Design for sustainability

The healthcare sector is a significant contributor to global greenhouse gas emissions, largely due to resource-intensive care processes. Telemedicine represents an innovative solution for managing chronic patients, with the potential to reduce environmental impacts and improve healthcare accessibility. However, current literature lacks a standardized approach to comprehensively assess the environmental and social effects of such practices. The study aims to overcome current limitations evaluating the environmental and social impacts of televisits as an alternative to traditional in-person follow-up visits for chronic heart failure (HF) patients in a large Italian hospital. The Life Cycle Assessment (LCA) steps ensure an objective and standardized environmental evaluation. Data on social and human-related aspects for preliminary mapping of social impacts has been acquired by means structured interviews. Environmental LCA using the ReCiPe 2016 method quantifies the environmental impacts of one in-person or remote visit process for a one patient, including direct and indirect emissions from transportation, energy use, paper materials and telecommunications. Internal hospital stakeholders were involved in defining aspects of interest for initial mapping of social impacts. Results demonstrate that televisits significantly reduce environmental impacts, particularly in relation to patient transportation. On the social side, stakeholders reported benefits in terms of patient's experience, care continuity, and improvement of clinical workflows. The study emphasizes the necessity of establishing continuous impact monitoring systems to ensure long-term sustainability and efficiency.

Keywords: Life cycle assessment , Socioenvironmental impacts , Sustainable development , Telemedicine , Televisit environmental impact

The rapid diffusion of additive manufacturing (AM) raises the need to carefully evaluate its environmental sustainability. However, the continuous evolution of AM technologies makes life cycle assessment (LCA) studies, which consider traditional technologies, of little significance. The problem is accentuated if one wants to consider such studies to make decisions in the manufacturing sector for the near future. To overcome this limitation, this study considers patented AM technologies related to Direct Energy Deposition (DED) and employs prospective LCA methodology to estimate its future impacts. This approach holds the potential to provide valuable insights into the future of these technologies, particularly when they reach technological maturity and large-scale production. The peculiarity of this study lies in the use of patents as a source to extract the data that are used to model the foreground inventory of future DED. Consistently with the time scope of the analysis, the background inventory is also modelled in future time, modifying the production impacts of materials and energy using Integrated assessment modelling. The environmental impact of future DED is estimated by considering foreground data and background inventory. The results are compared with the DED impacts available on the market today to identify the main environmental benefits that patents can bring. The collection and analysis of the technological solutions claimed in the same patents and their classification according to the environmental advantages is used to identify eco-design solutions and strategies that can be considered to improve the sustainability of current DED technology.

Keywords: additive manufacturing , patent analysis , prospective LCA

The design of an assistive device for a student with disabilities to enable his inclusion into the University is based on ethical reasons. The device must be personalized starting from a participative and human centered approach. This work presents the design and development phases of a highly functional assistive device which enables a quadriplegic user, who is unable to speak, to autonomously communicate. The starting phase deals with the identification of the user needs, considering the residual mobility of a single arm and the needs of the caregivers. The motion study allows to define the requirements which enable the definition of the system architecture. A modular architecture is chosen, allowing damaged or obsolete parts to be easily replaced extending the useful life of the device, but reducing the resources consumption during the life cycle. Sustainable and recyclable materials are selected using Additive Manufacturing technologies.

Keywords: Human centered design , Inclusive design , Personalized assistive device

This study investigates the pyrolysis characteristics and kinetics of dairy cattle manure from organic (OSDM) and conventional (CSDM) systems through a comprehensive analysis of thermogravimetric (TG) data combined with advanced machine learning techniques. The results showed that the different diets and manure management systems influence pyrolysis thermal decomposition and kinetics. Also, CSDM releases volatile components more efficiently, suggesting a more favorable thermal decomposition process. Also, kinetic analysis revealed that the average activation energy of CSDM (203 kJ/mol) was slightly higher than that of OSDM (185 kJ/mol), suggesting that the pyrolysis of CSDM requires more energy to initiate the reactions. Moreover, a novel machine learning approach was based on training models on the empirical TG and conversion degree data, allowing for the forecasting of weight loss profiles under varying thermal conditions. For both the manure samples, the predicted TG curves with a heating rate of 25 and 50 °C/min were found to be in good agreement with the experimental ones.

