Material Flow Analysis and Occupational Exposure Assessment in Additive Manufacturing End-of-Life Material Management.

Additive manufacturing (AM) offers a variety of material manufacturing techniques for a wide range of applications across many industries. Most efforts at process optimization and exposure assessment for AM are centered around the manufacturing process. However, identifying the material allocation and potentially harmful exposures in end-of-life (EoL) management is equally crucial to mitigating environmental releases and occupational health impacts within the AM supply chain. This research tracks the allocation and potential releases of AM EoL materials within the US through a material flow analysis. Of the generated AM EoL materials, 58% are incinerated, 33% are landfilled, and 9% are recycled by weight. The generated data set was then used to examine the theoretical occupational hazards during AM EoL material management practices through generic exposure scenario assessment, highlighting the importance of ventilation and personal protective equipment at all stages of AM material management. This research identifies pollution sources, offering policymakers and stakeholders insights to shape pollution prevention and worker safety strategies within the US AM EoL management pathways.

Multi-scale techno-economic assessment of nitrogen recovery systems for livestock operations.

Intensive livestock farming generates vast amounts of organic materials, which are an important source of nitrogen releases. These anthropogenic nitrogen releases contribute to multiple environmental problems, including eutrophication of water systems, contamination of drinking water sources, and greenhouse gas emissions. Nitrogen recovery and recycling are technically feasible, and there exists a number of processes for nitrogen recovery from livestock material in the form of different products. In this work, a multi-scale techno-economic assessment of techniques for nitrogen recovery and recycling is performed. The assessment includes a material flow analysis of each process, from material collection to final treatment, to determine nitrogen recovery efficiency, losses, and recovery cost, as well as an environmental cost-benefit analysis to compare the nitrogen recovery cost versus the economic losses derived from its uncontrolled release into the environment. The results show that transmembrane chemisorption process results in the lowest recovery cost, 3.4-10.4 USD per kilogram of nitrogen recovered in the range of studied processing scales. The recovery of nitrogen from livestock material through three technologies, i.e., transmembrane chemisorption, MAPHEX, and stripping in packed bed, reveales to be cost-effective. Since the economic losses due to the harmful effects of nitrogen into the environment are estimated at 32-35 USD per kilogram of nitrogen released, nitrogen recycling is an environmentally and economically beneficial approach to reduce nutrient pollution caused by livestock operations.

Tracking end-of-life stage of chemicals: A scalable data-centric and chemical-centric approach.

Chemical flow analysis (CFA) can be used for collecting life-cycle inventory (LCI), estimating environmental releases, and identifying potential exposure scenarios for chemicals of concern at the end-of-life (EoL) stage. Nonetheless, the demand for comprehensive data and the epistemic uncertainties about the pathway taken by the chemical flows make CFA, LCI, and exposure assessment time-consuming and challenging tasks. Due to the continuous growth of computer power and the appearance of more robust algorithms, data-driven modelling represents an attractive tool for streamlining these tasks. However, a data ingestion pipeline is required for the deployment of serving data-driven models in the real world. Hence, this work moves forward by contributing a chemical-centric and data-centric approach to extract, transform, and load comprehensive data for CFA at the EoL, integrating cross-year and country data and its provenance as part of the data lifecycle. The framework is scalable and adaptable to production-level machine learning operations. The framework can supply data at an annual rate, making it possible to deal with changes in the statistical distributions of model predictors like transferred amount and target variables (e.g., EoL activity identification) to avoid potential data-driven model performance decay over time. For instance, it can detect that recycling transfers of 643 chemicals over the reporting years (1988 to 2020) are 29.87%, 17.79%, and 20.56% for Canada, Australia, and the U.S. Finally, the developed approach enables research advancements on data-driven modelling to easily connect with other data sources for economic information on industry sectors, the economic value of chemicals, and the environmental regulatory implications that may affect the occurrence of an EoL transfer class or activity like recycling of a chemical over years and countries. Finally, stakeholders gain more context about environmental regulation stringency and economic affairs that could affect environmental decision-making and EoL chemical exposure predictions.

A generic scenario analysis of end-of-life plastic management: Chemical additives.

