Life Cycle Assessment Metrics Research Articles

Green Synthesis of Reticular Materials

AbstractTo help ensure a prosperous future on Earth for coming generations, academia and industry need to transform the way they plan and carry out the synthesis of novel materials to make them more environmentally sustainable. In particular, the field of reticular materials, i.e., metal‐organic frameworks, zeolitic imidazolate frameworks, and covalent organic frameworks, has great potential to outperform other materials and revolutionize various fields of applications. This review highlights several key aspects from the choice of their starting materials, solvents and synthetic methodologies that fall under the umbrella of the Green Chemistry principles, and incorporates a Circular Economy perspective by providing relevant strategies such as reuse, regeneration, or recycling to maximize the value of the Earth's available resources. Moreover, it will shed light on the life cycle assessment results of selected reticular materials and consider how constraints imposed by Green Chemistry principles, life cycle assessment metrics, and circular patterns will shape the future rational sustainable design and discovery of reticular materials.

Alternative meats and alternative facts

The alternative animal product arena is complex and quite var­ied. Some plant-based alternative meat products are derived entirely from plants. Other entrepreneurs are using cultivated cells of animal origin to derive a product structurally similar to meat and milk and comprised of animal proteins. Many of these endeavors use a combination of both approaches with the plant-based product providing filler for the cultivated meat or milk product. Most of the discussion around alternative meats has focused on bovine alternatives because of the iconic position of cattle in many climate and sustainability discussions. A consid­erable amount of capital has been raised based on the
 envisioned market share of these products, although estimates vary widely, and the high profile initial public offering of Beyond Meat shares in May 2019 which saw share prices soar from $75 to $235, has this year seen share prices plummet to about one-tenth of that highest price. Proponents of alternative meats suggest these production systems are more sustainable based on antici­patory greenhouse gas emission (GHG) and land use life-cycle assessment (LCA)/kilogram (kg) of product, as compared to LCA metrics for ruminant meat. Much of the rationale
 invokes a simplistic narrative around GHG/kg of protein. The GHG “sustainability” metric will invariably reflect poorly on ruminants as they are uniquely able to digest cellulose through their rumen microbes, and the methanogens produces methane which is a potent but short-lived greenhouse gas. And because ruminants are typically grazing in marginal land unsuitable for producing crops, they also have a high land use m2/kg of protein. However, there is no reason to conclude that food production on well-managed pasture and rangeland is, a priori, less sustainable than food production of well-managed arable cropland. More­over, the other sustainability externalities of ruminant meat production systems such as the provision of ecosystem services and biodiversity conservation, the consumption of inedible food waste and cellulose, manure, transportation, contributions to the livelihoods and food security of 1.3 billion livestock keepers, the fact that meat provides nutrients in addition to protein, and that existing harvesting systems utilize everything but the “moo” are often ignored or simply disregarded.

An integration of machine learning models and life cycle assessment for lignocellulosic bioethanol platforms

The availability and accuracy of data play a critical role in the development of Life Cycle Assessment (LCA) models, as they directly impact the effectiveness and reliability of the model. Machine learning (ML) approach has the potential to improve the data quality, leading to enhanced performance of LCA models. In this study, ML algorithms (random forest, extreme gradient boosting, and artificial neural network) were applied to predict the ethanol yield and NaOH consumption, which were key life cycle inventories for lignocellulosic bioethanol process. The prediction models exhibited a decent level of accuracy for ethanol yield (accuracy ∼ 0.85) and NaOH consumption (accuracy > 0.8). In addition, ML algorithms (genetic algorithms, particle swarm optimization, and simulate annealing) were applied to identify optimal feedstock characteristics and conditions for maximum ethanol yield. The optimization models showed an 18% improvement compared to the highest yield from the dataset (0.41 g/g). The ML models (prediction and optimization algorithms) were further integrated with the LCA model (GREET1) to enhance the ethanol yield and to reduce greenhouse gas (GHG) emission for target feedstocks. This resulted in a 6% and 19% improvement in ethanol yield and an approximately 8% and 14% reduction of GHG emissions for grass and corn stover, respectively. This study demonstrates the feasibility of utilizing ML to predict LCA metrics for the development of LCA models and to optimize the process parameters to minimize environmental impact.

Trade-offs between Sustainable Development Goals in carbon capture and utilisation.

