In the past several years, Materials Genome Initiative (MGI) efforts have produced myriad examples of computationally designed materials in the fields of energy storage, catalysis, thermoelectrics, and hydrogen storage as well as large data resources that are used to screen for potentially transformative compounds. The bottleneck in high-throughput materials design has thus shifted to materials synthesis, which motivates our development of a methodology to automatically compile materials synthesis parameters across tens of thousands of scholarly publications using natural language processing techniques. To demonstrate our framework’s capabilities, we examine the synthesis conditions for various metal oxides across more than 12 thousand manuscripts. We then apply machine learning methods to predict the critical parameters needed to synthesize titania nanotubes via hydrothermal methods and verify this result against known mechanisms. Finally, we demonstrate the capacity for transfer learning by using machine learning models to predict synthesis outcomes on materials systems not included in the training set and thereby outperform heuristic strategies.

Increased interest in high entropy alloys (HEA)s has led to significant activity in the development of new equimolar multicomponent metal systems. The present viewpoint article suggests applying a lens of practicality related to alloy economics and resource usage issues. A framework for HEA materials selection is presented to assist the metallurgical community as it searches for HEAs with feasible implementation possibilities, by identifying unsuitable alloying elements based on price or metrics of supply availability. For some metrics, such as price volatility, the elemental diversification in HEAs could prove beneficial, while for others, such as recyclability, elemental breadth introduces significant challenges.Download high-res image (369KB)Download full-size image

Increased use of secondary raw materials in metal production offers several benefits including reduced cost and lowered energy burden. The lower cost of secondary or scrap materials is accompanied by an increased uncertainty in elemental composition. This increased uncertainty for different scraps, if not managed well, results in an increased risk that the elemental concentrations in the final products fall outside customer specifications. Previous results show that incorporating this uncertainty explicitly into batch planning can modify the potential use of scrap materials while managing risk. Chance-constrained formulations provide one approach to uncertainty-aware batch planning; however, typical formulations assume normal distributions to represent the compositional uncertainty of the materials. Compositional variation in scrap materials has been shown to have a skewed distribution, and therefore, the performance of these models, in terms of their ability to provide effective planning, it may then be heavily influenced by the structure of the compositional data used. To address this issue, this work developed several approximations for skewed distributional forms within chance-constrained formulations. We explored a lognormal approximation based on Fenton’s method; a convex approximation based on Bernstein inequalities; and a linear approximation using fuzzy set theory. Each of these methods was formulated and case studies executed using compositional data from an aluminum remelter. Results indicate that the relationship between the underlying structure/distribution of the compositional data and how these distributions are formulated in batch planning can modify the use of secondary raw materials.

The challenges brought on by the increasing complexity of electronic products, and the criticality of the materials these devices contain, present an opportunity for maximizing the economic and societal benefits derived from recovery and recycling. Small appliances and computer devices (SACD), including mobile phones, contain significant amounts of precious metals including gold and platinum, the present value of which should serve as a key economic driver for many recycling decisions. However, a detailed analysis is required to estimate the economic value that is unrealized by incomplete recovery of these and other materials, and to ascertain how such value could be reinvested to improve recovery processes. We present a dynamic product flow analysis for SACD throughout Portugal, a European Union member, including annual data detailing product sales and industrial-scale preprocessing data for recovery of specific materials from devices. We employ preprocessing facility and metals pricing data to identify losses, and develop an economic framework around the value of recycling including uncertainty. We show that significant economic losses occur during preprocessing (over $70 M USD unrecovered in computers and mobile phones, 2006–2014) due to operations that fail to target high value materials, and characterize preprocessing operations according to material recovery and total costs.