Plastic growing demand and the increment in global plastics production have raised the number of spent plastics, out of which over 90% are either landfilled or incinerated. Both methods for handling spent plastics are susceptible to releasing toxic substances, damaging air, water, soil, organisms, and public health. Improvements to the existing infrastructure for plastics management are needed to limit chemical additive release and exposure resulting from the end-of-life (EoL) stage. This article analyzes the current plastic waste management infrastructure and identifies chemical additive releases through a material flow analysis. Additionally, we performed a facility-level generic scenario analysis of the current U.S. EoL stage of plastic additives to track and estimate their potential migration, releases, and occupational exposure. Potential scenarios were analyzed through sensitivity analysis to examine the merit of increasing recycling rates, using chemical recycling, and implementing additive extraction post-recycling. Our analyses identified that the current state of plastic EoL management possesses high mass flow intensity toward incineration and landfilling. Although maximizing the plastic recycling rate is a reasonably straightforward goal for enhancing material circularity, the conventional mechanical recycling method requires improvement because major chemical additive release and contamination routes act as obstacles to achieving high-quality plastics for future reuse and should be mitigated through chemical recycling and additive extraction. The potential hazards and risks identified in this research create an opportunity to design a safer closed-loop plastic recycling infrastructure to handle additives strategically and support sustainable materials management efforts to transform the US plastic economy from linear to circular.

Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models.

Analyzing chemicals and their effects on the environment from a life cycle viewpoint can produce a thorough analysis that takes end-of-life (EoL) activities into account. Chemical risk assessment, predicting environmental discharges, and finding EoL paths and exposure scenarios all depend on chemical flow data availability. However, it is challenging to gain access to such data and systematically determine EoL activities and potential chemical exposure scenarios. As a result, this work creates quantitative structure-transfer relationship (QSTR) models for aiding environmental managment decision-making based on chemical structure-based machine learning (ML) models to predict potential industrial EoL activities, chemical flow allocation, environmental releases, and exposure routes. Further multi-label classification methods may improve the predictability of QSTR models according to the ML experiment tracking. The developed QSTR models will assist stakeholders in predicting and comprehending potential EoL management activities and recycling loops, enabling environmental decision-making and EoL exposure assessment for new or existing chemicals in the global marketplace.

A graph-based modeling framework for tracing hydrological pollutant transport in surface waters.

Anthropogenic pollution of hydrological systems affects diverse communities and ecosystems around the world. Data analytics and modeling tools play a key role in fighting this challenge, as they can help identify key sources as well as trace transport and quantify impact within complex hydrological systems. Several tools exist for simulating and tracing pollutant transport throughout surface waters using detailed physical models; these tools are powerful, but can be computationally intensive, require significant amounts of data to be developed, and require expert knowledge for their use (ultimately limiting application scope). In this work, we present a graph modeling framework - which we call HydroGraphs - for understanding pollutant transport and fate across waterbodies, rivers, and watersheds. This framework uses a simplified representation of hydrological systems that can be constructed based purely on open-source data (National Hydrography Dataset and Watershed Boundary Dataset). The graph representation provides a flexible intuitive approach for capturing connectivity and for identifying upstream pollutant sources and for tracing downstream impacts within small and large hydrological systems. Moreover, the graph representation can facilitate the use of advanced algorithms and tools of graph theory, topology, optimization, and machine learning to aid data analytics and decision-making. We demonstrate the capabilities of our framework by using case studies in the State of Wisconsin; here, we aim to identify upstream nutrient pollutant sources that arise from agricultural practices and trace downstream impacts to waterbodies, rivers, and streams. Our tool ultimately seeks to help stakeholders design effective pollution prevention/mitigation practices and evaluate how surface waters respond to such practices.

ADAM: A web platform for graph-based modeling and optimization of supply chains.

Modeling and optimization are essential tasks that arise in the analysis and design of supply chains (SCs). SC models are essential for understanding emergent behavior such as transactions between participants, inherent value of products exchanged, as well as impact of externalities (e.g., policy and climate) and of constraints. Unfortunately, most users of SC models have limited expertise in mathematical optimization, and this hinders the adoption of advanced decision-making tools. In this work, we present ADAM, a web platform that enables the modeling and optimization of SCs. ADAM facilitates modeling by leveraging intuitive and compact graph-based abstractions that allow the user to express dependencies between locations, products, and participants. ADAM model objects serve as repositories of experimental, technology, and socio-economic data; moreover, the graph abstractions facilitate the organization and exchange of models and provides a natural framework for education and outreach. Here, we discuss the graph abstractions and software design principles behind ADAM, its key functional features and workflows, and application examples.

