End-of-life Products Research Articles

Developing a robust indicator to evaluate circular economy through reuse strategy: A case study of using water treatment sludge as a coagulant for dewatering of iron ore tailings slurry

Due to the lack of tools, which are simple enough to assess the reuse strategy in the concept of circular economy (CE), in the present paper, reuse efficiency index (REI) was introduced as a new indicator, inspired by previously developed indexes, namely circular economy index (CEI) and value-based resource efficiency (VRE). The REI, as a measure of economic efficiency of reuse strategy, is the ratio of the net value obtained from the reuse of end of life (EOL) product instead of using a new product over the value of materials and energy inputs required to reproduce the EOL product. To calculate REI for any specific EOL product, information regarding mass, physicochemical properties and operational data on the consumption of energy, which are mainly available in the financial archives of companies, is crucial and the market price of EOL product must also be predicted. In another part of the study, the reuse of ferric chloride-based water treatment sludge (FCS) as a coagulant, instead of using high cost polymeric coagulant, for iron ore tailings slurry dewatering was evaluated through REI. The results showed that depending on the market prices of energy and materials as well as the iron (FeCl3) content in the FCS, the REI could vary between 0.51 and 1.04, corresponding to 51–104% added value. Overall, REI is based on accessible information and its simplicity and comprehensibility makes it applicable for managers and policymakers to invest and create technologies for reaching closed-loop cycles in the concept of CE through reuse action.

Mixed integer programming and multi-objective enhanced differential evolution algorithm for human–robot responsive collaborative disassembly in remanufacturing system

The recycling of waste products is essential for resource reuse. However, turning operation direction causes significant fatigue to operators handling end-of-life (EoL) products, consequently degrading the recycling efficiency. Accordingly, this study employs responsive collaboration robots to aid operators in turning the operation direction of disassembled products. To solve the human-robot responsive collaboration disassembly line balancing problem (HRRC-DLBP), a mixed integer programming (MIP) model is constructed, and a decoding mechanism is designed in this study. Additionally, a multi-objective enhanced differential evolution algorithm (MEDE) in which the decoding mechanism is incorporated is devised and applied to solve the HRRC-DLBP. The MEDE algorithm is validated by comparing its solution results with those of the MIP model. Finally, the MEDE is used to optimise the EoL printer case for the HRRC-DLBP and the disassembly line balancing problem in which the operation direction is turned by humans (H-DLBP). The optimisation results show that the recycling of EoL products is more efficient using the HRRC-DLBP than employing the H-DLBP.

Effective reduction of pathogenic Escherichia coli and long-term immunity in pigs through early parenteral vaccination

ABSTRACT The present study aimed to examine the ability of a parenteral vaccine of direct application in suckling pigs to generate adaptive immunity against pathogenic Escherichia coli under field conditions as well as the long-term immune response at the end of productive life and the influence on the prevalence of pathogens involved in the Porcine Enteric Disease Complex (PEDC). Pigs were divided into vaccinated (n = 1,893) and non-vaccinated (control, n = 1,869) groups. We collected fecal swabs from nursery pigs presenting diarrhea and intestinal tissue at slaughter to assess E. coli virulence factors genes, PEDC pathogens, histology, IgA-producing cells, and cytokines levels. Fecal swabs: vaccinated group 0% pathogenic E. coli vs. 94.1% in control (p < 0.0001). Intestinal samples: Vaccinated group 35% positivity for E. coli vs. 85% in control (p = 0.001). PEDC pathogens: Only Lawsonia intracellularis was identified (40% of ileum and 20% of colon samples in vaccinated vs. 100% of ileum and 70% of colon in the control). Vaccinated had more intestine cellular infiltrate (p < 0.028), IgA-producing cells (p < 0.01), and inflammatory cytokines. The parenteral vaccine against E. coli for suckling pigs may be a strategy to control E. coli infection throughout pigs’ lives and our results suggest a possible interaction with L. intracellularis.

