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The recycling of spent lithium-ion batteries (LIB) is becoming increasingly important with regard to environmental, economic, geostrategic, and health aspects due to the increasing amount of LIB produced, introduced into the market, and being spent in the following years. The recycling itself becomes a challenge to face on one hand the special aspects of LIB-technology and on the other hand to reply to the idea of circular economy. In this paper, we analyze the different recycling concepts for spent LIBs and categorize them according to state-of-the-art schemes of waste treatment technology. Therefore, we structure the different processes into process stages and unit processes. Several recycling technologies are treating spent lithium-ion batteries worldwide focusing on one or several process stages or unit processes.

PURPOSE: This report proposes a life-cycle analysis (LCA)-oriented methodology for systematic inventory analysis of the use phase of manufacturing unit processes providing unit process datasets to be used in life-cycle inventory (LCI) databases and libraries. The methodology has been developed in the framework of the CO2PE! collaborative research programme (CO2PE! 2011a) and comprises two approaches with different levels of detail, respectively referred to as the screening approach and the in-depth approach. METHODS: The screening approach relies on representative, publicly available data and engineering calculations for energy use, material loss, and identification of variables for improvement, while the in-depth approach is subdivided into four modules, including a time study, a power consumption study, a consumables study and an emissions study, in which all relevant process in- and outputs are measured and analysed in detail. The screening approach provides the first insight in the unit process and results in a set of approximate LCI data, which also serve to guide the more detailed and complete in-depth approach leading to more accurate LCI data as well as the identification of potential for energy and resource efficiency improvements of the manufacturing unit process. To ensure optimal reproducibility and applicability, documentation guidelines for data and metadata are included in both approaches. Guidance on definition of functional unit and reference flow as well as on determination of system boundaries specifies the generic goal and scope definition requirements according to ISO 14040 (2006) and ISO 14044 (2006). RESULTS: The proposed methodology aims at ensuring solid foundations for the provision of high-quality LCI data for the use phase of manufacturing unit processes. Envisaged usage encompasses the provision of high-quality data for LCA studies of products using these unit process datasets for the manufacturing processes, as well as the in-depth analysis of individual manufacturing unit processes. CONCLUSIONS: In addition, the accruing availability of data for a range of similar machines (same process, different suppliers and machine capacities) will allow the establishment of parametric emission and resource use estimation models for a more streamlined LCA of products including reliable manufacturing process data. Both approaches have already provided useful results in some initial case studies (Kellens et al. 2009; Duflou et al. (Int J Sustain Manufacturing 2:80–98, 2010); Santos et al. (J Clean Prod 19:356–364, 2011); UPLCI 2011; Kellens et al. 2011a) and the use will be illustrated by two case studies in Part 2 of this paper (Kellens et al. 2011b).

Purpose This report presents two case studies, one for both the screening approach and the in-depth approach, demonstrating the application of the life cycle assessment-oriented methodology for systematic inventory analysis of the machine tool use phase of manufacturing unit processes, which has been developed in the framework of the CO 2 PE! collaborative research programme (CO 2 PE! 2011 ) and is described in part 1 of this paper (Kellens et al. 2011 ). Screening approach The screening approach, which provides a first insight into the unit process and results in a set of approximate LCI data, relies on representative industrial data and engineering calculations for energy use and material loss. This approach is illustrated by means of a case study of a drilling process. In-depth approach The in-depth approach, which leads to more accurate LCI data as well as the identification of potential for environmental improvements of the manufacturing unit processes, is subdivided into four modules, including a time study, a power consumption study, a consumables study and an emissions study, in which all relevant process in- and outputs are measured and analysed in detail. The procedure of this approach, together with the proposed CO 2 PE! template, is illustrated by means of a case study of a laser cutting process. Results The CO 2 PE! methodology aims to provide high-quality LCI data for the machine tool use phase of manufacturing unit processes, to be used in life cycle inventory databases and libraries, as well as to identify potential for environmental improvement based on the in-depth analysis of individual manufacturing unit processes. Two case studies illustrate the applicability of the methodology.

Very Long Baseline Interferometry (VLBI) solution can yield accurate information of angular position, and has been successfully used in the field of deep space exploration, such as astrophysics, imaging, detector positioning, and so on. The increase in VLBI data volume puts higher demands on efficient processing. Essentially, the main step of VLBI is the correlation processing, through which the angular position can be calculated. Since the VLBI correlation processing is both computation-intensive and data-intensive, the CPU cluster is usually employed in practical application to perform complex distributed computation. In this paper, we propose a parallel implementation of VLBI correlator based on graphics processing unit (GPU) to realize a more efficient and economical angular position calculation of deep space target. On the basis of massively GPU parallel computing, the coalesced access strategy and the parallel pipeline strategy are introduced to further accelerate the VLBI correlator. Experimental results show that the optimized GPU-based VLBI method can meet the real-time processing requirements of the received data stream. Compared with the sequential method, the proposed approach can reach a 224.1 × calculation speedup, and a 36.8 × application speedup. Compared with the multi-CPUs method, it can achieve 28.6 × calculation speedup and 4.7 × application speedup.