Carbon capture and utilisation (CCU) provides an appealing framework to turn carbon emissions into valuable fuels and chemicals. However, given the vast energy required to activate the CO2 molecule, CCU may have implications on sustainable development that are still poorly understood due to the narrow scope of current carbon footprint-oriented assessments lacking absolute sustainability thresholds. To bridge this gap, we developed a power-chemicals nexus model to look into the future and understand how we could produce 22 net-zero bulk chemicals of crucial importance in a sustainable manner by integrating fossil, CCU routes and power technologies, often assessed separately. We evaluated the environmental performance of these technologies in terms of their contribution to 5 Sustainable Development Goals (SDGs), using 16 life cycle assessment metrics and 9 planetary boundaries (PB) to quantify and interpret the impact values. We found that fossil chemicals could hamper the attainment of SDG 3 on good health and well-being and SDG 13 on climate change. CCU could help meet SDG 13 but would damage other SDGs due to burden-shifting to human health, water scarcity, and minerals and metals depletion impacts. The collateral damage could be mitigated by judiciously combining fossil and CCU routes with carbon-negative power sources guided by optimisation models incorporating SDGs-based performance criteria explicitly. Our work highlights the importance of embracing the SDGs in technology development to sensibly support the low-carbon energy and chemicals transition.

Group contribution‐based LCA models to enable screening for environmentally benign novel chemicals in CAMD applications

AbstractThis study considers the development of suitable models for the estimation of life cycle assessment (LCA) indices of organic chemicals. Unlike state‐of‐the‐art models, the tools developed here correlate LCA indices with the molecular composition according to the well‐established group contribution (GC) approach. The LCA indices considered here are global warming potential, cumulative energy demand, and Eco‐Indicator 99. The model development uses data from existing LCA databases, where each material is associated with its cradle‐to‐gate LCA metrics. A variety of regression and nonregression methodologies are recruited to achieve the optimum correlation. GC models can be used to screen for molecules with optimal and/or desirable properties, using appropriate molecular design synthesis algorithms. In this framework, the models developed here are linked to the design algorithm to enable the consideration of LCA features together with other properties, for the design of environmentally benign liquid–liquid extraction solvents.

Microalgae to biofuels through hydrothermal liquefaction: Open-source techno-economic analysis and life cycle assessment

Hydrothermal liquefaction is a promising conversion technology in algae biofuel research due to its ability to agnostically convert proteins, carbohydrates, and lipids to biocrude. The high-temperature conditions that define this conversion process require the material to maintain a subcritical liquid state, which complicates the assessment of accurate thermochemical properties due to the required pressure. To clarify this issue, this work compares the estimated performance of algal hydrothermal liquefaction between different thermodynamic models. A process model was developed in Aspen Plus from a robust assessment of current literature. Techno-economic assessment and life-cycle assessment metrics are derived from this model and used as key performance indicators. The baseline fuel price contribution of hydrothermal liquefaction is $0.45 per liter gasoline equivalent. Independently decreasing the temperature from 350 °C to 260 °C while maintaining yield reduces the conversion cost by 19%, illustrating the importance of understanding the high-temperature thermodynamics of the system. Different thermodynamic property models can vary fuel conversion cost results by $0.07 per liter gasoline equivalent. The baseline global warming potential is +23 g CO2 eq MJ−1 and the net energy ratio is 0.30. Environmental metrics beyond global warming potential and net energy ratio are also discussed for the first time. Uncertainties in conversion performance are bounded through a scenario analysis that manipulates parameters such as product yield and nutrient recycle to produce a range of economic and environmental metrics. The work is supplemented with an open source model to support future hydrothermal liquefaction assessments and accelerate the development of commercial-scale systems.

Designing biomass supply chains within planetary boundaries

AbstractThe design of sustainable supply chains, which recently emerged as an active area of research in process systems engineering, is vital to ensure sustainable development. Despite past and ongoing efforts, the available methods often overlook impacts beyond climate change or incorporate them via standard life cycle assessment metrics that are hard to interpret from an absolute sustainability viewpoint. We here address the design of biomass supply chains considering critical ecological limits of the Earth—planetary boundaries—which should never be surpassed by anthropogenic activities. Our method relies on a mixed‐integer linear program that incorporates a planetary boundaries‐based damage model to quantify absolute sustainability precisely. We apply this approach to the sugarcane‐to‐ethanol industry in Argentina, identifying the optimal combination of technologies and network layout that minimize the impact on these ecological boundaries. Our framework can find applications in a wide range of supply chain problems related to chemicals and fuels production, energy systems, and agriculture planning.