Identifying and modelling the barriers to returns of end-of-life (EOL) heavy-duty machinery in Nigeria: A consumers’ perspective

EOL product returns has become more prevalently practiced in companies in developed nations as a response to government's strict regulations and customers' expectations. Correspondingly, there is a growing body of theoretical literature on the topic within this context as a crucial strategy to accelerate the development of the circular economy (CE). However, consumers' participation in EOL returns of heavy-duty machinery is immature in developing nations due to barriers, and studies are scarce on the analysis of the barriers. To fill this gap, this study aims to identify and analyze the barriers that hinder consumers' participation in EOL returns of heavy-duty machinery within a developing nation-Nigeria. Barriers were identified from published literature and experts' inputs and then, classified under technological, organizational, and environmental (supply chain) categories. Then, analysis was done via a methodology integrating interval-valued neutrosophic analytical hierarchy process (IVN-AHP) with multi-objective optimization on the basis of a ratio analysis plus the full multiplicative form (MULTIMOORA). Findings indicate that the environmental(supply chain) barriers rank the highest, followed by technological barriers. Specifically, consumers participation in EOL returns of heavy-duty machinery in Nigeria is mostly hindered by lack of regulations, lack of financial incentives, lack of skills, undeveloped reverse logistics system and poor access to facilities for recovery operations. Furthermore, we proposed some strategies to support stakeholders to mitigate the barriers. Thus, this study gives valuable guidelines to decision-makers to enable the transition to a CE paradigm in the heavy-duty sector within developing nations for utmost sustainability.

Hybrid closed-loop supply chain with different collection competition in reverse channel

In the hybrid closed-loop supply chain (CLSC) with diverse collectors, customers increasingly prioritize the convenience in returning end-of-life (EOL) products. This trend necessitates supply chain entities to enhance their collection service to address the intensifying competition for EOL products collection. To devise appropriate convenience and pricing strategies, we construct a hybrid game-theoretic CLSC model encompassing a manufacturer, a retailer, and a third-party collector, and we propose new collection functions that cater to varying levels of collection competition, spanning monopoly, duopoly, and hybrid competitive scenarios. Furthermore, we delve into the influence of customers' preferences or distrust towards a specific collector on the collection competition, which is caused by factors such as more convenient offline stores or unqualified recycling process. Our study first reveals that collection competition is affected by all collectors' convenience strategies, while the total collection rate is dominated by the most preferred collectors' convenience strategy. Second, both customer's preference and distrust negatively affect pricing and convenience strategies, albeit with differing effects on collection rates across various competition levels. Notably, channel distrust exerts a more substantial influence on manufacturers' and third-party collectors' strategies. Third, we also examine how competitive collection and customer behavior shape the proportion of collected products destined for different recovery options, i.e., remanufacturing and recycling, as well as the overall efficiency of recovering collected products across the entire supply chain. And channel preference plays a more positive role in enhancing the overall collection rate for the supply chain.

Resilient and sustainable semiconductor supply chain network design under trade credit and uncertainty of supply and demand

Supply Chain (SC) performance appraisal extends beyond economic evaluation to encompass environmental and social impacts, as well as resilience to supply and demand disruptions. Thus, SC network design decisions should simultaneously optimize all these performance metrics as a primary objective. However, existing studies optimized these objectives independently or in a limited scope, failing to comprehensively address the interconnected nature of these performance metrics. Therefore, this study proposes a mixed integer linear programming (MILP) model with four objectives to address a multi-tier resilient and sustainable semiconductor SC network design problem under trade credit, supply, and demand uncertainties. The model aims to determine the optimal number and location of various facilities, the amount to order from suppliers, economic production quantities, quantity allocation between SC entities, and ideal supplier selections. The proposed supply chain network contributes to the global effort to reduce carbon emissions and enhance the circular economy by considering carbon trading, recycling, and the proper disposal of end-of life products. Uncertainties in input parameters are addressed using a fuzzy programming approach, while lexicographic optimization and the ɛ-constraint method are used to solve the proposed model. This paper presents a numerical example to illustrate the applicability of the proposed model and provide managerial insights. The numerical analysis results show that provided an appropriate confidence level for the uncertain parameters, the sets of Pareto optimal solutions can be generated. And it also shows that the total SC cost increases with decreasing carbon emissions level and increasing job opportunities created. This research advances sustainable and resilient supply chain management practices while offering practical guidance for decision-makers in real-world contexts.

