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The worldwide plastic waste accumulation has posed probably irreversible harm to the environment, and the main dilemma for this global issue is: How to define the waste quality grading system to maximise plastic recyclability? This work reports a machine learning approach to evaluating the recyclability of plastic waste by categorising the quality trends of the contained polymers with auxiliary materials. The result reveals the hierarchical resource quality grades predictors that restrict the mapping of the waste sources to the demands. The Pinch Analysis framework is then applied using the quality clusters to maximise plastic recyclability. The method identifies a Pinch Point – the ideal waste quality level that limits the plastic recycling rate in the system. The novel concept is applied to a problem with different polymer types and properties. The results show the maximum recycling rate for the case study to be 38 % for PET, 100 % for PE and 92 % for PP based on the optimal number of clusters identified. Trends of environmental impacts with different plastic recyclability and footprints of recycled plastic are also compared.

Abstract The petrochemical supply chain is a worldwide undertaking, where final products will often travel thousands of miles from oil well to gas station pump. Within the crude oil supply chain, various entities compete and attempt to maximize their profits by exploiting the demands and needs of the other companies within the supply chain. Each company or player in the crude oil industry has its own objective, and it will compete against other players trying to pursue their respective objectives. Due to the non-cooperative structure of the crude oil supply chain, the decisions that maximize profit often do not coincide with decisions that minimize environmental impact, as a reduction in environmental impact usually correlates with a reduction in profit. In this work, the crude oil supply chain from oil well to refinery is modelled as a mixed-integer bilevel linear program that accounts for conflicting objectives and interactions between different stakeholders. The composition, pricing, transportation distances, and environmental impacts of the different crude oils are taken into consideration in the model. In the bilevel problem, the crude oil producers aim to maximize their own profits from the sale of their crude oil, while the crude oil refiner has the dual objectives of both maximizing the profit made from the sale of distilled products to the market and minimizing the life cycle environmental impact of the refinery products, which is determined by the type of crude oil purchased by the refinery. The resulting model is then applied to two case studies, both based on a U.S. refinery purchasing oil from various crude oil-producing countries. Both case studies produce a set of pareto-optimal decisions for the refiner that display the inherent trade-offs between minimizing “cradle-to-gate” environmental impact and maximizing profit. At the trade-off point in the first case, a 4.4% decrease in profit leads to a 3.0% decrease in the kilograms of CO2 per megajoule of energy produced. Meanwhile, the trade-off point selected in the second case displays a 7.5% reduction in the total environmental impact while decreasing total profit by only 5.9%. Furthermore, the refiner’s profit at the trade-off point in the second case is $2.148 M, which is situated between the worst-case deterministic profit of $1.558 M and the best-case deterministic profit of $2.652 M.

The widespread COVID-19 pandemic led to a shortage in the supply of N95 respirators in the United States until May 2021. In this study, we address the energy, environmental, and economic benefits of the decontamination-and-reuse of the N95 masks. Two popular decontamination methods, including dry heat and vapor hydrogen peroxide (VHP), are investigated in this study for their effective pathogen inactivation and favorable performance in preserving filtration efficiency and structural integrity of respirators. Two multiple reuse cases, under which the N95 masks are disinfected and used five times with the dry heat method and 20 times using the VHP method, are considered and compared with a single-use case. Compared to the single-use case, the dry heat-based multiple-use case reduces carbon footprint by 50% and cumulative energy demand (CED) by 17%, while the VHP-based case decreases carbon footprint by 67% and CED by 58%. The dry-heat-based and VHP-based multiple reuse cases also present environmental benefits in most of the other impact categories, primarily due to substituting new N95 respirators with decontaminated ones. Decontaminating and reusing respirators costs 77% and 89% less than the case of single-use and disposal. The sensitivity analysis results show that the geographical variation in the power grid and the times of respirator use are the most influential factors for carbon footprint and CED, respectively. The result also reaffirms the energy, environmental, and economic favorability of the decontamination and reuse of N95 respirators.