Keywords: Kinetics , Machine learning , Manure , Pyrolysis , Thermogravimetry , Waste management

A sustainable battery ecosystem is essential for the European Union's transition to clean energy, a goal underscored by the Green Deal's commitment to climate neutrality by 2050. Lithium-ion batteries, crucial for electric vehicles and energy storage, are at the heart of this shift. Yet, the rapid growth in demand and dependence on imported critical raw materials, such as lithium, cobalt, and nickel, expose Europe to significant geopolitical and environmental vulnerabilities. Recent regulations, particularly the Battery Regulation (EU) 2023/1542, set rigorous standards to address these vulnerabilities through improved lifecycle management and recycling practices. This paper examines the challenges in securing stable raw material supplies, minimizing environmental impacts, and scaling up recycling capacities. Demand forecasting models, statistical production projections based on Compound Annual Growth Rates, and supply risk assessments using Herfindahl-Hirschman Index and Fragile States Index have been applied to estimate future material availability. These are integrated with scenario-based simulations to assess potential gaps under optimistic, baseline, and pessimistic conditions. Additionally, simplified Life Cycle Assessment and a regulatory gap analysis were conducted to evaluate environmental impacts and the adequacy of current compliance tools. The results indicate that, despite regulatory efforts and projected recycling growth, Europe is unlikely to achieve full raw material autonomy by 2030. Structural dependencies on high-risk countries persist, and recycling alone will not close critical supply gaps.

Reducing electricity and natural gas consumption is increasingly vital for industrial sustainability, especially in energy-intensive sectors such as ceramic sanitary ware production. This study introduces an innovative method to lower energy use during ceramic firing by optimizing raw material formulations. Specifically, we investigated three approaches for fire clay washbasins and vitreous china toilets: altering existing formula ratios and adding fluxing agents (talc, nepheline, or K-feldspar) to both the bulk and glaze. All products were tested for compliance with UNI 4543 standards. The new formulations achieved a 9.87% reduction in shrinkage, leading to decreased raw material consumption and demonstrated enhanced mechanical properties, notably a higher modulus of rupture. Additionally, the low-temperature-melting glaze improved surface quality without affecting color. Life cycle assessment and cost analysis showed up to 4.6% lower environmental impact and 5.61% reduction in production costs, all without requiring production line modifications. The novelty of this work lies in the dual integration of fluxing agents into both body and glaze, enabling lower firing temperatures and superior product performance. Future research will explore the long-term durability of these formulations and investigate further sustainable additives, paving the way for broader energy-efficient innovations in ceramic manufacturing. © The Author(s), under exclusive licence to Springer-Verlag London Ltd.

Keywords: Energy efficiency , Fluxing agents , Green manufacturing , LCA , Vitreous China

This study compares the environmental impact and durability of diamond-coated WC–Co substrates produced via the Hot Filament Chemical Vapor Deposition (HF-CVD) process with different pre-treatment and deposition conditions. Diamond coatings on WC–Co substrates offer significant wear resistance, making them ideal for various industrial applications. However, the process faces challenges due to residual stresses from thermal expansion differences and chemical reactions between cobalt binders and carbon-rich gases, which may cause poorly adherent coatings. Various pre-treatment techniques, such as chemical etching and laser methods, are essential to improve coating adhesion and prevent graphite formation through the selective removal of the binder. This study conducts a comparative Life Cycle Assessment analysis of two industrial pre-treatment methods, Murakami's chemical etching and diode laser treatment, and investigates the environmental impact of using two assistant gases (Nitrogen and Argon) and two Methane/Hydrogen ratios (1% and 2%) in HF-CVD. To overcome the gaps present in previous studies, wear and service life were considered across the six scenarios by defining the wear ratio, obtaining more accurate results in terms of environmental impact. From the ReCiPe midpoint, endpoint and GWP analysis emerges that Chemical Etching with 2% CH4/H2 is the least impactful scenario for the overall deposition process, while laser pretreatments have better ReCiPe indicators if is considered only the pre-treatment phase. The results aim to guide manufacturers in optimizing pre-treatment processes and deposition conditions for enhanced sustainability, taking into account both technical and environmental factors.