An ontology and rule-based method for human–robot collaborative disassembly planning in smart remanufacturing

Disassembly is a decisive step in the remanufacturing process of End-of-Life (EoL) products. As an emerging semi-automatic disassembly paradigm, human–robot collaborative disassembly (HRCD) offers multiple disassembly methods to enhance flexibility and efficiency. However, HRCD increases the complexity of planning and determining the optimal disassembly sequence and scheme. Currently, the optimisation process of heuristic methods is difficult to interpret, and the results cannot be guaranteed as globally optimal. Consequently, this paper introduces a general ontology model for HRCD, along with a rule-based reasoning method, to automatically generate the optimal disassembly sequence and scheme. Firstly, the HRCD ontology model establishes the disassembly-related information for EoL products in a standardised approach. Then, customised disassembly-related rules are proposed to regulate the precedence constraints and optional disassembly methods for each disassembly task of EoL products. The optimal disassembly sequence and scheme are automatically generated by combining supportive rules with the ontology model. Lastly, the human–robot collaborative disassembly planning of a gearbox is presented as a case study to validate the feasibility of the proposed methods. Our method generates an optimal disassembly scheme compared with other heuristic algorithms, achieving the shortest process time of 308 units and the fewest number of disassembly direction change of 3 times. Additionally, the reasoning procedure can be easily tracked and modified. The proposed method is both universal and easily reproducible, allowing it to be extended to support the entire remanufacturing process.

Cumulative energy demand analysis in the current manufacturing and end-of-life strategies for a polymeric composite at different fibre-matrix combinations

Fibre Reinforced Polymers (FRPs) are finding more applications in different industrial sectors. From a sustainability point of view, a component made of FRPs reduces energy consumption and CO2 emissions during its use-phase due to the material's lightweight nature. However, the production of these materials impacts the global energy demand significantly. To mitigate this impact, circular economy strategies are essential. This study focuses on a Cumulative Energy Demand (CED) analysis for different End-of-Life (EoL) strategies of FRPs components. Three EoL routes were evaluated: i.e., combustion, recycling and reforming of continuous fibres reinforced thermoplastics. Different fibres and matrices and three Fibre Volume Fractions (FVF) were taken into account. Specifically, Glass Fibres, Carbon Fibres, Polypropylene, and Polyether ether ketone were examined while FVF of 11%, 23% and 45% were evaluated. A Life Cycle Inventory data was built combining literature review and CES Edupack database. The results provided some guidelines for optimising the product's EoL phase in terms of CED reduction underlining the advantages and high competitiveness of the reforming strategy especially if high-performance matrices and/or fibres are processed. Recycling results to be a valuable EoL alternative if FRPs made by high-performance fibres and high FVF are employed while combustion is the more advisable option if low-performance matrices and fibres are used.

Integrated multi-product reverse supply chain design and disassembly line balancing under uncertainty

End-of-life (EOL) product recycling has received increasing attention because of potential environmental, social and economic benefits. A well-designed reverse supply chain (RSC) can efficiently handle EOL products. As the critical activity in the RSC, the disassembly process decomposes collected EOL products into components to fulfill the demands of remanufacturing plants. Efficiently coordinating RSC design and disassembly line balancing decisions may improve the whole system performance significantly, especially when facing multiple EOL products and uncertainty. This paper investigates a novel integrated RSC design and disassembly line balancing problem to handle multiple EOL products where the supply of EOL products, the demand for components, and task times in disassembly are stochastic. This complex problem needs to jointly determine the number and locations of disassembly plants, disassembly equipment procurement, disassembly line balancing, and inventory levels of both EOL products collected and components dismantled. The objectives are to maximize the expected profit and minimize the carbon emissions simultaneously. For the problem, a bi-objective two-stage stochastic programming model is formulated and an exact ɛ-constrained method is proposed, transforming the bi-objective problem into a series of single-objective problems. Especially, an improved Benders decomposition approach is developed to solve each single-objective problem efficiently. Numerical experiments comprising an illustrative case and 200 random instances are conducted to evaluate the performance of proposed methods. Moreover, some managerial insights are drawn.

