life cycle assessment (LCA)

Life cycle assessment (LCA) is one of the tools that is increasingly being used to examine the environmental impact of a product through its entire life cycle [3]. Udo de Haes et al. [4] highlighted LCA as a global tool, while a wide range of LCA applications are presented in [5]. The increasing application of LCA as a tool for making policy decisions as well as material and design choice and need for robust and up-to-date information for such studies is also

Uncertainty is a pervasive topic in LCA and can be defined in various ways [8]. In [9], definition of uncertainty given by [10] is quoted: "Uncertainty is defined as incomplete or imprecise knowledge, which can arise from uncertainty in the data regarding the system, the choice of models used to calculate emissions and the choice of scenarios with which to define system

LCA is an analytical tool which needs intensive data: methodological choices, initial assumptions, and degree of data uncertainty have a profound effect on validity of LCA results [11], and existing quantitative uncertainty methods in LCA require also a huge amount of accurate data [12]. Problem of aleatory uncertainty or "lack of knowledge" and epistemic uncertainty or "variability" [13] is discussed in [14], when it is highlighted that quantification for the aleatory uncertainty is usually performed using the MC. Detailed description of the combination of sources of uncertainty (parameter, model, and scenario uncertainties) and methods (deterministic, probabilistic, possibilistic, and simple methods) to address them is presented by [15]. According to the [16] discussed municipal solid waste incineration model, it is suggested that

The LCI analysis involved the collection and calculation of data and procedures to quantify the relevant input and output of the product system. Very often large amounts of data required for LCI [17, 18] are affected by uncertainty [19, 20]. The main sources of uncertainty presented in [21, 22] is quoted in [23, 24]. With respect to parameter uncertainty, the common practice in LCA consists in representing uncertainty parameters by single probability distributions, e.g., a normal distribution is characterized by an average and a standard deviation [25, 26], while in [8], uncertainty is defined as geometric standard deviation of intermediate and elementary exchanges at the unit process level. To obtain a result, different statistical methods can be applied. The most well-known sampling method is MC simulation (e.g., [18–26]) easily applied to LCA [27], while a most sophisticated method is the Latin hypercube (LH) method, where the sampling strategy is not entirely random but utilizes stratified probability distributions [27]. MC simulation is also recommended in the IPCC 2006 Guidelines [28, 29]. Most software for LCA is by now able to deal with uncertainties, in most cases on the

studies have been conducted [2].

presented in [6, 7].

28 Lanthanides

**2. Uncertainty in LCA**

boundaries, respectively."

uncertainty is due to data gap or inaccurate data.

The framework of LCA, structured according to International Organization for Standardization ISO 14040 [38] standard, is described in [39].

#### **3.1. Goal and scope of the study**

The goal of this study was to provide LCI under uncertainty calculus on the probabilistic MC approach for the primary data delivered from the secondary REE recovery process following the guidelines in ISO 14040:2006 standard.

#### *3.1.1. Functional unit*

The FU, central concept in LCA, is the measure of the performance delivered by the system under study [3, 18]. The FU has been defined as 1000 kg of a secondary source to be excavated and processed as the input for all subsequent processes.

#### *3.1.2. Data quality and collection*

As noted above, very often LCI required a lot of data [17, 18] that are well correlated to the study context [40]. Data quality is discussed widely in literature [17, 23, 40–44]. In [45] analyzed uncertainty in a comparative LCA of hand drying systems pointed that data collection is one of the limitations in their LCA analysis. The databases presented in this study are affected to several uncertainties. According to [41], the basic uncertainty in data quality considerations of the inventory of rare earth concentrate processes comes out with data obtained from the literature studies. Large uncertainties exist for the infrastructure and also for particle emissions, fresh water use, and land use [41]. Another reason for the uncertainties is the nature of the chemicals used for the recovery of the REEs from the concentrate after flotation and beneficiation processes (e.g., collector, conditioner, depressant) due to production system characterized by diverse practices and technologies [41, 46], as well as various laboratory methods.