Keywords: Chemical etching , Diamond coating , Fibre laser , HF-CVD , LCA , WC–Co

Battery demand is rising rapidly, with a market growth of over 30% annually. EV batteries, projected to make up 90% of the market by 2030, require attention. However, as the market grows, the environmental impacts associated with this expansion also increase significantly. Therefore, a comprehensive approach that includes Life Cycle Assessment (LCA) is deemed necessary. The BatPaC tool is widely utilized in both scientific and industrial contexts for designing and configuring EV batteries through minimal input requirements. This paper seeks to integrate BatPaC with an LCA tool by leveraging the input data and calculations related to various masses and processes considered in BatPaC. This integration aims to provide a more holistic view of the environmental impacts alongside performance and cost assessments. By implementing this approach, stakeholders will be able to enhance eco-design strategies, aligning with the growing regulatory and consumer pressures for greener technologies.

Keywords: BatPaC , Battery Sustainability , Eco-Design , EV Battery , LCA

Purpose: The combination of machine learning (ML) and life cycle-based methodologies is a promising strategy to overcome some of the most relevant issues regarding carbon footprint (CF) calculation/prediction. This research presents an approach and a software tool to apply ML techniques with the aim of solving issues related to data availability in the life cycle inventory (LCI) and improving the accuracy of carbon footprint predictions. The final purpose is to streamline the CF calculation process in the dairy cattle farming sector. Methods: The methodology used in this study consists of three steps: dataset creation, data optimization, and enhanced prediction. Initially, a dataset was compiled from primary sources and literature on LCA studies related to dairy cattle farming. This dataset contained missing data that could affect prediction accuracy. ML techniques were then applied to improve the quality of the data. A ML tool implementing 11 different regression algorithms was developed. This user-friendly tool, easily accessible also to non-ML experts, automatically optimizes data quality by determining the most appropriate algorithm for predicting missing values on a case-by-case basis. In the final step, the same tool was used on the optimized dataset to identify and use the best model for accurate CO2-eq emissions prediction. Results and discussion: An initial assessment was conducted to evaluate the accuracy of a ML model. The results of the test demonstrated that the Gaussian kernel regression model exhibited the highest performance among the available models, with a root mean square error (RMSE) of 18.87%. This value will be used as a threshold for the assessment of the efficacy of the described approach. The tool was then employed to optimize the dataset by predicting missing values. Lastly, the tool was utilized to predict CO2-eq emissions once more, but this time using the optimized dataset for training. As with the previous test, the Gaussian model exhibited the highest performance, but this time with a RMSE of 14.65%. The RMSE was 4% lower, thereby reflecting an increased accuracy in the prediction. Conclusions: The results demonstrate that the presented ML-based approach and tool are effective in predicting lacking inventory data, as well as in improving the accuracy and reliability of CO2-eq emissions prediction in life cycle-based studies. The developed tool makes ML more accessible to non-expert users and facilitates usability. This integration of ML and life cycle-based methodologies shows promising avenues for more accurate and efficient environmental impact assessments in agriculture.