Recent legislation has focused attention on the supply chains of tin, tungsten, tantalum, and gold (3TG), specifically those originating from the eastern part of the Democratic Republic of Congo. The unique properties of these so-called “conflict minerals” lead to their use in many products, ranging from medical devices to industrial cutting tools. This paper calculates per product use of 3TG in several information, communication, and technology (ICT) products such as desktops, servers, laptops, smart phones, and tablets. By scaling up individual product estimates to global shipment figures, this work estimates the influence of the ICT sector on 3TG mining in covered countries. The model estimates the upper bound of tin, tungsten, tantalum, and gold use within ICT products to be 2%, 0.1%, 15%, and 3% of the 2013 market share, respectively. This result is projected into the future (2018) based on the anticipated increase in ICT device production.

Recycling provides a key strategy to move toward a more sustainable society by partially mitigating the impact of fast-growing material consumption. One main barrier to increased recycling arises from the fact that in many real world contexts, the quality of secondary (or scrap) material is unknown and highly variable. Even if scrap material is of known quality, there may be finite space or limited operational flexibility to separate or sort these materials prior to use. These issues around identification and grouping given the operational constraints create limitations to simply developing an appropriate sorting strategy, let alone implementing one. This study suggests the use of data mining as a strategy to manage raw materials with uncertain quality using existing data from the recycling industry. A clustering analysis is used to recognize the pattern of raw materials across a broad compositional range in order to provide criteria for grouping (binning) raw materials. This strategy is applied to an industrial case of aluminum recycling to explore the benefits and limitations in terms of secondary material usage. In particular, the case investigated is around recycling industrial byproducts (termed dross for the case of the aluminum industry). The binning strategy obtained by the clustering analysis can significantly reduce material cost by increasing the compositional homogeneity and distinctiveness of uncertain raw materials. This result suggests the potential opportunity to increase low-quality secondary raw material usage before investment in expensive sorting technology.

As an alternative transportation fuel to petrodiesel, biodiesel has been promoted within national energy portfolio targets across the world. Early estimations of low lifecycle greenhouse gas (GHG) emissions of biodiesel were a driver behind extensive government support in the form of financial incentives for the industry. However, studies consistently report a high degree of uncertainty in these emissions estimates, raising questions concerning the carbon benefits of biodiesel. Furthermore, the implications of feedstock blending on GHG emissions uncertainty have not been explicitly addressed despite broad practice by the industry to meet fuel quality standards and to control costs. This work investigated the impact of feedstock blending on the characteristics of biodiesel by using a chance-constrained (CC) blend optimization method. The objective of the optimization is minimization of feedstock costs subject to fuel standards and emissions constraints. Results indicate that blending can be used to manage GHG emissions uncertainty characteristics of biodiesel, and to achieve cost reductions through feedstock diversification. Simulations suggest that emissions control policies that restrict the use of certain feedstocks based on their GHG estimates overlook blending practices and benefits, increasing the cost of biodiesel. In contrast, emissions control policies which recognize the multifeedstock nature of biodiesel provide producers with feedstock selection flexibility, enabling them to manage their blend portfolios cost effectively, potentially without compromising fuel quality or emissions reductions.

Life cycle assessment (LCA) is a technique used to assess the environmental impact of products, processes, or materials. Recently, its importance as a decision-making tool to help evaluate current inventories and innovation of environmentally responsible products has grown; however, the amount of information needed to completely assess even the simplest product’s environmental impact may require significant time and resources. Myriad quantitative and qualitative effort-reducing strategies have been considered to accelerate the pace and reduce the cost of LCA. Although these streamlining methodologies reduce the time and effort of conducting LCA, they introduce variability and uncertainty into the results, creating a challenge for stakeholders who may need to make decisions based on the information. This Article explores the impact of streamlining on the credibility of LCA results given the uncertainty in the context of several case studies related to materials production in common consumer products. A technique for the structured analysis of the bill of materials is proposed, which leverages statistical analysis in the context of uncertainty.

