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

This study describes the development of life cycle inventory (LCI) to rare earth elements (REEs) based on the secondary sources, conducted according to ISO 14040 (2006) guidelines. Monte Carlo (MC) simulation with the Crystal Ball (CB) spreadsheet-based software was employed to stochastic modeling of life cycle inventory. The number of simulations was set at 10,000. The study scope considered LCI associated with REE concentrate production from New Kankberg (Sweden) gold mine tailings production (input gate) to the final delivery of rare earth elements (end gate) to reprocessing/beneficiation for rare earth element recovery. For the presented case, lognormal distribution has been assigned to scandium (Sc), dysprosium (Dy), yttrium (Y), lanthanum (La), cerium (Ce), praseodymium (Pr), neodymium (Nd), samarium (Sm), europium (Eu), gadolinium (Gd), holmium (Ho), erbium (Er), terbium (Tb), thulium (Tm), ytterbium (Yb), and lutetium (Lu). The MC simulation (10,000 trials) for the sum of analyzed REEs used for CB is presented in the form of statistics. Sensitivity analysis (SA) presented in the form of tornado charts and spider charts was performed. The results from this study suggest that uncertainty analysis is a powerful tool that should support and aid decision-making and is more trusted than the deterministic approach

The Thermal Waste Treatment Plant in Kraków, Poland: A Case Study

The thermal waste treatment plant (TWTP) in Kraków (eco-incinerator) was created as a response to the energy and ecological needs of Kraków as part of the project “Municipal Waste Management Program in Krakow.” The TWTP is able to process 220,000 tons of municipal waste during the year. Estimated values of the 65,000 MWh of electricity and 280,000 MWh of heat are produced as a result of the waste combustion. The energy obtained by way of the thermal transformation process is largely organic and renewable. The TWTP is equipped with a state-of-the-art exhaust purification system that meets strict emission standards for air. The emission standards will meet the requirements the Ordinance of the Minister of the Environment of November 4, 2014 on emission standards for certain types of installations, sources of fuel combustion and devices for incineration or co-incineration of waste (Journal of Laws of 2014, item 1546, including further amendments). The cleaning process takes place in the exhaust aftertreatment process and is based on the following steps: (i) denitrification of exhaust gases, (ii) flue gas cleaning by means of a semi-dry method and (iii) dust extraction. As the project’s general contractor was POSCO E&C from South Korea

Life Cycle Inventory (LCI) Modeling of Municipal Solid Waste (MSW) Management Systems in Kosodrza, Community of Ostrów, Poland: A Case Study

The purpose of this study is to perform the life cycle assessment (LCA) limited to life cycle inventory (LCI) related to municipal solid waste operating in Kosodrza, community of Ostrów, in Poland. The current LCI is a representative for year 2015 by application of PN-EN ISO 14040. The system boundary was labeled as gate-to-gate. The data used in this study, involving consumption of energy and fuels, water, materials, and waste, is obtained from (i) site-specific measured or calculated data and (ii) secondary data taken from integrated permit issued by Marshal of the Podkarpackie region in Rzeszów for the establishment of municipal services in Ostrów by entering the records concerning the waste landfill in Kosodrza. This study is based on the deterministic approach to LCI. Hence, uncertainty analysis is not carried out. The LCI model can be used in full LCA study

Stochastic analysis in production process and ecology under uncertainty

The monograph addresses a problem of stochastic analysis based on the uncertainty assessment by simulation and application of this method in ecology and steel industry under uncertainty. The first chapter defines the Monte Carlo (MC) method and random variables in stochastic models. Chapter two deals with the contamination transport in porous media. Stochastic approach for Municipal Solid Waste transit time contaminants modeling using MC simulation has been worked out. The third chapter describes the risk analysis of the waste to energy facility proposal for Konin city, including the financial aspects. Environmental impact assessment of the ArcelorMittal Steel Power Plant, in Kraków - in the chapter four - is given. Thus, four scenarios of the energy mix production processes were studied. Chapter five contains examples of using ecological Life Cycle Assessment (LCA) - a relatively new method of environmental impact assessment - which help in preparing pro-ecological strategy, and which can lead to reducing the amount of wastes produced in the ArcelorMittal Steel Plant production processes. Moreover, real input and output data of selected processes under uncertainty, mainly used in the LCA technique, have been examined. The last chapter of this monograph contains final summary. The log-normal probability distribution, widely used in risk analysis and environmental management, in order to develop a stochastic analysis of the LCA, as well as uniform distribution for stochastic approach of pollution transport in porous media has been proposed. The distributions employed in this monograph are assembled from site-specific data, data existing in the most current literature, and professional judgment