Keywords: Dairy cattle , Data gaps , Data optimization , GHG emissions , Life cycle inventory

Industry 5.0 requires practical methods to translate Augmented Reality (AR) concepts into effective shop floor applications, demonstrating their value to operators. This study introduces a framework for implementing and validating Augmented Reality (AR)-based tools designed to enhance sustainability awareness and assist operators in energy management within industrial settings. The approach combines a reference software architecture for rapid AR deployment with a three-part user-experience assessment, measuring usability (System Usability Scale - SUS), technology acceptance (Technology Acceptance Model - TAM), and cognitive workload (NASA-TLX). To test this framework, an AR-based prototype tool was deployed on enterprise smartphones and evaluated in three scenarios: monitoring service-utility energy consumption, monitoring production equipment, and conducting on- and off-the-job training of the operators. Thirty shop floor professionals completed tasks and provided UX feedback. The results showed good usability (mean SUS 78.4/100), with perceived ease of use and contextual relevance driving technology acceptance. Moreover Nasa TLX analysis indicates mental demand as the predominant factor.

Keywords: Augmented Reality , Employee training , Energy monitoring , Human-centric design , Industry 5.0 , Sustainability , User experience

A meticulous thermo-hygrometric control is essential for various industrial production processes, particularly those involving the painting phases of body-in-white, in which the air temperature and relative humidity in production boots must be limited in strict intervals to ensure the high quality of the final product. However, traditional proportional integrative derivative (PID) controllers may result in non-optimal control strategies, leading to energy wastage due to response delays and unnecessary superheatings. In this regard, predictive models designed for control can significantly aid in achieving all the targets set by the European Union. This paper focuses on the development of a predictive model for the energy consumption of an air handling unit (AHU) used in the paint-shop area of an automotive production process. The model, developed in MATLAB 2024b, is based on mass and energy balances within each component, and phenomenological equations for heat exchangers. It enables the evaluation of thermal powers and water mass flow rates required to process an inlet air flow rate to achieve a target condition for the temperature and relative humidity. The model was calibrated and validated using experimental data of a real case study of an automotive production process, obtaining mean errors of 16% and 31% for the hot and cold heat exchangers, respectively, in predicting the water mass flow rate. Additionally, a control logic based on six regulation thermo-hygrometric zones was developed, which depended on the external conditions of temperature and relative humidity. Finally, as the main outcome, several examples are provided to demonstrate both the applicability of the developed model and its potential in optimizing energy consumption, achieving energy savings of up to 46% compared to the actual baseline control strategy, and external boundary conditions, identifying an optimal trade-off between energy saving and operation feasibility.

Keywords: air conditioning , air handling unit , automotive industrial process , energy optimization , model predictive control , thermodynamic modeling

The decommissioning of offshore oil rigs presents complex environmental challenges and opportunities, particularly in the context of energy transition goals and marine ecosystem protection. This study applies a Life Cycle Assessment (LCA) approach to evaluate the energy and environmental impacts associated with two different decommissioning approaches: full removal and partial removal. The analysis considers greenhouse gas emissions, energy consumption, material recovery, and long-term waste management. The study demonstrates important energy savings through the recovery and recycling of steel, which offsets energy-intensive operations such as cutting and marine transport. In addition, the analysis underscores the potential of integrating decommissioned infrastructure into offshore renewable energy systems, highlighting synergies with circular economy principles and the decarbonization of offshore operations. The findings highlight the importance of site-specific assessments and integrated policy frameworks to guide environmentally sound decommissioning decisions in offshore energy infrastructure. The analysis shows that full removal results in 14,300 kg CO2 eq emissions during cutting and transport, compared to 3090 kg CO2 eq for partial removal. Meanwhile, steel recycling generates environmental benefits of −3.80 × 106 kg CO2 eq for full removal and −1.17 × 106 kg CO2 eq for partial removal.

Keywords: decommissioning , energy transition , LCA , oil rigs , recycling , renewables

Selective laser melting (SLM) allows powder reuse in consecutive cycles, a cost-effective and sustainable practice. However, repeated reuse can degrade powder quality, impacting the final product's mechanical properties. This study presents a methodology combining Failure Mode and Effect Analysis (FMEA) with the Theory of Inventive Problem Solving (TRIZ) to address issues in reusing Ti6AL4V powder in SLM. The approach identifies and mitigates potential failures while leveraging an AI-based chatbot to expedite solution-finding. A case study is implemented to demonstrate the FMEA-TRIZ approach proposed in this paper, showcasing the potential benefits of this innovative methodology.