Analysis of incentive policies for phosphorus recovery at livestock facilities in the Great Lakes area.

Livestock operations have been highly intensified over the last decades, resulting in the advent of large concentrated animal feeding operations (CAFOs). Intensification decreases production costs but also leads to substantial environmental impacts. Specifically, nutrient runoff from livestock waste results in eutrophication, harmful algal blooms, and hypoxia. The implementation of nutrient recovery systems in CAFOs can abate nutrient releases and negative ecosystem responses, although they might negatively affect the economic performance of CAFOs. We design and analyze potential incentive policies for the deployment of phosphorus recovery technologies at CAFOs considering the geospatial vulnerability to nutrient pollution. The case study demonstration consists of 2217 CAFOs in the U.S. Great Lakes area. The results reveal that phosphorus recovery is more economically viable in the largest CAFOs due to economies of scale, although they also represent the largest eutrophication threats. For small and medium-scale CAFOs, phosphorus credits progressively improve the profitability of nutrient management systems. The integration of biogas production does not improve the economic performance of phosphorus recovery systems at most of CAFOs, as they lack enough size to be cost-effective. Phosphorus recovery proves to be economically beneficial by comparing the net costs of nutrient management systems with the negative economic impact derived from phosphorus releases. The incentives necessary for avoiding up to 20.7×103 ton/year phosphorus releases and achieve economic neutrality in the Great Lakes area are estimated at $223 million/year. Additionally, the fair distribution of limited incentives is studied using a Nash allocation scheme, determining the break-even point for allocating monetary resources.

Anaerobic digestate management, environmental impacts, and techno-economic challenges.

Digestate is a nutrient-rich by-product from organic waste anaerobic digestion but can contribute to nutrient pollution without comprehensive management strategies. Some nutrient pollution impacts include harmful algal blooms, hypoxia, and eutrophication. This contribution explores current productive uses of digestate by analyzing its feedstocks, processing technologies, economics, product quality, impurities, incentive policies, and regulations. The analyzed studies found that feedstock, processing technology, and process operating conditions highly influence the digestate product characteristics. Also, incentive policies and regulations for managing organic waste by anaerobic digestion and producing digestate as a valuable product promote economic benefits. However, there are not many governmental and industry-led quality assurance certification systems for supporting commercializing digestate products. The sustainable and safe use of digestate in different applications needs further development of technologies and processes. Also, incentives for digestate use, quality regulation, and social awareness are essential to promote digestate product commercialization as part of the organic waste circular economy paradigm. Therefore, future studies about circular business models and standardized international regulations for digestate products are needed.

A data engineering approach for sustainable chemical end-of-life management.

The presence of chemicals causing significant adverse human health and environmental effects during end-of-life (EoL) stages is a challenge for implementing sustainable management efforts and transitioning towards a safer circular life cycle. Conducting chemical risk evaluation and exposure assessment of potential EoL scenarios can help understand the chemical EoL management chain for its safer utilization in a circular life-cycle environment. However, the first step is to track the chemical flows, estimate releases, and potential exposure pathways. Hence, this work proposes an EoL data engineering approach to perform chemical flow analysis and screening to support risk evaluation and exposure assessment for designing a safer circular life cycle of chemicals. This work uses publicly-available data to identify potential post-recycling scenarios (e.g., industrial processing/use operations), estimate inter-industry chemical transfers, and exposure pathways to chemicals of interest. A case study demonstration shows how the data engineering framework identifies, estimates, and tracks chemical flow transfers from EoL stage facilities (e.g., recycling and recovery) to upstream chemical life cycle stage facilities (e. g., manufacturing). Also, the proposed framework considers current regulatory constraints on closing the recycling loop operations and provides a range of values for the flow allocated to post-recycling uses associated with occupational exposure and fugitive air releases from EoL operations.