Exploring the effects that a non-stationary climate and dynamic electricity grid mix has on whole building life cycle assessment: A multi-city comparison

There is a significant share of greenhouse gas (GHG) emissions attributed to the built environment, either for production or the operation of buildings. Various initiatives are being implemented to reduce the release of GHGs into the atmosphere relying on the evaluation, tracking, recording and verification of GHG emissions and removals. The annual accounting of GHG flows associated with buildings should be conducted in a lifecycle context to ensure that policies are effective at mitigating climate change. Buildings operate for decades and both the climate and electricity grid mix are expected to change significantly during such a time frame. This study aims to support the design of resource- as well as energy-efficient buildings using a sound life cycle assessment (LCA) methodology in the preliminary design stage. A straightforward method that can be applied for a detailed understanding of the effects of climate change and prospective electricity grid mix on building energy use is presented. The novelty of this study was to integrate long-term energy projections in a high temporal resolution LCA for buildings and taking into account different future climates and prospective electricity mixes across Canada. The research integrated the dynamic LCA capabilities directly into a Building Information Model (BIM). Such dynamic considerations as climate and energy mix improves the environmental importance and scientific robustness of LCA metrics. The proposed methodology will assist users to apply a clear framework that helps to define an optimized design alternative through a dynamic energy analysis and future weather forecasting simulation. An example of an actual office building is provided to demonstrate the capabilities and usefulness of the developed integrated framework.

Environmental impact assessment of biomass process chains at early design stages using decision trees

Life cycle assessment (LCA) is generally considered as a suitable methodology for the evaluation of environmental impacts of processes. However, it requires large amount and often inaccessible process data at early design stages. The present study provides an approach to streamline LCA for a broad set of biomass process chains. The proposed method breaks away from conventional LCA work in that the purpose is to support decision at early stages assuming minimal use of data available and points to most dominant LCA impacts, providing useful feedback to process design. The prediction mechanism employs decision trees, which form “if-then rules” using a set of critical parameters of the process chain with respect to various environmental impacts. The models classify products into three classes, namely having low, medium, and high environmental impact. Data for model development were obtained from early design stages and include descriptors of the molecular structure of the product and process chain-related variables corresponding to chemistry, complexity, and generic process conditions. Twenty-three LCA metrics were selected as target attributes, according to the ReCiPe and the cumulative energy demand (CED) methods. A broad set of process chains is derived from the work of Karka et al. (Int J Life Cycle Assess 22(9):1418–1440, 2017). Results demonstrate that the average classification error for the decision trees ranges between 13.4 and 43.8% for the various LCA metrics and multifunctionality approaches. Allocation approaches present a better classification performance (up to 25% error) compared with the substitution approach for LCA metrics, such as climate change, CED, and human health. For the majority of models, low- and high-output classes are characterized by better predictive performance compared with the medium class. The interpretability of selected decision trees is analyzed in terms of pruning levels and “irrational” branches. The results of the application of the decision tress for recently published case studies show for instance that 8 out of 13 cases were correctly classified for CED. The proposed approach provides a first generation of models in the form of computationally inexpensive and easily interpretable decision trees that can be used as pre-screening tools for the environmental assessment of bio-based production ahead of detailed design and conventional LCA approaches. The transparent structure of the decision trees facilitates the identification of critical decision variables providing insights for improvement in terms of process parameters, biomass feedstock, or even targeted product.

A framework for the environmental, health and safety hazard assessment for amine-based post combustion CO2 capture