Keywords: FMEA , generative AI , powder degradation , reused powde , Ti6AL4V , TRIZ

Oscillating Water Column (OWC) devices are a promising technology for harnessing wave energy, utilizing the movement of seawater induced by waves. OWC systems offer renewable energy solutions with minimal environmental impact. The Power Take-Off (PTO) system, crucial for converting pneumatic power into usable energy like electricity, is central to their functionality. This study investigates using a ducted Savonius turbine as the PTO system in an OWC setup. Experimental tests were conducted on a scaled Savonius turbine to assess the turbine efficiency. A CAD model of the setup was created and imported into a Computational Fluid Dynamics (CFD) environment for detailed fluid dynamic analysis, utilizing the Sliding Mesh combined with the Dynamic Fluid Body Interaction (DFBI) technique to simulate turbine motion. Validation of the CFD model was achieved by comparing numerical results with experimental data. A fifth-order Bell-Metha polynomial profile power augmenter was considered to enhance turbine performance by increasing the rotational speed and reducing resistance. This strategy aims to minimize energy loss and operational disturbances by targeting vortex reduction upstream of the turbine. With the validated setup, power augmenters were introduced and a parametric analysis was performed to assess turbine efficiency, using the coefficients of the Bell-Metha profile as parameters. The results were promising for future development in wave energy conversion technology.

Keywords: CFD , OWC , Parametric analysis , Savonius turbine

The maritime sector faces an ecological revolution. In addition to the use of green propulsion systems and reducing the environmental impact of non-recyclable parts, global performance enhancement of the vessels is essential to this change. Hydrofoils are one of the technologies that is significantly impacting this shift: using wings under the hull, may significantly decrease drag and, in the case of powered boats, emissions. Similar to aircraft, the design of hydrofoil wings entails a process that adjusts parameters such as attack angles, profile types, and overall wing geometry to satisfy moments and forces, often requiring extensive design efforts. Through a visual programming language, this paper introduces a new parametric method of designing hydrofoils wings with the aim of speeding up the process. Starting from the 2D airfoil geometry data the logic created makes it easier to edit parametrically the characteristics dimensions of the wings using user-friendly controls and coefficients as inputs of the design. This method in output produces both Brep surfaces and important preliminary information including estimating lift and drag. A case study is presented for the design of hydrofoil moth main wings.

Keywords: Design , Hydrofoil , Parametric

The production of titanium (Ti6Al4V) powder is critical for aerospace, biomedical, and additive manufacturing but poses environmental challenges due to its energy intensity. Existing assessments often rely on static LCAs, offering limited optimization, or employ fragmented parametric models that do not capture full system interdependencies. This study introduces a novel, comprehensive parametric Life Cycle Assessment (LCA) framework for Ti6Al4V powder production, addressing these limitations. Its core methodological innovation lies in the integration of the entire production chain (from mining to sieving) and the simultaneous optimization of technically crucial, interdependent operational parameters, specifically, TiO2 content in slag (typically 0.78–0.90), atomization electrode diameter (0.04–0.10 m), and argon pressure (often ≈5.5 MPa), rather than just parameterizing mass/energy flows as often seen in prior models. This is achieved by linking upstream process quality (e.g., slag composition impacting chlorination energy) to downstream performance and environmental impacts (e.g., atomization energy and waste generation) through empirically-derived relationships based on extensive literature data. The model minimizes environmental impact under user-defined control conditions (target powder diameter, region, impact category). Numerical investigation demonstrates significant impact reduction potential. Crucially, the model quantifies environmental trade-offs between conflicting objectives and reveals critical hotspots, with atomization and chlorination consistently accounting for >70 % of impacts even post-optimization. Energy consumption sensitivity is high, varying over five-fold for key steps based on parameter adjustments.