Valuing economic impact reductions of nutrient pollution from livestock waste.

Nutrient pollution from livestock waste impacts both fresh and marine coastal waters. Harmful algae blooms (HABs) are a common ecosystem-level response to such pollution that is detrimental to both aquatic life and human health and that generates economic losses (e.g., property values and lost tourism). Waste treatment and management technologies are not well established practices due, in part, to the difficulty to attribute economic value to associated social and environmental impacts of nutrient pollution. In this work, we propose a computational framework to quantify the economic impacts of HABs. We demonstrate the advantage of quantifying these impacts through a case study on livestock waste management in the Upper Yahara watershed region (in the state of Wisconsin, USA). Our analysis reveals that every excess kilogram of phosphorus runoff from livestock waste results in total economic losses of 74.5 USD. Furthermore, we use a coordinated market analysis to demonstrate that this economic impact provides a strong enough incentive to activate a nutrient management and valorization market that can help balance phosphorus within the study area. The proposed framework can help state, tribes, and federal regulatory agencies develop regulatory and non-regulatory policies to mitigate the impacts of nutrient pollution.

A data engineering framework for on-site end-of-life industrial operations.

Sustainable initiatives for converting end-of-life (EoL) material flows into feedstocks would make a crucial contribution towards protecting our environment and mitigating the negative impacts of anthropogenic activities. Chemical flow analysis enables decision-makers to identify potential environmental releases and exposure pathways at the EoL stage and, therefore, improves the estimation of chemical exposure. Certain industrial facilities apply on-site pollution abatement operations, thereby constituting nodes of the chemical EoL management chain that can be evaluated and improved to enable greater circularity of materials. This work enhances and extends a recently published EoL data engineering framework by using publicly-available databases, data- driven models, and analytic hierarchy approaches to track chemicals, estimate releases, and potential exposure pathways at on-site industrial pollution management operations. The extended framework develops pollution abatement unit (PAU) technologies and estimates their efficiencies, chemical releases, exposure media, operating expenses, and capital expenditures. Relevant case studies based on the food and pharmaceutical industry sectors illustrate the application of the framework for chemical flow allocation and analysis of a chemical of concern and the benefits of integrating and extending the framework with data-driven and multi-criteria decision-making models. The results show how the enhanced framework designs and evaluates PAU technology systems for managing EoL chemical flows and provides release inventories and pathways for conducting chemical risk evaluation and exposure assessment of potential on-site EoL scenarios.

A geospatial environmental and techno-economic framework for sustainable phosphorus management at livestock facilities.

Nutrient pollution of waterbodies is a major worldwide water quality problem. Excessive use and discharge of nutrients can lead to eutrophication and algal blooms in fresh and marine waters, resulting in environmental problems associated with hypoxia, public health issues related to the release of toxins and freshwater scarcity. A promising option to address this problem is the recovery of nutrient releases prior to being discharged into the environment. Driven by the sustainable materials management concept, the COW2NUTRIENT (Cattle Organic Waste to NUTRIent and ENergy Technologies) framework is developed for the techno-economic evaluation and selection of nutrient recovery systems at livestock facilities. Environmental vulnerability to nutrient pollution determined through a geographic information system (GIS)-based model and techno-economic information of different state-of-the-art nutrient management technologies are combined in a multi-criteria decision analysis (MCDA) model, resulting in the selection and economic analysis of the most suitable process for each studied livestock facility. This framework has been employed for studying the implementation of sustainable phosphorus management systems at 2,217 livestock facilities in the Great Lakes area, resulting in capital expenses of 2.5 billion USD if only phosphorus recovery technologies are installed, and up to 5.2 billion USD if nutrient management is combined with biogas and power production. However, considering potential economic incentives for the recovery of phosphorus, net revenues up to 230 million USD per year can be achieved. Therefore, the framework presented reveals the potential of implementing nutrient management systems at regional scale for the abatement of phosphorus releases from livestock facilities.

Data engineering for tracking chemicals and releases at industrial end-of-life activities.

