Renewables-Integrated Energy Systems Can Provide Electricity at Lower Cost with Less Environmental and Social Damage

Renewables are clean but intermittent resources, whereas nonrenewables are cheaper and reliable but polluting resources. Optimally combining renewablessolar PV modules, wind turbines, and fuel cellswith traditional nonrenewablescoal and power from the grid―in a renewables-integrated energy system (RIES) can provide sustainable power for meeting the growing energy needs. However, such RIES optimization is a complex problem owing to the presence of multiple objectives, dynamic variations in load, and intermittency of renewables. Therefore, we propose a novel sustainability framework that optimizes the combination of different resources in RIES to obtain the best trade-off between cost of electricity, cost to society accounted for mortalities from emissions, and cost to ecosystems accounted for land use, water use, and CO2 emissions. The optimized RIES satisfying hourly load profile at three differentially populated locations in India show that the electricity cost can be reduced while reducing harm to society and to ecosystems. Our optimized designs show about 111% reduction in mortalities compared to the base case. Further, we find that the benefits of optimized RIES in terms of reducing greenhouse gas emissions and reducing human mortality are highest in densely populated regions, which coincidentally suffer from worse air quality

Recycle-BERT: Extracting Knowledge about Plastic Waste Recycling by Natural Language Processing

Managing waste plastic is a serious global challenge since most of this waste is either landfilled, incinerated, burned in the open, or littered. Each of these approaches has a large environmental impact. Establishing a circular economy of plastics requires its recovery and recycling, and much effort is now focused in this direction. The body of literature on approaches for managing the end of life of plastics is growing exponentially, making it increasingly difficult to segregate the most relevant information across multiple articles. Such work is extremely time- and effort-consuming, particularly when performed manually. To address this issue, in this study, we propose a methodology based on natural language processing (NLP) for automatically extracting and compiling information that is most relevant to a selected category of plastics. In the developed methodology, the research articles are first extracted with the help of a science-direct Elsevier Application Programming Interface key by utilizing a set of keywords such as “polyethylene recycle methods”, “polyethylene terephthalate recycle methods”, “polypropylene recycle methods”, and “polystyrene recycle methods” for relevant articles. Extracted articles are processed to address two fundamental problems; (i) classification and (ii) question and answer (Q&A) related to literature pertaining to plastic waste recycling. To this extent, we developed a bundle of NLP tools called Recycle-Bidirectional Encoder Representations from Transformers (BERT). Under the hood, Recycle-BERT comprised five language models, (1) Class-BERT, for classifying the literature as relevant or nonrelevant; (2) Catalyst-BERT, for extracting catalyst details for recycling; (3) Method-BERT, for finding the methods enlisted in the literature for recycling; (4) Reactant-BERT to identify the reactants used for waste recycling; and (5) Product-BERT to pinpoint products obtained from recycling. We have evaluated the performance of the developed models based on the metrics such as accuracy and F1-score. For the classification task, an accuracy metric value of 0.974 is obtained for the test data set. Similarly, the metric F1-score values for the Q&A task are 0.7646, 0.8014, 0.8221, and 0.8512 for the test data set for Catalyst-BERT, Method-BERT, Reactant-BERT, and Product-BERT, respectively. The results indicate the proposed NLP-based model’s ability to extract essential information from the literature related to plastic waste processing, aiding suitable recommendations to assist transformation to a sustainable circular economy

Table_1_Glass Fracture Upon Ballistic Impact: New Insights From Peridynamics Simulations.DOCX

Most glasses are often exposed to impact loading during their service life, which may lead to the failure of the structure. While in situ experimental studies on impact-induced damage are challenging due to the short timescales involved, continuum-based computational studies are complicated by the discontinuity in the displacement field arising from the propagation of cracks. Here, using peridynamics simulations, we investigate the role of the mechanical properties and geometry in determining the overall damage on a glass plate subjected to ballistic impact. In particular, we analyze the role of bullet velocity, bullet material, and elastic modulus, fracture energy, and radius of the plate. Interestingly, we observe a power-law dependence between the total damage and the fracture energy of the glass plate. Through an auto-regressive analysis of the evolution of cracks, we demonstrate that the self-affine growth of cracks leads to this power-law dependence. Overall, the present study illustrates how peridynamic simulations can offer new insights into the fracture mechanics of glasses subjected to ballistic impacts. This improved understanding can pave way to the design and development of glasses with improved impact-resistance for applications ranging from windshields and smart-phone screens to ballistics.</p