Purpose Chinese coal power generation is part of the life cycle of most products and the largest single source for many emissions. Reducing these emissions has been a priority for the Chinese government over the last decade, with improvements made by replacing older power plants, improving thermal efficiency and installing air pollution control devices. In the present research, we aim to acknowledge these improvements and present updated unit process data for Chinese coal power. In the course of doing so, we also explore the implementation and interpretation of overall dispersions related to a generically averaged process, such as Chinese coal power. Methods In order to capture geographical and temporal dispersions, updated unit process data were calculated for Chinese coal power at both a national and a provincial level. The updated unit process dataset was also propagated into life cycle inventory (LCI) ranges using Monte Carlo simulations, allowing for discrepancies to be evaluated against the most commonly used inventory database (ecoinvent) and overall dispersions to be shown for some selected provinces. Results and discussion Compared to ecoinvent, the updated dataset resulted in reductions with between 8 and 67 % for all evaluated inventory flows except for dinitrogen monoxide (N 2 O). However, interprovincial differences in emissions diverged with up to 250 %. A random outcome in a few Monte Carlo runs was inverted operators, where positive values became negative or the other way around. This is a known possible outcome of matrix calculations that needs to be better evaluated when interpreting propagated outcomes. Conclusions The present manuscript provides recommendations on how to implement and interpret dispersions propagated into LCI results. In addition, updated and easily accessible unit process data for coal power plants averaged across China and for individual provinces are presented, with clear distinctions of inherent uncertainties, spread (variance) and unrepresentativeness. Recommendations are also provided for future research and software developments.

Purpose Ecoinvent applies a method for estimation of default standard deviations for flow data from characteristics of these flows and the respective processes that are turned into uncertainty factors in a pedigree matrix, starting from qualitative assessments. The uncertainty factors are aggregated to the standard deviation. This approach allows calculating uncertainties for all flows in the ecoinvent database. In ecoinvent 2 the uncertainty factors were provided based on expert judgment, without (documented) empirical foundation. This paper presents (1) a procedure to obtain an empirical foundation for the uncertainty factors that are used in the pedigree approach and (2) a proposal for new uncertainty factors, received by applying the developed procedure. Both the factors and the procedure are a result of a first phase of an ecoinvent project to refine the pedigree matrix approach. A separate paper in the same edition, also the result of the aforementioned project, deals with extending the developed approach to other probability distributions than lognormal (Muller et al.). Methods Uncertainty is defined here simply as geometric standard deviation (GSD) of intermediate and elementary exchanges at the unit process level. This fits to the lognormal probability distribution that is assumed as default in ecoinvent 2, and helps to overcome scaling effects in the analysed data. In order to provide the required empirical basis, a broad portfolio of data sources is analysed; it is especially important to consider sources outside of the ecoinvent database to avoid circular reasoning. The ecoinvent pedigree matrix from version 2 is taken as a starting point, skipping the indicator “sample size” since it will not be used in ecoinvent 3. This leads to a pedigree matrix with five data quality indicators, each having five score values. The analysis is conducted as follows: for each matrix indicator and for each data source, indicator scores are set in relation to data sets, building groups of data sets that represent the different data quality indicator scores in the pedigree matrix. The uncertainty in each of the groups is calculated. The uncertainty obtained for the group with the ideal indicator score is set as a reference, and uncertainties for the other groups are set in relation to this reference uncertainty. The obtained ratio will be different from 1, it represents the unexplained uncertainty, additional uncertainty due to a lower data quality, and can be directly used as uncertainty factor candidates. Results and discussion The developed approach was able to derive empirically based uncertainty factor candidates for the pedigree matrix in ecoinvent. Uncertainty factors were obtained for all data quality indicators and for almost all indicator scores in the matrix. The factors are the result of the first analysis of several data sources, further analyses and discussions should be used to strengthen their empirical basis. As a consequence, the provided uncertainty factors can change in future. Finally, a few of the qualitative score descriptions in the pedigree matrix left room for interpretation, making their application not ambiguous. Conclusions and perspectives An empirical foundation for the uncertainty factors in the pedigree matrix overcomes one main argument against their use, which in turn strengthens the whole pedigree approach for quantitative uncertainty assessment in ecoinvent. This paper provides an approach to obtain an empirical basis for the uncertainty factors, and it provides also empirically based uncertainty factors, for indicator scores in the pedigree matrix. Basic uncertainty factors are not provided, it is recommended to use the factors from ecoinvent 2 for the time being. In the developed procedure, using GSD as the uncertainty measure is essential to overcome scaling effects; it should therefore also be used if the analysed data do not follow a lognormal distribution. As a consequence, uncertainty factors obtained as GSD ratios need to be translated to range estimators relevant for these other distributions. Formulas for this step are provided in a separate paper (Muller et al.). The work presented in this paper could be the starting point for a much broader study to provide a better basis for input uncertainty in LCA, not only in ecoinvent.