Comparative Life-Cycle Assessment of Electricity-Generation Technologies: West Texas Case Study

This comparison of five power plants in West Texas is intended to provide various decision-makers and stakeholders with a holistic picture of the life-cycle environmental impacts associated with these power plants. A key contribution of this analysis is that we assumed all power plants generate the same amount of electricity over a 30-year life, taking a 500 MW combined-cycle gas turbine (CCGT) plant as a benchmark. Also, in two cases, we added battery storage to wind and solar PV facilities to render them nearly as dispatchable as the CCGT. We included the entire supply chain supporting electricity generation, which encompassed raw material sourcing, processing, manufacturing, operations, and product end of life, also called “cradle to grave”. We report on 18 environmental impacts using ReCiPe midpoint (H) impact assessment. The supply chains are global, and impacts are felt differently by host communities across the world. The results can help stakeholders identify hotspots across numerous supply chains with the highest environmental impacts. We discuss some remedial measures and challenges to inform future analysis by the research community.

Linear Disassembly Line Balancing Problem with Tool Deterioration and Solution by Discrete Migratory Bird Optimizer

In recent years, the global resource shortage has become a serious issue. Recycling end-of-life (EOL) products is conducive to resource reuse and circular economy and can mitigate the resource shortage issue. The disassembly of EOL products is the first step for resource reuse. Disassembly activities need tools, and tool deterioration occurs inevitably during the disassembly process. This work studies the influence of tool deterioration on disassembly efficiency. A disassembly line balancing model with the goal of maximizing disassembly profits is established, in which tool selection and assignment is a critical part. A modified discrete migratory bird optimizer is proposed to solve optimization problems. The well-known IBM CPLEX optimizer is used to verify the correctness of the model. Six real-world products are used for disassembly experiments. The popular fruit fly optimization algorithm, whale optimization algorithm and salp swarm algorithm are used for search performance comparison. The results show that the discrete migratory bird optimizer outperforms all three other algorithms in all disassembly instances.

Chemical composition and sensory analysis in hamburgers made from laying quails at the end of productive life

Background: The aim of this study was to evaluate chemical composition and sensory analysis in hamburgers prepared with meat from laying quaiTls at disposal age. Main Body: The design was completely randomized, with four treatments of quail hamburger, represented by the inclusion of bacon in its preparation, as follows: quail meat without the inclusion of bacon; 5% inclusion of bacon, 10% inclusion of bacon and 15% inclusion, and three replicates. The quails were cleaned, ground and seasoned for each product. There was a significant difference in sensory acceptance for aroma and flavor characteristics. In all variables, acceptance increased following the level of bacon inclusion. In the purchase intent test, hamburgers with 10 and 15% inclusion of bacon were the most accepted. For the chemical analysis, there was a difference for the variables, lipids, protein, moisture, ash and caloric value. The products were accepted by the consumer, highlighting the highest levels of bacon inclusion.

Pricing, greening, and recycling decisions of capital-constrained closed-loop supply chain with government subsidies under financing strategies

BackgroundDue to the growing awareness of environmental protection and their economic benefits, there has been rapid development in the closed-loop supply chain (CLSC) in recent years, with a general occurrence of CLSCs led by collectors. MotivationEven with government support such as subsidies, the operation of CLSC still requires immense investment, such as purchasing recycling equipment. Nevertheless, the unprecedented economic recession during the post-pandemic era has heightened the risk of insufficient funding for CLSC. This highlights the importance of understanding the complexities involved in CLSC financing strategies, as they are crucial for ensuring the sustainability and resilience of CLSC in the face of challenges. MethodsTo investigate the equilibrium decisions and financing strategy selection of CLSCs, we developed and analyzed a series of three-stage Stackelberg models, considering practical scenarios involving capital constraints, environmentally friendly green activities, government subsidies for End-of-Life (EOL) recycling, as well as introducing common trade credit financing and bank financing for generality. ConclusionThe findings demonstrate: (1) Under trade credit financing, it is always beneficial for the collector granting credit. Interest income can motivate the collector to recover more EOL products. (2) In addition to financing interest rates, government subsidies and remanufacturing cost savings can affect the financing strategy selection of the capital-constrained manufacturer. Our study provides guidance for managers in selecting optimal financing strategies under specific conditions. (3) The total profit of CLSC under trade credit financing is always higher and we propose a revenue-sharing contract to realize the win-win status with the optimal profit of CLSC. (4) Furthermore, our results underscore the importance of government recycling subsidies, remanufacturing cost savings, and the positive impact achieved by reducing recycling difficulties and enhancing customer awareness of green consumption.