Life Cycle Inventory (LCI) Stochastic Approach Used for Rare Earth Elements (REEs), Considering Uncertainty

The purpose of the paper is to present the results of the stochastic modelling with uncertainty performed with the use of Monte Carlo (MC) simulation with 10,000 cycles and a confidence interval of 95 %, as recommended. Analysed REEs were fitted by lognormal distributions by using the Crystal Ball® (CB) spreadsheet-based software after defining the geometric mean value (μg) and the standard deviation (σg), automatically calculated (matches) the lower, as well as, upper boundaries of lognormal distribution. The number of replications of a simulation affects the quality of the results. The principal output report provided by CB and presented in this study consists of the graphical representation in the form of the frequency chart, percentiles summary, and statistics summary. Additional CB options provide a sensitivity analysis with tornado diagrams. The data that was used for MC simulation of the LCI model includes available and published data concerning associated with the REEs. This paper discusses the results and show that the adopted approach is applicable for any REEs used in the LCI studies under uncertainty. The results obtained from this study can be used as the first step in performing a full LCA analysis and help practitioners as well as decision-makers in the environmental engineering and management

Zastosowanie analizy niepewności opartej na symulacji Monte Carlo (MC) do inwentaryzacji cyklu życia (LCI)

The use of Monte Carlo (MC) simulation was presented in order to assess uncertainty in life cycle inventory (LCI) studies. The MC method is finded as an important tool in environmental science and can be considered the most effective quantification approach for uncertainties. Uncertainty of data can be expressed through a definition of probability distribution of that data (e.g. through standard deviation or variance). The presented case in this study is based on the example of the emission of SO2, generated during energy production in Integrated Steel Power Plant (ISPP) in Kraków, Poland. MC simulation using software Crystal Ball® (CB), software, associated with Microsoft® Excel, was used for the uncertainties analysis. The MC approach for assessing parameter uncertainty is described. Analysed parameter (SO2,) performed in MC simulation were assigned with log-normal distribution. Finally, the results obtained using MC simulation, after 10,000 runs, more reliable than the deterministic approach, is presented in form of the frequency charts and summary statistics. Thanks to uncertainty analysis, a final result is obtained in the form of value range. The results of this study will encourage other researchers to consider this approach in their projects, and the results of this study will encourage other LCA researchers to consider the uncertainty in their projects and bring closer to industrial application

Chapter Life Cycle Inventory (LCI) Approach Used for Rare Earth Elements (REEs) from Monazite Material, Considering Uncertainty

This study describes the development of life cycle inventory (LCI) to rare earth elements (REEs) based on the secondary sources, conducted according to ISO 14040 (2006) guidelines. Monte Carlo (MC) simulation with the Crystal Ball (CB) spreadsheet-based software was employed to stochastic modeling of life cycle inventory. The number of simulations was set at 10,000. The study scope considered LCI associated with REE concentrate production from New Kankberg (Sweden) gold mine tailings production (input gate) to the final delivery of rare earth elements (end gate) to reprocessing/beneficiation for rare earth element recovery. For the presented case, lognormal distribution has been assigned to scandium (Sc), dysprosium (Dy), yttrium (Y), lanthanum (La), cerium (Ce), praseodymium (Pr), neodymium (Nd), samarium (Sm), europium (Eu), gadolinium (Gd), holmium (Ho), erbium (Er), terbium (Tb), thulium (Tm), ytterbium (Yb), and lutetium (Lu). The MC simulation (10,000 trials) for the sum of analyzed REEs used for CB is presented in the form of statistics. Sensitivity analysis (SA) presented in the form of tornado charts and spider charts was performed. The results from this study suggest that uncertainty analysis is a powerful tool that should support and aid decision-making and is more trusted than the deterministic approach