In this paper, we review the emerging concept of digital twins (DTs) for urban water systems (UWS) based on the literature, stakeholder interviews and analyzing the current DT implementation process in the utility company VCS Denmark (VCS). Here, DTs for UWS are placed in the context of DTs at the component, unit process/operation or hydraulic structure, treatment plant, system, city, and societal levels. A UWS DT is characterized as a systematic virtual representation of the elements and dynamics of the physical system, organized in a star-structure with a set of features connected by data links that are based on standards for open data. This allows the overall functionality to be broken down into smaller, tangible units (features), enabling microservices that communicate via data links to emerge (the most central feature), facilitated by application programing interfaces (APIs). Coupled to the physical system, simulation models and advanced analytics are among the most important features. We propose distinguishing between living and prototyping DTs, where the term “living” refers to coupling observations from an ever-changing physical twin (which may change with, e.g., urban growth) with a simulation model, through a data link connecting the two. A living DT is thus a near real-time representation of an UWS and can be used for operational and control purposes. A prototyping DT represents a scenario for the system without direct coupling to real-time observations, which can be used for design or planning. By acknowledging that different DTs exist, it is possible to identify the value-creation from DTs achieved by different end-users inside and outside a utility organization. Analyzing the DT workflow in VCS shows that a DT must be multifunctional, updateable, and adjustable to support potential value creation across the utility company. This study helps clarify key DT terminology for UWS and identifies steps to create a DT by building upon digital ecosystems (DEs) and open standards for data.

Purpose The recycling of lithium-ion batteries is an emerging field faced with the challenge of recovering more than the most valuable elements from the batteries. While the literature presents many innovative approaches to the problem, an overview of the technical and environmental prospects of hydrometallurgical black mass recycling remains crucial. The goal was to analyze the impacts of a black mass process flowsheet and suggest ways to further reduce the impacts of battery recycling. Methods The flowsheet was drafted from the literature by combining both state-of-the-art and experimentally demonstrated unit processes by starting with the leaching system, where reductive leaching is performed using only the copper and iron impurities already present in the black mass. The process targeted copper, manganese, cobalt, nickel, and lithium recovery, and three scenarios for manganese recovery were investigated. The flowsheet was simulated using HSC Sim software, and the mass and energy balances were adapted into internally consistent life cycle inventories. The scope was “gate-to-gate” in Europe and CML methodology was used for impact assessment. Results and discussion Assuming that mechanical pre-treatment carries more environmental benefits than burdens, the results indicated that hydrometallurgical black mass recycling had a tentatively lower environmental footprint compared to virgin raw materials in all impact categories except ozone depletion, the results indicated that hydrometallurgical black mass recycling had a tentatively lower environmental footprint compared to virgin raw materials in all impact categories except ozone depletion. Sulfuric acid and neutralizing chemicals were among the most significant contributors to the impacts, and therefore further analysis was conducted based on an experimental study on low acid leaching with a low (< 0.5 M) initial sulfuric acid concentration instead of the baseline 2 M. This reduced the impacts by approximately 30–40% in all categories by decreasing downstream chemical consumption, and more significantly decreased ozone depletion. The challenges and opportunities for further process improvement were also considered. Conclusions The study highlights the importance of process optimization to improve the environmental sustainability of battery chemical production, but also revealed critical research gaps in the experimental literature. Rather than focusing on a single unit process, experimental black mass recycling research should aim at finding solutions that are optimal for the up- and downstream units, such as minimization of aluminum in the black mass and acid consumption.

Microplastics are classified as emerging pollutants of the aquatic environment, necessitating a comprehensive understanding of their properties for successful management and treatment. Wastewater treatment plants (WWTPs) serve as point sources of microplastic pollution of the aquatic and terrestrial (eco)systems. The first part of this review explores the basic definitions of microplastics, sources, types, physical and chemical methods of identifying and characterizing microplastics in WWTPs. The next part of the review details the occurrence of microplastics in various unit processes of WWTPs and sewage sludge. Followed by this, various methods for removing microplastics from wastewater are presented. Finally, the research gaps in this area were identified, and suggestions for future perspectives were provided.

Atmospheric corrosion can seriously affect the performance of steel structures over long periods of time; thus, it is essential to evaluate the rate of corrosion and subsequent modification of dynamic properties of a structure over different time periods. Standards and codes represent the general guidelines and suggest general protection techniques to prevent structures from corrosion damage. The available models in the literature propose the thickness reduction method that accounts for the exposure time of structures in corrosive environments. The purpose of this study is to review the existing corrosion models in the literature and report as well as compare their effectiveness in low (C2 level), medium (C3 level) and high (C4 level) corrosivity class in accordance with the ISO standard. Furthermore, the influence of corrosion loss during the lifetime of a structure is studied through a realistic case study model using FEM (finite element method) in both linear and nonlinear regions. The results showed that the corrosion can considerably affect the dynamic characteristics of the structure. For instance, the vibration period rose up to 15% for the C4 class and 100-year lifespan. Additionally, the corroded structure presented higher acceleration and drift demand, and the base reaction forces were reduced up to 60% for the same class and time period.