Concept for a data-based approach to support decision-making in tactical tasks for planning disassembly systems

Emissions-driven climate change and increasing amounts of waste highlight the need to transition to a circular economy characterized by closed-loop material flows. Of central importance is the expansion of the end-of-life (EOL) product disassembly, where companies must be supported in the selection of parts and the design of the disassembly system. A critical aspect is the relationship between the disassembly depth – the extent to which an EOL product is disassembled – and the design of disassembly systems. While an increased disassembly depth can improve product value and circularity, it also affects the needed disassembly process, potentially requiring adjustments to the disassembly system design. Due to this interaction and the flexible choice of the disassembly depth per product, numerous possible disassembly scenarios with different profits must be evaluated and compared. This contribution proposes an approach to support disassembly planning by enabling the data-based comparison of these scenarios. Using simulation and process mining, this approach generates comprehensive process data and facilitates the comparison of a multitude of combinations. The development of the proposed approach is described in detail, and future research opportunities are identified, such as the Design of Experiments needed to efficiently simulate various scenarios.

A New Pareto Discrete NSGAII Algorithm for Disassembly Line Balance Problem

With the increasing variety and quantity of end-of-life (EOL) products, the traditional disassembly process has become inefficient. In response to this phenomenon, this article proposes a random multiproduct U-shaped mixed-flow incomplete disassembly line balancing problem (MUPDLBP). MUPDLBP introduces a mixed disassembly method for multiple products and incomplete disassembly method into the traditional DLBP, while considering the characteristics of U-shaped disassembly lines and the uncertainty of the disassembly process. First, mixed-flow disassembly can improve the efficiency of disassembly lines, reducing factory construction and maintenance costs. Second, by utilizing the characteristics of incomplete disassembly to reduce the number of dismantled components and the flexibility and efficiency of U-shaped disassembly lines in allocating disassembly tasks, further improvement in disassembly efficiency can be achieved. In addition, this paper also addresses the characteristics of EOL products with heavy weight and high rigidity. While retaining the basic settings of MUPDLBP, the stability of the assembly during the disassembly process is considered, and a new problem called MUPDLBP_S, which takes into account the disassembly stability, is further proposed. The corresponding mathematical model is provided. To obtain high-quality disassembly plans, a new and improved algorithm called INSGAII is proposed. The INSGAII algorithm uses the initialization method based on Monte Carlo tree simulation (MCTI) and the Group Global Crowd Degree Comparison (GCDC) operator to replace the initialization method and crowding distance comparison operator in the NSGAII algorithm, effectively improving the coverage of the initial population individuals in the entire solution space and the evenness and spread of the Pareto front. Finally, INSGAII’s effectiveness has been affirmed by tackling both current disassembly line balancing problems and the proposed MUPDLBP and MUPDLBP_S. Importantly, INSGAII outshines six comparison algorithms with a top rank of 1 in the Friedman test, highlighting its superior performance.

A Self-Adaptive Learning Approach for Uncertain Disassembly Planning Based on Extended Petri Net

Disassembly is the first phase to demanufacture end-of-life (EOL) products that are separated into parts/components for recovery. The quality conditions of EOL products are highly uncertain, which would result in some uncertain information during the disassembly process, e.g., the disassembly time and recovering revenue of each subassembly. It is quite challenging to determine the optimal/near-optimal disassembly solutions under uncertain information. This paper studies uncertain disassembly planning (UDP) and proposes a self-adaptive learning approach to quickly identify the near-optimal disassembly solutions. First, we model the UDP by extending Petri nets, where not only disassembly operations but also EOL options of each subassembly are represented in the extended Petri Net. Based on the UDP model, we develop the self-adaptive learning approach, which integrates an approximation procedure for estimating uncertain disassembly information, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -learning algorithm for training disassembly samples, and a heuristic method for selecting the best disassembly solution. Finally, a hybrid Li-ion battery pack of Audi A3 Sportback e-tron is selected as the case study and applied to test the proposed self-adaptive learning approach. The experimental results demonstrate that our proposed method can efficiently find a better disassembly solution than the existing disassembly solution within 200 trainings in the case study.