The increased use of secondary (i.e., recycled) and renewable resources will likely be key toward achieving sustainable materials use. Unfortunately, these strategies share a common barrier to economical implementation − increased quality variation compared to their primary and synthetic counterparts. Current deterministic process-planning models overestimate the economic impact of this increased variation. This paper shows that for a range of industries from biomaterials to inorganics, managing variation through a chance-constrained (CC) model enables increased use of such variable raw materials, or heterogeneous feedstocks (hF), over conventional, deterministic models. An abstract, analytical model and a quantitative model applied to an industrial case of aluminum recycling were used to explore the limits and benefits of the CC formulation. The results indicate that the CC solution can reduce cost and increase potential hF use across a broad range of production conditions through raw materials diversification. These benefits increase where the hFs exhibit mean quality performance close to that of the more homogeneous feedstocks (often the primary and synthetic materials) or have large quality variability. In terms of operational context, the relative performance grows as intolerance for batch error increases and as the opportunity to diversify the raw material portfolio increases.

•Econometric modeling to understand fundamental drivers behind copper scrap availability.•Industrial activity and world GDP correlate with total scrap supply.•Method described can complement Materials Flow Analysis (MFA).•Contribute to understanding the levers for policy aimed at improving recycling.The supply of recycled material depends on historic consumption, i.e. what constitutes scrap available today originates from previously made products. Analytical tools, such as materials flow analysis, use this observation to estimate scrap metal flows. The supply of recycled metal also depends on changing economic conditions, e.g. metal consumption rates correlate with changes in gross domestic product. We use an autoregressive distributed lag approach to model the supply of recycled copper as a complementary approach to material flow analysis. We find that both industrial activity and world GDP correlate with total scrap supply, with limited dependence on copper price. We also develop independent models for direct remelt (higher quality) and refined (lower quality) scrap. A 1% increase in industrial production leads to a 2.1% increase in higher quality scrap quantity, while a similar increase in world GDP leads to a 1.4% increase in lower quality scrap. Based on this model dependence, we suggest that a recycling policy aimed at increasing recycling through the use of subsidies, taxes or price incentives should be directed towards the low-end segment of the scrap market and there it may still only have limited impact.

What is the burden upon your feet? With sales of running and jogging shoes in the world averaging a nontrivial 25 billion shoes per year, or 34 million per day, the impact of the footwear industry represents a significant portion of the apparel sector's environmental burden. A single shoe can contain 65 discrete parts that require 360 processing steps for assembly. While brand name companies dictate product design and material specifications, the actual manufacturing of footwear is typically contracted to manufacturers based in emerging economies. Using life cycle assessment methodology in accordance with the ISO 14040/14044 standards, this effort quantifies the life cycle greenhouse gas emissions, often referred to as a carbon footprint, of a pair of running shoes. Furthermore, mitigation strategies are proposed focusing on high leverage aspects of the life cycle.Using this approach, it is estimated that the carbon footprint of a typical pair of running shoes made of synthetic materials is 14 ± 2.7 kg CO2-equivalent. The vast majority of this impact is incurred during the materials processing and manufacturing stages, which make up around 29% and 68% of the total impact, respectively. Other similar studies in the apparel industry have reported carbon footprints of running shoes ranging between 18 and 41 kg CO2-equivalent/pair (PUMA, 2008; Timberland, 2009).For consumer products not requiring electricity during use, the intensity of emissions in the manufacturing phase is atypical; most commonly, materials make up the biggest percentage of impact. This distinction highlights the importance of identifying mitigation strategies within the manufacturing process, and the need to evaluate the emissions reduction efficacy of each potential strategy. By suggesting a few of the causes of manufacturing dominance in the global warming potential assessment of this product, this study hypothesizes the characteristics of a product that could lead to high manufacturing impact. Some of these characteristics include the source of energy in manufacturing and the form of manufacturing, in other words the complexity of processes used and the area over which these process are performed (particularly when a product involves numerous parts and light materials). Thereby, the work provides an example when relying solely on the bill of materials information for product greenhouse gas emissions assessment may underestimate life cycle burden and ignore potentially high impact mitigation strategies.Highlights► The GHG impact of running shoes is quantified, accounting for uncertainty. ► Drivers of impact are analyzed to create mitigation strategies. ► The qualities of products with manufacturing-dominated impacts are explored.