AbstractThis article addresses the sustainable design and synthesis of open‐loop recycling process of waste high‐density polyethylene (HDPE) under both environmental and economic criteria. We develop by far the most comprehensive superstructure for producing monomers, aromatic mixtures, and fuels from waste HDPE. The superstructure optimization problem is then formulated as a multi‐objective mixed‐integer nonlinear fractional programming (MINFP) problem to simultaneously optimize the unit net present value (NPV) and unit life cycle environmental impacts. A tailored global optimization algorithm integrating the inexact parametric algorithm with the branch‐and‐refine algorithm is applied to efficiently solve the resulting nonconvex MINFP problem. Results show that the optimal unit NPV ranges from $107.2 to $151.3 per ton of HDPE treated. Moreover, the unit life cycle greenhouse gas emissions of the most environmentally friendly HDPE recycling process are 0.40 ton CO2‐eq per ton of HDPE treated, which is 63% of that of the most economically competitive process design.

Proporcionar estimaciones precisas del rendimiento de los cultivos a grandes escalas espaciales y comprender las pérdidas de rendimiento bajo un estrés climático extremo es un desafío urgente para mantener la seguridad alimentaria mundial. Si bien el enfoque de aprendizaje profundo basado en datos ha demostrado una gran capacidad para predecir patrones de rendimiento, su capacidad para detectar y atribuir los impactos de los extremos climáticos en los rendimientos sigue siendo desconocida. En este estudio, desarrollamos un marco de aprendizaje multitarea basado en redes neuronales profundas para estimar las variaciones del rendimiento del maíz a nivel de condado en el Cinturón del Maíz de EE. UU. de 2006 a 2018, con un enfoque especial en la pérdida extrema de rendimiento en 2012. Encontramos que nuestro modelo de aprendizaje profundo reflejó las variaciones de rendimiento con buena precisión para 2006-2018 (R2 = 0,81) y reprodujo bien las anomalías extremas de rendimiento en 2012 (R2 = 0,79). Un análisis de atribución adicional indicó que el estrés térmico extremo fue la principal causa de pérdida de rendimiento, contribuyendo al 72.5% de la pérdida de rendimiento, seguido de anomalías en el déficit de presión de vapor (17.6%) y la precipitación (10.8%). Nuestro modelo de aprendizaje profundo también pudo estimar el impacto acumulado de los factores climáticos en el rendimiento del maíz e identificar que la fase de sedación fue la etapa más crítica que dio forma a la respuesta del rendimiento al estrés climático extremo en 2012. Nuestros resultados proporcionan un nuevo marco de aprendizaje profundo espacio-temporal para evaluar y atribuir la respuesta del rendimiento de los cultivos a las variaciones climáticas en la era rica en datos. Fournir des estimations précises du rendement des cultures à grande échelle spatiale et comprendre les pertes de rendement en cas de stress climatique extrême est un défi urgent pour maintenir la sécurité alimentaire mondiale. Bien que l'approche d'apprentissage en profondeur axée sur les données ait montré une grande capacité à prédire les modèles de rendement, sa capacité à détecter et à attribuer les impacts des extrêmes climatiques sur les rendements reste inconnue. Dans cette étude, nous avons développé un cadre d'apprentissage multitâche basé sur un réseau neuronal profond pour estimer les variations du rendement du maïs au niveau du comté par rapport à la Corn Belt américaine de 2006 à 2018, avec un accent particulier sur la perte de rendement extrême en 2012. Nous avons constaté que notre modèle d'apprentissage profond prévoyait les variations de rendement avec une bonne précision pour 2006-2018 (R2 = 0,81) et reproduisait bien les anomalies de rendement extrêmes en 2012 (R2 = 0,79). Une analyse d'attribution plus poussée a indiqué que le stress thermique extrême était la principale cause de la perte de rendement, contribuant à 72,5 % de la perte de rendement, suivi des anomalies du déficit de pression de vapeur (17,6 %) et des précipitations (10,8 %). Notre modèle d'apprentissage en profondeur a également permis d'estimer l'impact accumulé des facteurs climatiques sur le rendement du maïs et d'identifier que la phase de soie était l'étape la plus critique de la réponse du rendement au stress climatique extrême en 2012. Nos résultats fournissent un nouveau cadre d'apprentissage profond spatio-temporel pour évaluer et attribuer la réponse du rendement des cultures aux variations climatiques à l'ère riche en données. Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food security. While the data-driven deep learning approach has shown great capacity in predicting yield patterns, its capacity to detect and attribute the impacts of climatic extremes on yields remains unknown. In this study, we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018, with a special focus on the extreme yield loss in 2012. We found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018 (R2 = 0.81) and well reproduced the extreme yield anomalies in 2012 (R2 = 0.79). Further attribution analysis indicated that extreme heat stress was the major cause for yield loss, contributing to 72.5% of the yield loss, followed by anomalies of vapor pressure deficit (17.6%) and precipitation (10.8%). Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in 2012. Our results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era. يمثل توفير تقديرات دقيقة لمحصول المحاصيل على نطاقات مكانية واسعة وفهم خسائر الغلة في ظل الإجهاد المناخي الشديد تحديًا عاجلاً للحفاظ على الأمن الغذائي العالمي. في حين أظهر نهج التعلم العميق القائم على البيانات قدرة كبيرة على التنبؤ بأنماط الغلة، فإن قدرته على اكتشاف وعزو تأثيرات الظواهر المناخية المتطرفة على الغلة لا تزال غير معروفة. في هذه الدراسة، طورنا إطارًا تعليميًا عميقًا متعدد المهام قائمًا على الشبكة العصبية لتقدير الاختلافات في محصول الذرة على مستوى المقاطعة عبر حزام الذرة الأمريكي من عام 2006 إلى عام 2018، مع التركيز بشكل خاص على الخسارة الشديدة في المحصول في عام 2012. وجدنا أن نموذج التعلم العميق الخاص بنا قد عرقل اختلافات العائد بدقة جيدة للفترة 2006-2018 (R2 = 0.81) وأعاد إنتاج الشذوذ الشديد في العائد في عام 2012 (R2 = 0.79). أشار تحليل الإسناد الإضافي إلى أن الإجهاد الحراري الشديد كان السبب الرئيسي لفقدان الغلة، حيث ساهم بنسبة 72.5 ٪ من فقدان الغلة، تليها حالات شاذة من عجز ضغط البخار (17.6 ٪) وهطول الأمطار (10.8 ٪). كان نموذج التعلم العميق لدينا قادرًا أيضًا على تقدير التأثير المتراكم للعوامل المناخية على غلة الذرة وتحديد أن مرحلة الحرير كانت المرحلة الأكثر أهمية في تشكيل استجابة الغلة للإجهاد المناخي الشديد في عام 2012. توفر نتائجنا إطارًا جديدًا للتعلم العميق المكاني والزماني لتقييم وعزو استجابة غلة المحاصيل للتغيرات المناخية في العصر الغني بالبيانات.

Étant donné que le secteur de l'énergie est le principal contributeur aux émissions mondiales de gaz à effet de serre, la décarbonisation des systèmes énergétiques est cruciale pour l'atténuation du changement climatique. Deux défis majeurs de la décarbonisation des systèmes énergétiques sont la planification de la transition vers les énergies renouvelables et l'exploitation durable des systèmes. Pour relever les défis, l'intégration des technologies de l'information et de la communication émergentes peut faciliter à la fois la conception et le fonctionnement des futurs systèmes énergétiques intelligents avec une forte pénétration des énergies renouvelables et des structures décentralisées. Le présent travail fournit un aperçu complet de l'applicabilité des technologies de l'information et de la communication émergentes dans la transition vers les énergies renouvelables et les systèmes énergétiques intelligents, y compris l'intelligence artificielle, l'informatique quantique, la blockchain, les technologies de communication de nouvelle génération et le métavers. Des orientations de recherche pertinentes sont introduites en examinant la littérature existante. Cette revue se termine par une discussion des cas d'utilisation industrielle et des démonstrations des technologies énergétiques intelligentes. Dado que el sector energético es el contribuyente dominante a las emisiones mundiales de gases de efecto invernadero, la descarbonización de los sistemas energéticos es crucial para la mitigación del cambio climático. Dos de los principales desafíos de la descarbonización de los sistemas energéticos son la planificación de la transición renovable y las operaciones de sistemas sostenibles. Para abordar los desafíos, la incorporación de tecnologías emergentes de información y comunicación puede facilitar tanto el diseño como las operaciones de futuros