Keywords: Constraint non-linear programming , Optimization model , Parametric LCA , Ti6Al4V powder production

The Internet of Things (IoT) paradigm has transformed the establishment of interconnected technology settings, paving the way for novel approaches to intelligent service management. Smart Cities and Smart Homes are just a few examples of how this technology might improve people's lives. This study proposes a novel way to Smart Home management that combines the Internet of Things with graphic representation and machine learning techniques. Our technology enables you to collect and analyze data from sensors and other devices, resulting in relevant information and autonomous actions that optimize energy and ventilation control.

Keywords: Energy Optimization , Environmental Monitoring , Internet of things , Smart Home , User-Centered Automation

Extended reality (XR) technologies are no longer peripheral innovations but emerging cornerstones of human–technology interaction across critical sectors. This article takes the position that engineering and healthcare represent the most mature and strategically relevant domains for XR adoption, given their safety-critical nature, intensive training requirements, and strong alignment with the human-centric visions of Industry 5.0 and Healthcare 5.0. We synthesize evidence from product design, manufacturing, training, and patient care to demonstrate how XR is reshaping workflows, skills, and therapeutic practices. Beyond surveying applications, we argue that the future of XR depends on its integration with artificial intelligence, digital twins, and multisensory feedback, converging into systems capable of perceiving, reasoning, and adapting to complex physical and human environments. We contend that widespread adoption will remain limited without open standards, validated protocols, and robust evaluation frameworks addressing safety, interoperability, and data governance. By framing XR as both a technological enabler and a societal imperative, this position article calls for coordinated action among researchers, practitioners, and policymakers to realize XR's role in building sustainable, personalized, and participatory innovation ecosystems.

Keywords: digital industry , extended reality , human-centered innovation , human–computer interfaces/interactions , immersive technology , virtual and augmented reality environments , virtual prototyping , XR for training , XR in healthcare , XR in manufacturing

Current quantitative methods for estimating product-related environmental emissions face limitations in supporting sustainable design, particularly in second-life product strategies. This paper highlights challenges in accurately assessing emissions and environmental impacts under existing regulations, which often fail to reward designs enabling circularity. Through examples of current practices, it underscores methodological ambiguities and regulatory gaps, proposing a research agenda for improved tools and frameworks. These advancements aim to better support the design, production, and certification of sustainable, second-life-ready solutions, fostering more effective environmental impact reduction. Additionally, the paper emphasizes the need for regulatory adaptation to incentivize circular design practices, ensuring a fair evaluation of products conceived for second-life applications.

Keywords: circular economy , design for x (DfX) , ecodesign , systems engineering (SE) , uncertainty

Hyper-redundant manipulators offer high dexterity and manoeuvrability in constrained environments, yet their design must integrate structural efficiency with environmental adaptability. This study presents a co-design framework for lightweight, cable-driven hyper-redundant manipulators optimised for underwater applications, such as the usage in combination with a Remotely Operated Vehicle. Building on a modular architecture of an eight-degree-of-freedom cable-driven manipulator, the methodology integrates Gaussian process regression-based stress prediction and generative design to achieve mass and size reductions, as well as a hydrodynamically efficient shape while ensuring structural integrity under extreme static loads. A 3D-printed module fabricated from Onyx, a carbon fibre-reinforced nylon, achieved near-neutral buoyancy in seawater, validated through submerged testing of a two-module prototype. An external buoyant element design was then provided for manipulators with no inherent buoyancy, accounting for printability, density mismatch between actual and theoretical density, and joint range of motion. This work advances underwater hyper-redundant robot design by combining data-driven optimisation with modular buoyancy strategies and hydrodynamic efficiency, providing a scalable method for fluid environments.

Keywords: Cable-driven , Design , Hyper-redundant , Manipulator , Optimisation , Robot , Underwater