A common sustainability issue, arising in production systems, is the efficient use of resources for providing goods or services. With the increased interest in a hydrogen (H₂) economy, the life-cycle environmental performance of H₂ production has special significance for assisting in identifying opportunities to improve environmental performance and to guide challenging decisions and select between technology paths. Life cycle impact assessment methods are rapidly evolving to analyze multiple environmental impacts of the production of products or processes. This study marks the first step in developing process-based streamlined life cycle analysis (LCA) of several H₂ production pathways combining life cycle impacts at the midpoint (17 problem-oriented) and endpoint (3 damage-oriented) levels using the state-of-the-art impact assessment method ReCiPe 2016. Steam reforming of natural gas, coal gasification, water electrolysis via proton exchange membrane fuel cell (PEM), solid oxide electrolyzer cell (SOEC), biomass gasification and reforming, and dark fermentation of lignocellulosic biomass were analyzed. An innovative aspect is developed in this study is an analysis of water consumption associated with H₂ production pathways by life-cycle stage to provide a better understanding of the life cycle water-related impacts on human health and natural environment. For water-related scope, Water scarcity footprint (WSF) quantified using Available WAter REmaining (AWARE) method was applied as a stand-alone indicator. The paper discusses the strengths and weaknesses of each production pathway, identify the drivers of environmental impact, quantify midpoint environmental impact and its influence on the endpoint environmental performance. The findings of this study could serve as a useful theoretical reference and practical basis to decision-makers of potential environmental impacts of H₂ production systems.

The paper presents a holistic evaluation of the energy and environmental profile of two renewable energy technologies: Photovoltaics (thin-film and crystalline) and solar thermal collectors (flat plate and vacuum tube). The selected renewable systems exhibit size scalability (i.e., photovoltaics can vary from small to large scale applications) and can easily fit to residential applications (i.e., solar thermal systems). Various technical variations were considered for each of the studied technologies. The environmental implications were assessed through detailed life cycle assessment (LCA), implemented from raw material extraction through manufacture, use, and end of life of the selected energy systems. The methodological order followed comprises two steps: i. LCA and uncertainty analysis (conducted via SimaPro), and ii. techno-economic assessment (conducted via RETScreen). All studied technologies exhibit environmental impacts during their production phase and through their operation they manage to mitigate significant amounts of emitted greenhouse gases due to the avoided use of fossil fuels. The life cycle carbon footprint was calculated for the studied solar systems and was compared to other energy production technologies (either renewables or fossil-fuel based) and the results fall within the range defined by the global literature. The study showed that the implementation of photovoltaics and solar thermal projects in areas with high average insolation (i.e., Crete, Southern Greece) can be financially viable even in the case of low feed-in-tariffs. The results of the combined evaluation provide insight on choosing the most appropriate technologies from multiple perspectives, including financial and environmental.

Digital Twins (DTs) are a core enabler of Industry 4.0 in manufacturing. Cognitive Digital Twins (CDTs), as an evolution, utilize services and tools towards enabling human-like cognitive capabilities in DTs. This paper proposes a conceptual framework for implementing CDTs to support resilience in production, i.e., to enable manufacturing systems to identify and handle anomalies and disruptive events in production processes and to support decisions to alleviate their consequences. Through analyzing five real-life production cases in different industries, similarities and differences in their corresponding needs are identified. Moreover, a connection between resilience and cognition is established. Further, a conceptual architecture is proposed that maps the tools materializing cognition within the DT core together with a cognitive process that enables resilience in production by utilizing CDTs.

Supply chain agility and resilience are key factors for the success of manufacturing companies in their attempt to respond to dynamic changes. The circular economy, the need for optimized material flows, ad-hoc responses and personalization are some of the trends that require supply chains to become “cognitive”, i.e., able to predict trends and flexible enough in dynamic environments, ensuring optimized operational performance. Digital twins (DTs) is a promising technology, and a lot of work is done on the factory level. In this paper, the concept of cognitive digital twins (CDTs) and how they can be deployed in connected and agile supply chains is elaborated. The need for CDTs in the supply chain as well as the main CDT enablers and how they can be deployed under an operational model in agile networks is described. More emphasis is given on the modelling, cognition and governance aspects as well as on how a supply chain can be configured as a network of connected CDTs. Finally, a deployment methodology of the developed model into an example of a circular supply chain is proposed.