Monazite and xenotime from titania-zircon paleo beach placers in Australia, in the 1980s, were the third most important source of REEs in the world [52]. According to [53], in Australia, monazite typically has associated radioactivity due to thorium content (by substitution up to 30%). Until 1995, rare earth production in Australia was largely a byproduct of processing

Life Cycle Inventory (LCI) Approach Used for Rare Earth Elements (REEs) from Monazite Material…

http://dx.doi.org/10.5772/intechopen.80261

In addition to Australia, monazite deposits in Brazil, India, Malaysia, Thailand, China, Thailand, Sri Lanka, South Africa, and the United States constitute the second largest segment [49]. Present-day production is from India, Malaysia, Sri Lanka, Thailand, and Brazil [52]. Moreover, approximately 500 t of monazite per year was produced from 1952 to 1994 as a byproduct of titania-zircon production from Pleistocene sands near Green Cove Springs in Florida [52].

The Carolina monazite belt, from which a total of about 5000 t of monazite was produced

[55]. Bear Valley, Idaho, where monazite- and yttrium-bearing euxenite was mined by dredging, contains an estimated 10,000 t of REOs along with significant niobium and tantalum, on the basis of data from [56]. At Baotou, the largest producer of rare earths in China, the

According to ENVIREE project, flotation tests have been carried out on the flotation tailing from New Kankberg to find out if the REEs can be recovered [57]. The results indicate that most of the REEs are in monazite. *Monazite* is the second most important rare earth, after bastnaesite, and is a rare earth phosphate mineral that contains various amounts of thorium [50]. Sample from the flotation tailing was delivered to the ENVIREE project. After delivery of samples and their homogenization, they were analyzed. As mentioned above, ICP-MS analysis of samples was investigated,

In this study we concentrate on a set of 16 REEs, denoted as critical [58] (European Commission 2014), namely, Sc, Dy, Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Ho, Er, Tm, Tb, Yb, and Lu. MC, an uncertainty propagation method [59], required definition of the mean, type of statistical distribution, and standard deviation (SD) for each parameter [59]. In this study, the uncertainty analysis was modeled using probability distributions considered to be lognormal (term *lognormal distribution*) was derived from [60], according to the criteria proposed by [18] that "heavy metals is a sum parameter in the form of Pb, equivalents of following heavy metals: As, B, Cr, Cu, Hg, Mn, Mo, Ni, Pb and Sb," and according to the estimations published by [61], as well as following the [62, 63] indication, that environmental parameters in LCA studies are independent and usually follow the lognormal distribution as do the impact results [59]. Other studies showed that the lognormal distribution has been used by [9] for the variability assessment by means of bootstrap technique (applied for the computation of the median absolute deviation (MAD) for measure of the *variability* in statistical analysis). As pointed out by [62], the lognormal distribution has an upside-down bathtub-shaped hazard rate [64, 65], and no negative values are possible [18]. Lognormal distribution always remains positive, and it is consistent with the data available in the ecoinvent database and the pedigree matrix approach, as suggested by [45]. In addition, it is interesting to note that according to analysis, the trace element concentrations in gold processing have been concluded that concentration distribution of the elements between the grinding stages and the discharge stages was not uniform probably due to the different physical and chemical processes at various stages [64, 65].

in order to test the availability of REE extraction. The results are presented in **Table 1** [1].

of monazite

31

between 1885 and 1917, has considerable placer reserves that average 0.25 kg/m3

bastnesite concentrates contain a small amount of monazite [49].

monazite contained in heavy mineral sands [54].

The primary data used in the study is obtained from the Deliverable D1.2 Report on the physical-chemical properties of available materials for the recovery of REE and Deliverable D1.3 chemical and mineralogical data of secondary REE sources [1]. The secondary data used in the study is obtained from the following sources:

