Environmental Assessment of Electrochemical Energy Storage Device Manufacturing to Identify Drivers for Attaining Goals of Sustainable Materials 4.0

Predicting the Redox Potentials of Phenazine Derivatives Using DFT-Assisted Machine Learning

This study investigates four machine-learning (ML) models to predict the redox potentials of phenazine derivatives in dimethoxyethane using density functional theory (DFT). A small data set of 151 phenazine derivatives having only one type of functional group per molecule (20 unique groups) was used for the training. Prediction accuracy was improved by a combined strategy of feature selection and hyperparameter optimization, using the external validation set. Models were evaluated on the external test set containing new functional groups and diverse molecular structures. High prediction accuracies of R ² > 0.74 were obtained on the external test set. Despite being trained on the molecules with a single type of functional group, models were able to predict the redox potentials of derivatives containing multiple and different types of functional groups with good accuracies (R ² > 0.7). This type of performance for predicting redox potential from such a small and simple data set of phenazine derivatives has never been reported before. Redox flow batteries (RFBs) are emerging as promising candidates for energy storage systems. However, new green and efficient materials are required for their widespread usage. We believe that the hybrid DFT-ML approach demonstrated in this report would help in accelerating the virtual screening of phenazine derivatives, thus saving computational and experimental costs. Using this approach, we have identified promising phenazine derivatives for green energy storage systems such as RFBs.

The Relationship between Circular Economy, Industry 4.0 and Supply Chain Performance: A Combined ISM/Fuzzy MICMAC Approach

This paper aims to assess the relationship between Industry 4.0 (I4.0) and the circular economy that could contribute to supply chain management performance. To achieve this, a combination of the interpretative structural modelling (ISM) and (cross-impact matrix multiplication applied to classification) MICMAC approach was used to establish the interrelationships between these topics. The developed analysis reveals that there are 19 constructs capable of elucidating this relationship and that there is a hierarchy between these constructs, which are presented in a structural model. Further, the different levels of dependency and driving power are compared in a cluster diagram. As the main result, it was found that there is a strong mutual relationship between the basic technologies. The use of Internet of Things and cloud computing technologies influences the collection of large amounts of data, leading to big data, which in turn influence the use of data analytics tools to obtain competitive advantages. These outcomes may contribute to managers’ more assertive decision-making regarding the selection, implementation, and evaluation of projects adopting Industry 4.0 technologies and circular economy approaches in supply chains. Moreover, our study could be the basis for future empirical research to investigate how companies incorporate Industry 4.0 technologies into their processes and how this influences the quest for sustainable supply chains.

A Framework and Baseline for the Integration of a Sustainable Circular Economy in Offshore Wind

Circular economy and renewable energy infrastructure such as offshore wind farms are often assumed to be developed in synergy as part of sustainable transitions. Offshore wind is among the preferred technologies for low-carbon energy. Deployment is forecast to accelerate over ten times faster than onshore wind between 2021 and 2025, while the first generation of offshore wind turbines is about to be decommissioned. However, the growing scale of offshore wind brings new sustainability challenges. Many of the challenges are circular economy-related, such as increasing resource exploitation and competition and underdeveloped end-of-use solutions for decommissioned components and materials. However, circular economy is not yet commonly and systematically applied to offshore wind. Circular economy is a whole system approach aiming to make better use of products, components and materials throughout their consecutive lifecycles. The purpose of this study is to enable the integration of a sustainable circular economy into the design, development, operation and end-of-use management of offshore wind infrastructure. This will require a holistic overview of potential circular economy strategies that apply to offshore wind, because focus on no, or a subset of, circular solutions would open the sector to the risk of unintended consequences, such as replacing carbon impacts with water pollution, and short-term private cost savings with long-term bills for taxpayers. This study starts with a systematic review of circular economy and wind literature as a basis for the coproduction of a framework to embed a sustainable circular economy throughout the lifecycle of offshore wind energy infrastructure, resulting in eighteen strategies: design for circular economy, data and information, recertification, dematerialisation, waste prevention, modularisation, maintenance and repair, reuse and repurpose, refurbish and remanufacturing, lifetime extension, repowering, decommissioning, site recovery, disassembly, recycling, energy recovery, landfill and re-mining. An initial baseline review for each strategy is included. The application and transferability of the framework to other energy sectors, such as oil and gas and onshore wind, are discussed. This article concludes with an agenda for research and innovation and actions to take by industry and government.

Battery Manufacturing Resource Assessment to Minimise Component Production Environmental Impacts

A promising route to attain a reliable impact reduction of supply chain materials is based on considering circular economy approaches, such as material recycling strategies. This work aimed to evaluate potential benefits of recycling scenarios for steel, copper, aluminium and plastic materials to the battery manufacturing stage. Focused on this aim, the life cycle assessment (LCA) and the environmental externalities methodologies were applied to two battery study cases: lithium manganese oxide and vanadium redox flow (VRFB) batteries, based on a cradle-to-gate LCA approach. In general, the results provided an insight into the raw material handling route. Environmental impacts were diminished by more than 20% in almost all the indicators, due to the lower consumption of virgin materials related to the implemented recyclability route. Particularly, VRFB exhibited better recyclability ratio than the Li-ion battery. For the former, the key components were the periphery ones attaining around 70% of impact reduction by recycling steel. Components of the power subsystem were also relevant, reaching around 40% of environmental impact reduction by recycling plastic. The results also foresaw opportunities for membranes, key components of VRFB materials. Based on findings, recycling strategies may improve the total circularity performance and economic viability of the studied systems.