Performing risk evaluation is necessary to determine whether a chemical substance presents an unreasonable risk of injury to human health or the environment across its life cycle stages. Data gathering, reconciliation, and management for supporting risk evaluation are time-consuming and challenging, especially for end-of-life (EoL) activities due to the need for proper reporting and traceability. A data engineering framework using publicly-available databases to track chemicals in waste streams generated by industrial activities and transferred to other facilities across different U.S. locations for waste management is implemented. The analysis tracks chemicals in waste streams generated at industrial processes and handling at off-site facilities and then estimates releases from EoL activities. The final product of this effort is a framework that identifies a set of chemical, activity, and industry sector categories as well as hazardous waste flows, emission factors, and uncertainty indicators to describe EoL activities. This framework helps to identify EoL exposure scenarios that would otherwise not be evaluated. As a case study, methylene chloride, one of the first ten chemicals to undergo risk evaluation under the amended U.S. Toxic Substances Control Act, was evaluated with results highlighting potential additional exposure scenarios.

A Visualization and Control Strategy for Dynamic Minimization of Chemical Process Releases.

Our societal needs for greener, economically viable products and processes have grown given the adverse environmental impact and unsustainable development caused by human activities, including chemical releases, exposure, and impacts. To make chemical processes safer and more sustainable, a novel sustainability-oriented control strategy is developed in this work. This strategy enables the incorporation of online sustainability assessment and process control with sustainability constraints into chemical process operations. Specifically, U.S. Environmental Protection Agency (EPA)'s GREENSCOPE (Gauging Reaction Effectiveness for the ENvironmental Sustainability of Chemistries with a multi-Objective Process Evaluator) tool is used for sustainability assessment and environmental release minimization of chemical processes. The multivariable GREENSCOPE indicators in real time can be represented using a novel visualization method with dynamic radar plots. The analysis of the process dynamic behavior in terms of sustainability performance provides means of defining sustainability constraints for the control strategy to improve process sustainability aspects with lower scores. For the control tasks, Biologically Inspired Optimal Control Strategy (BIO-CS) is implemented with sustainability constraints so that the control actions can be calculated considering the sustainability performance. This work leads to a significant step forward towards augmenting the capability of process control to meet future demands on multiple control objectives (e.g., economic, environmental, and safety related). The effectiveness of the proposed framework is illustrated via two case studies associated with a fermentation system. The results show that the proposed control strategy can effectively drive the system to the desired setpoints while meeting a preset sustainability constraint and improving the transient sustainability performance by up to 16.86% in terms of selected GREENSCOPE indicators.

Model-driven spatial evaluation of nutrient recovery from livestock leachate for struvite production.

Nutrient pollution is one of the major worldwide water quality problems, resulting in environmental and public health issues. Agricultural activities are the main source of nutrient release emissions, and the livestock industry has been proven to be directly related to the presence of high concentrations of phosphorus in the soil, which potentially can reach waterbodies by runoff. To mitigate the phosphorus pollution of aquatic systems, the implementation of nutrient recovery processes allows the capture of phosphorus, preventing its release into the environment. Particularly, the use of struvite precipitation produces a phosphorus-based mineral that is easy to transport, enabling redistribution of phosphorus to deficient locations. However, livestock leachate presents some characteristics that hinder struvite precipitation, preventing extrapolation of the results obtained from wastewater studies to cattle waste. Consideration of these elements is essential to determine the optimal operating conditions for struvite formation, and for predicting the amount of struvite recovered. In this work, a detailed thermodynamic model for precipitates formation from cattle waste is used to develop surrogate models to predict the formation of struvite and calcium precipitates from cattle waste. The variability in the organic waste composition, and how it affects the production of struvite, is captured through a probability framework based on the Monte Carlo method embedded in the model. Consistent with the developed surrogate models, the potential of struvite production to reduce the phosphorus releases from the cattle industry to watersheds in the United States has been assessed. Also, the more vulnerable locations to nutrient pollution were determined using the techno-ecological synergy sustainability metric (TES) by evaluating the spatial distribution and balance of phosphorus from agricultural activities. Although only struvite formation from cattle operations is considered, reductions between 22% and 36% of the total phosphorus releases from the agricultural sector, including manure releases and fertilizer application, can be achieved.

Applying Environmental Release Inventories and Indicators to the Evaluation of Chemical Manufacturing Processes in Early Stage Development.