sistemas de energía inteligente con altas penetraciones de energía renovable y estructuras descentralizadas. El presente trabajo proporciona una visión general completa de la aplicabilidad de las tecnologías emergentes de información y comunicación en la transición renovable y los sistemas de energía inteligente, incluida la inteligencia artificial, la computación cuántica, la cadena de bloques, las tecnologías de comunicación de próxima generación y el metaverso. Las direcciones de investigación relevantes se introducen a través de la revisión de la literatura existente. Esta revisión concluye con una discusión de los casos de uso industrial y demostraciones de tecnologías de energía inteligente. Since the energy sector is the dominant contributor to global greenhouse gas emissions, the decarbonization of energy systems is crucial for climate change mitigation. Two major challenges of energy systems decarbonization are renewable transition planning and sustainable systems operations. To address the challenges, incorporating emerging information and communication technologies can facilitate both the design and operations of future smart energy systems with high penetrations of renewable energy and decentralized structures. The present work provides a comprehensive overview of the applicability of emerging information and communication technologies in renewable transition and smart energy systems, including artificial intelligence, quantum computing, blockchain, next-generation communication technologies, and the metaverse. Relevant research directions are introduced through reviewing existing literature. This review concludes with a discussion of the industrial use cases and demonstrations of smart energy technologies. نظرًا لأن قطاع الطاقة هو المساهم المهيمن في انبعاثات غازات الدفيئة العالمية، فإن إزالة الكربون من أنظمة الطاقة أمر بالغ الأهمية للتخفيف من آثار تغير المناخ. يتمثل تحديان رئيسيان لإزالة الكربون من أنظمة الطاقة في تخطيط الانتقال المتجدد وعمليات الأنظمة المستدامة. ولمواجهة التحديات، يمكن أن يؤدي دمج تقنيات المعلومات والاتصالات الناشئة إلى تسهيل تصميم وتشغيل أنظمة الطاقة الذكية المستقبلية ذات الاختراقات العالية للطاقة المتجددة والهياكل اللامركزية. يقدم العمل الحالي لمحة شاملة عن قابلية تطبيق تقنيات المعلومات والاتصالات الناشئة في التحول المتجدد وأنظمة الطاقة الذكية، بما في ذلك الذكاء الاصطناعي والحوسبة الكمومية وسلسلة الكتل وتقنيات الاتصالات من الجيل التالي والميتافيرس. يتم تقديم اتجاهات البحث ذات الصلة من خلال مراجعة الأدبيات الموجودة. تختتم هذه المراجعة بمناقشة حالات الاستخدام الصناعي والعروض التوضيحية لتقنيات الطاقة الذكية.

In light of current energy policies responding to rapid climate change, much attention has been directed to developing feasible approaches for transitioning energy production from fossil-based resources to renewable energy. Although existing studies analyze regional dispatch of renewable energy sources and capacity planning, they do not fully explore the impacts of the energy storage system technology's technical and economic characteristics on renewable energy integration and energy transition, and the importance of energy storage systems to the energy transition is currently ignored. To fill this gap, we propose an integrated optimal power flow and multi-criteria decision-making model to minimize system cost under operational constraints and evaluate the operational performance of renewable energy technologies with multidimensional criteria. The proposed method can identify the most critical features of energy storage system technologies to enhance renewable energy integration and achieve New York State's climate goals from 2025 to 2040. We discover that lead-acid battery requires an additional 38.66 GW capacity of renewable energy sources than lithium-ion battery to achieve the zero carbon dioxide emissions condition. Based on the cross-sensitivity analysis in the multidimensional evaluation, the vanadium redox flow battery performs the best, and the nickel-cadmium battery performs the worst when reaching the zero carbon dioxide emissions target in 2040. The results of the proposed model can also be conveniently generalized to select ESS technology based on the criteria preferences from RE integration and energy transition studies and serve as a reference for ESS configurations in future energy and power system planning.