A context-aware real-time human-robot collaborating reinforcement learning-based disassembly planning model under uncertainty

ABSTRACT Herein, we present a real-time multi-agent deep reinforcement learning model as a disassembly planning framework for human–robot collaboration. This disassembly plan optimises sequences to minimise operation time and the disassembling costs of end-of-life (EoL) products. Combining different data-driven decision-making tools, the plan aims to handle the complexities and uncertainties of disassembly tasks. Based on the physical features and geometric limitations of EoL product components, we calculate product disassembly difficulty scores. Subsequently, the deep reinforcement learning model integrates these scores into planning process. The model allocates tasks in real time according to the online conditions of the human operator, cobot, and product, enabling the model to cope with uncertainties that may change the process routine. We also present different scenarios wherein a cobot collaborates with human operators with different skill levels. To evaluate model performance, we compare it with baseline models in terms of the convergence time and incorporated disassembly features. The analysis indicates that our model converges three times faster than a baseline model applied to the same case study. Moreover, our model includes more features of the disassembly problem in its decision-making process than any other baseline model.

Multi-objective multi-fidelity optimisation for position-constrained human-robot collaborative disassembly planning

ABSTRACT Human-robot collaborative disassembly lines are widely used by remanufacturing companies to disassemble end-of-life (EOL) products. When disassembling large-sized EOL products, each workstation on a disassembly line is generally divided into multiple operating positions, so that different operators can disassemble the same product at their respective positions at the same time, thereby greatly improving efficiency. This paper focuses on a position-constrained human-robot collaborative disassembly planning (PC-HRCDP) problem for the above-mentioned lines, including three subproblems of disassembly sequence planning, disassembly line balancing and robot path planning. A multi-objective mixed integer programming model for PC-HRCDP is developed to solve small-scale instances. Furthermore, a multi-objective multi-fidelity optimisation (MO-MFO) algorithm is proposed to solve large-scale instances. Comprehensive experiments are conducted based on 10 problem instances generated in this study. Experimental results show that the proposed MO-MFO is better than a high-fidelity optimisation algorithm in terms of running time. In addition, benefiting from the strategy of MO-MFO to allocate the limited high-fidelity computational budget to solutions in the two stages of multi-objective optimisation and optimal sampling, MO-MFO is significantly better than the existing representative multi-fidelity optimisation algorithms in terms of the hyper-volume and the inverted generational distance.

Constraint programming for multi-line parallel partial disassembly line balancing problem with optional common stations

Due to the increased demand for efficient recycling systems for end-of-life (EOL) products, the role of disassembly lines in reverse supply chains has become crucial. Parallel disassembly lines can handle multi-type EOL products and consist of two or more lines. However, previous research has primarily focused on two-line disassembly systems and has not fully addressed the optionality of common stations. To address this gap, this study proposes three exact methods for optimizing multi-line parallel disassembly systems with optional common stations, partial disassembly mode, and AND/OR precedence relations. Firstly, a mixed-integer linear programming (MILP) model is formulated that optimizes three objectives: weighted line length, additional profits, and hazard evaluation. Secondly, two constraint programming (CP) models are developed with different solution methodologies to provide more extensive applications and efficient solutions. An illustrative example shows that production mode can significantly reduce line length and workstations, and computational results demonstrate that both CP methods outperform the MILP model in terms of solution quality and computational efficiency. Specifically, the CP-I method demonstrates a higher level of stability and efficiency in most instances, while the CP-II method excels in optimizing line length and station utilization. These results illustrate the potential for optimizing multi-line disassembly systems with optional common stations to enhance production flexibility in remanufacturing processes.

Vanadium: A U.S. Perspective on an Understudied Metal.

Vanadium is an element that is little known except to those who manufacture high-performance iron alloys and other widely used metal products that are indispensable for creating improved product performance across a variety of final-use sectors. We report here on deriving a detailed material flow cycle for vanadium in the United States for 1992-2021, the most recent year for which detailed data are available. The steels [tool steel, alloy steels, and high-strength low-alloy (HSLA) steels] are responsible for about half of the cumulative vanadium demand (167 Gg), with significantly smaller fractions being used to create catalysts, titanium-vanadium alloys, and several smaller product groups. These products flow to five end-use sectors, transport (61 Gg) and industrial machinery (62 Gg) being the largest. At end of product life, the vanadium-containing tool steels and catalysts are largely recycled, while most of the vanadium in carbon steels, alloy steels, HSLA steels, and other vanadium use sectors is functionally lost.