Given the environmental concerns driving process developments and considering the size and scale of potential application of post combustion capture (PCC), it is important to fully understand process hazards. In this work we present an environmental, health and safety (EHS) hazard assessment framework for screening and comparing amine based PCC options. A multi-criteria assessment compares the performance of 7m Monoethanolamine (MEA), 4m Diethanolamine (DEA), 10m Diglycolamine (DGA), 8m Piperazine (PZ) and a blend of 7m Methyldiethanolamine (MDEA) with 2m PZ under a set of different process conditions. The EHS assessment takes into account varying circulating volumes, process conditions and potential for solvent degradation. The EHS assessment is also compared with operating costs and life cycle assessment (LCA) metrics. The EHS assessment is carried out on two levels. The first is less data intensive and provides a unified score that can be used to compare and screen process alternatives, while the second level studies the contribution of individual degradation products to different EHS hazard categories in detail. Results of the parametric study show that lean loading is an influential factor in determining first level EHS scores, process costs and environmental impacts and a trade-off is observed between EHS and LCA scores for some of the solvents. In the second level EHS assessment, the parent amine heavily influences most mass dependent hazard categories such as the fire/explosion, acute toxicity and air mediated effects category. Heat stable salts affect the solid waste category. Most degradation products are considered corrosive or irritant and therefore feature strongly in that category in addition to the parent amine. The second level EHS assessment is complemented with workplace exposure analysis to ensure the compliance with workplace threshold limits. Nitrosamines stand out for acute and chronic toxicity effects and are therefore used as an example to demonstrate the workplace exposure. Workplace amines and nitrosamine concentrations are estimated to assess long and potential short term worker exposure as a result of gas leakages and spills respectively. Indoor ventilation rates are inadequate to keep workplace amine and nitrosamine concentrations below the recommended guidelines. To reduce short term exposure effects, a steady state liquid nitrosamine concentration lower than 13.7mM is required. The results of the parametric study show that changing process conditions can lower nitrosamine concentrations, but can also shift the contribution of the parent amine and degradation products of the other hazard categories and affect LCA metrics. The sensitivity of the EHS and LCA results to model assumptions and conditions are tested by varying inlet NOx, expected aerosols and solvent degradation levels. Low NOx levels in the flue gas are required for PZ and MDEA-PZ systems to meet the nitrosamine concentration thresholds for environmental and health concerns. This study also highlights the need to consider hazard effects during early process design phases, identifies degradation related data gaps and critical process parameters towards more environmentally benign PCC design.

Multi-criteria screening of chemicals considering thermodynamic and life cycle assessment metrics via data envelopment analysis: application to CO2capture

We propose a decision-support tool to assess the sustainability level of chemical products and processes.

Quantitative Uncertainty Analysis of Life Cycle Assessment for Algal Biofuel Production

As a result of algae's promise as a renewable energy feedstock, numerous studies have used Life Cycle Assessment (LCA) to quantify the environmental performance of algal biofuels, yet there is no consensus of results among them. Our work, motivated by the lack of comprehensive uncertainty analysis in previous studies, uses a Monte Carlo approach to estimate ranges of expected values of LCA metrics by incorporating parameter variability with empirically specified distribution functions. Results show that large uncertainties exist at virtually all steps of the biofuel production process. Although our findings agree with a number of earlier studies on matters such as the need for wet lipid extraction, nutrients recovered from waste streams, and high energy coproducts, the ranges of reported LCA metrics show that uncertainty analysis is crucial for developing technologies, such as algal biofuels. In addition, the ranges of energy return on (energy) invested (EROI) values resulting from our analysis help explain the high variability in EROI values from earlier studies. Reporting results from LCA models as ranges, and not single values, will more reliably inform industry and policy makers on expected energetic and environmental performance of biofuels produced from microalgae.

Identifying Key Life Cycle Assessment Metrics in the Multiobjective Design of Bioethanol Supply Chains Using a Rigorous Mixed-Integer Linear Programming Approach

The design of more sustainable bioethanol supply chains (SCs) has recently emerged as an active area of research. Most of the approaches presented so far have somehow a limited scope, as they focus on minimizing the emitted greenhouse gases as unique criterion, neglecting the damage caused in other impact categories. In this work, we address the multiobjective design of bioethanol SCs considering several life cycle assessment impacts. To overcome the numerical difficulties of dealing with several objective functions, we investigate the application of a rigorous mixed-integer linear programming-based dimensionality reduction method that minimizes the error of omitting objectives. The usefulness of this approach is tested through its application to the design of a bioethanol/sugar SC in Argentina, in which five environmental objectives are simultaneously optimized along with the net present value. The proposed method makes it possible to reduce the number of environmental indicators, thereby facilitating the calculation and analysis of the Pareto solutions.

Sustainability metrics for vehicles

Abstract The public has growing concerns about the effects of human activity on the environment for both themselves and future generations. The concept being developed to address these environmental concerns, along with economic and social-equity considerations, is called "sustainable development". The business community has already started to address their contribution to environmental issues through collaborative efforts under organizations such as the World Business Council for Sustainable Development. That work has focused primarily on industrial and corporate environmental and economic performance through the development of eco-efficiency metrics, which are a measure of resources and materials consumed per unit output of product, sales, or value-added. While this development is important, it is not enough. Clearly, the environmental performance of the entire product system must also be elucidated. Otherwise, one might forget, for example, the power plant emissions of his or her so-called "zero-emission" vehicle. The metrics required to characterize sustainable practices and systems have yet to be developed. The set will include economic, environmental, and social-equity metrics. Because of their system-oriented environmental performance representation, life-cycle assessment metrics are certainly going to be included in such a set. A discussion of some potential life-cycle metrics follows.