As manufacturing processes are developed through the early stages of technology readiness, various assessments can be used to evaluate their performance. Performance indicators describe processes by transforming attributes into scores that represent desirable objectives. One type of assessment is obtained by determining the life cycle inventories of inputs and outputs for processes. For a functional unit of product, the user finds the resources used and the releases to the environment, which can be compared to results for similar processes and/or combined with other processes in the life cycle. In this work, an expanded range of process inputs and releases is modeled, including forklift/loader, fugitive, storage, boiler, and cooling tower emissions. A generic scenario approach for the cooling tower releases provides a first approximation of emission and wastewater flows. These inventory values are used in performance indicators that can be placed on a scale between fixed best- and worst-case limits with the GREENSCOPE methodology, thus allowing comparisons across various technologies. The processes of interest are two conversion pathways for producing cellulosic ethanol from biomass via thermochemical and biochemical routes. The results can be used in risk assessments, decision making, evaluation of research, and in spurring future technology development.

Purpose-Driven Reconciliation of Approaches to Estimate Chemical Releases.

A framework is presented to address the toolbox of chemical release estimation methods available for manufacturing processes. Although scientists and engineers often strive for increased accuracy, the development of fit-for-purpose release estimates can speed results that could otherwise delay decisions important to protecting human health and the environment. A number of release estimation approaches are presented, with the newest using decision trees for regression and prediction. Each method is evaluated in a case study for cumene production to study the reconciliation of data quality concerns and requirements for time, resources, training, and knowledge. The evaluation of these decision support criteria and the lessons learned are used to develop a purpose-driven framework for estimating chemical releases.

Logistics Network Management of Livestock Waste for Spatiotemporal Control of Nutrient Pollution in Water Bodies.

Nutrient pollution is a widespread water quality problem, which originates from excess nutrient runoff from agricultural land, improperly managed farming operations, and point sources such as wastewater treatment plants. Some nutrient pollution impacts include harmful algal blooms (HABs), hypoxia, and eutrophication. HABs are major environmental events that cause severe health threats and economic losses (e.g., tourism, real estate, commercial fishing). A dimension of the nutrient pollution problem that has not received much attention is that this interacts with organic waste management practices. As a result, it is important to connect the time and location of point and nonpoint nutrient source releases, nutrient soil content, spatial layout, and hydrology of agricultural lands with the transport of nutrients to water bodies and their impacts on aquatic ecosystems. In this work, we show how nutrient concentration in water bodies and other spatiotemporal factors are related to HAB development and how logistics management of livestock waste can be used to conduct space-time management of nutrient pollution. A case study for the Upper Yahara Watershed in the State of Wisconsin (U.S.) is employed to demonstrate the practicability of the modeling framework. Our framework reveals that logistics network management for waste and nutrients can reduce the incidence rates of HABs, but reducing it to nonharmful levels would require long-term efforts such as installing nutrient recovery technologies, coordinating manure storage and application, and deploying management incentive plans.

Coordinated Management of Organic Waste and Derived Products.

We propose a coordination framework for managing urban and rural organic waste in a scalable manner by orchestrating waste exchange, transportation, and transformation into value-added products. The framework is inspired by coordinated management systems that are currently used to operate power grids across the world and that have been instrumental in achieving high levels of efficiency and technological innovation. In the proposed framework, suppliers and consumers of waste and derived products as well as transportation and technology providers bid into a coordination system that is operated by an independent system operator. Allocations and prices for waste and derived products are obtained by the operator by solving a dispatch problem that maximizes the social welfare and that balances supply and demand across a given geographical region. Coordination enables handling of complex constraints and interdependencies that arise from transportation and bio-physico-chemical transformations of waste into products. We prove that the coordination system delivers prices and product allocations that satisfy economic and efficiency properties of a competitive market. The framework is scalable in that it can provide open access that fosters transactions between small and large players in urban and rural areas and over wide geographical regions. Moreover, the framework provides a systematic approach to enable coordinated responses to externalities such as droughts and extreme weather events, to monetize environmental impacts and remediation, to achieve complex social goals such as geographical nutrient balancing, and to justify technology investment and development efforts. Furthermore, the framework can facilitate coordination with electrical, natural gas, water, and transportation, and food distribution infrastructures.