Application of stochastic approach based on Monte Carlo (MC) simulation for life cycle inventory (LCI) of the rare earth elements (REEs) in beneficiation rare earth waste from the gold processing: case study

The study proposes an stochastic approach based on Monte Carlo (MC) simulation for life cycle assessment (LCA) method limited to life cycle inventory (LCI) study for rare earth elements (REEs) recovery from the secondary materials processes production applied to the New Krankberg Mine in Sweden. The MC method is recognizes as an important tool in science and can be considered the most effective quantification approach for uncertainties. The use of stochastic approach helps to characterize the uncertainties better than deterministic method. Uncertainty of data can be expressed through a definition of probability distribution of that data (e.g. through standard deviation or variance). The data used in this study are obtained from: (i) site-specific measured or calculated data, (ii) values based on literature, (iii) the ecoinvent process „rare earth concentrate, 70% REO, from bastnäsite, at beneficiation”. Environmental emissions (e.g, particulates, uranium-238, thorium-232), energy and REE (La, Ce, Nd, Pr, Sm, Dy, Eu, Tb, Y, Sc, Yb, Lu, Tm, Y, Gd) have been inventoried. The study is based on a reference case for the year 2016. The combination of MC analysis with sensitivity analysis is the best solution for quantified the uncertainty in the LCI/LCA. The reliability of LCA results may be uncertain, to a certain degree, but this uncertainty can be noticed with the help of MC method

Chapter Life Cycle Inventory (LCI) Modeling of Municipal Solid Waste (MSW) Management Systems in Kosodrza, Community of Ostrów, Poland: A Case Study

The purpose of this study is to perform the life cycle assessment (LCA) limited to life cycle inventory (LCI) related to municipal solid waste operating in Kosodrza, community of Ostrów, in Poland. The current LCI is a representative for year 2015 by application of PN-EN ISO 14040. The system boundary was labeled as gate-to-gate. The data used in this study, involving consumption of energy and fuels, water, materials, and waste, is obtained from (i) site-specific measured or calculated data and (ii) secondary data taken from integrated permit issued by Marshal of the Podkarpackie region in Rzeszów for the establishment of municipal services in Ostrów by entering the records concerning the waste landfill in Kosodrza. This study is based on the deterministic approach to LCI. Hence, uncertainty analysis is not carried out. The LCI model can be used in full LCA study

Stochastic approach based on Monte Carlo (MC) simulation used for Life Cycle Inventory (LCI) uncertainty analysis in Rare Earth Elements (REEs) recovery

According to the European Commission’s Report on Critical Raw Materials and the Circular Economy, the raw materials, such as rare earths, have a high economic importance for the EU, and are essential for the production of a broad range of goods and applications used in everyday life, as well as they are crucial for a strong European industrial base. Uncertainty plays an important role in the real world used Life Cycle Assessment (LCA) approach. The validity of LCA depends strongly on the significance of the input data. Data uncertainty is often mentioned as a crucial limitation for a clear interpretation of LCA results. The stochastic modelling used for Monte Carlo (MC) analysis simulation was reported in order to assess uncertainty in life cycle inventory (LCI) of rare earth elements (REEs) recovery. The purpose of this study was REEs recovery from secondary sources analysed in the ENVIREE ERA-NET ERA-MIN-funded research project. The software Crystal Ball® (CB) program, associated with Microsoft® Excel, was used for the uncertainties analysis. Uncertainty of data can be expressed through a definition of probability distribution of those data. The output report provided by CB, after 10000 runs is reflected in the frequency charts and summary statistics. The analysed parameters were assigned with lognormal distribution. The uncertainty analysis offers a well-defined procedure for LCI studies, and provides the basis for defining the data needs for full LCA of the REEs beneficiation process. Results can improve current procedures in the REEs beneficiation process management and bring closer to industrial application through the involvement of end users