Zusammenfassung Deutschland zählt als wichtige Industrie- und Exportnation zu den größten Rohstoff- und Energiekonsumenten der Welt. Hierbei spielt insbesondere die Automobilbranche eine zentrale Rolle. Die Etablierung einer möglichst vollständig kreislauffähigen Wertschöpfung von potenziell umweltfreundlichen Technologien wie Elektrofahrzeugen ist folglich von wachsender Bedeutung. Neben dem aktuellen Stand werden Grenzen und der Entwicklungsbedarf auf dem Weg zu einer kreislauffähigen Wertschöpfung für die Antriebsstränge von Elektrofahrzeugen unter Berücksichtigung des gesamten Lebenszyklus analysiert. Es werden der Aufbau und die Zusammensetzung im Bereich batterieelektrischer Fortbewegungsmittel und ihre Nachhaltigkeit im Hinblick auf den Rohstoffbedarf näher behandelt. Dabei werden Strategien vorgestellt, die während der Nutzungs- und der End-of-Life-Phase ergriffen werden können. Zugleich werden Maßnahmen der kreislauffähigen Wertschöpfung revidiert, die auf designorientiertem Vorgehen basieren.

The rapid transformation of the automotive sector towards electrification is driving fundamental changes in vehicle design and manufacturing systems. Among the most disruptive innovations, giga-casting, the production of large, structurally integral components using high-pressure die casting, has emerged as a potential enabler for next-generation new energy vehicle platforms. This paper presents a preliminary analysis of the giga-casting landscape, integrating market dynamics, industrial adoption, and technological considerations with a focus on laser-based post-processing technologies. The analysis further highlights critical vehicle components suited for giga-casting and outlines the associated manufacturing process chain, including post-processing. As a core element, a comparative technological benchmark is provided for several post-processing technologies, with a dedicated deep dive into two core areas: cutting and cleaning. In the cutting domain, plasma, milling, and die cutting are discussed along laser-based approaches, while in cleaning, chemical treatments are compared to laser processing. The results underline the potential of laser-based methods to enhance efficiency, precision, and sustainability across the giga-casting process chain. Based on these insights, this article intents to serve as a first starting point for laser-based post-processing of giga-casted parts. The findings suggest that giga-casting can reshape new energy vehicle platform architectures, with laser-based post-processing emerging as a key enabler for industrial adoption.

Volatile electricity prices in liberalized markets complicate investment and operations for industrial Energy Flexibility Measures (EFMs). This paper proposes the Price Volatility Threshold (PVT), a percentile-based indicator that quantifies the minimum market price spread required for profitable EFM operations over a specified horizon. The method integrates static and dynamic cost formulations to represent fixed and usage-dependent costs. It defines feasibility as the ratio between observed volatility, the median of daily P95–P05 spreads, and the required spread derived from specific flexibility costs, efficiency losses, and a capture factor. Using German day-ahead market prices (2019–2024), we evaluate four EFMs (ventilation load increase/decrease, electric vehicle (EV) charging, and a battery energy storage system). Results show that ventilation measures are feasible in all years, EV charging attains feasibility under high volatility (2021–2022) and approaches breakeven thereafter, while battery storage remains non-viable in the day-ahead market at current cost levels. The PVT provides a transparent and reproducible metric that links market volatility to techno-economic feasibility, supporting industrial decision-making without the need for heavy parametric modeling.

Compressed air systems are integral to the manufacturing sector, with 70% of U.S. facilities relying on them for operations. Despite their widespread use, compressed air is costly, and compressed air systems often operate inefficiently due to anomalies, including leaks and other defects that remain undetected for extended temporal intervals. Detecting anomalous behavior in compressed air systems enables the early identification of system defects and the implementation of immediate response measures, thereby reducing excessive power consumption. This paper proposes a novel data-driven approach for assessing the health and energysavings potential of compressed air systems by detecting machine idle states and developing key performance indicators based on data extracted through well-established and easily implementable algorithms. Initially, selected algorithms infer idle states in compressed air systems. Key performance indicators are derived from the energy demand of the extracted intervals. Lastly, an energy usage analysis comparing energy demand during baseline temporal intervals and such intervals with elevated key performance indicators in detected idle states was conducted to assess the energy savings potential. Results find rolling-variance and neural-network-classifier superior for idle state detection, demonstrating the potential for practical application in industrial settings. Temporal intervals with elevated key performance indicators corresponded to up to 51.57% higher power consumption, underlining the potential cost savings and improved sustainability if maintenance measures are introduced promptly. The analysis is based on data collected from three real-world manufacturing companies.

Corporate carbon footprints are increasingly required for sustainability reporting and climate strategy development, yet their reliability is often limited by incomplete or insufficiently specific data, particularly in Scope 3. This study investigates data readiness (DR), defined as the availability, reliability, and systematic integration of relevant data, for calculating Corporate Carbon Footprints (CCF). The analysis is based on 17 company projects conducted in Baden-Württemberg, Germany. The results reveal significant variations across emission scopes: average DR is approximately 65% for Scope 1, 51% for Scope 2, and 51% for Scope 3. Within Scope 3, purchased goods and services (3.1) represent the dominant emission driver and the top priority for data improvement. Major data gaps exist in processing of sold products (3.10) and end-of-life treatment of sold products (3.12), where reliable activity data and emission factors are often lacking. Differences also emerge between company sizes: small and medium-sized enterprises tend to have higher data quality but less comprehensive datasets, whereas large companies achieve greater data completeness but face more complex coordination challenges. A maturity model provides scope- and category-specific guidance for data collection and calculation procedures. Key recommendations include the early implementation of Scope 3 materiality analyses, clear data ownership, ERP-based tracking of quantity-related activity data, supplier engagement, and the use of hybrid accounting approaches. Overall, systematic data development and robust governance of data processes are essential to enhance DR and enable emissions accounting to serve as a strategic management instrument.

Effizienter Klimaschutz ist angesichts unzureichender Emissionsreduktionen und zunehmender wirtschaftlicher Herausforderungen von hoher Relevanz. Durch die Entwicklung einer Methodik zur Identifikation sowie der ökonomisch-ökologischen Bewertung und Priorisierung von Dekarbonisierungsmaßnahmen werden wirtschaftlich effiziente Klimastrategien sichergestellt. Das Vorgehen basiert auf einer Erweiterung von Emissionsvermeidungskostenkurven durch szenariengestützte Zukunftsbetrachtung.

Der Beitrag beschreibt ein integriertes Vorgehen, das die Energiewertstrommethode mit der industriellen Energiesystemplanung verknüpft. Zuerst werden Energieeffizienzpotenziale prozessspezifisch identifiziert und geeignete Verbesserungsmaßnahmen abgeleitet. Darauf aufbauend werden Energiesystemvarianten modelliert, bewertet und zielkonform dimensioniert. So können Überdimensionierung vermieden und die Energieeffizienz sowie Wirtschaftlichkeit eines Produktionsstandortes verbessert werden.

Disassembly is vital for the circular economy, revealing a product’s structure and sequence. It enables assessments of reparability (DIN EN 45554), remanufacturing potential (DIN SPEC 91472), automation (TAA/NA method), and ease of disassembly (Peony Model or ReDiM method). However, to date, diverse data collection standards are used for each of these methods. These standards usually neglect the process forces and torques during manual disassembly, the screwdriver torque data, and the visual documentation. To address this gap, we developed and evaluated a cross-platform application (compatible with multiple operating systems on both mobile devices and desktops) for structured documentation of various manual disassembly process steps. Thereafter, scores for product reparability, remanufacturing capability, automation potential of disassembly, and ease of disassembly are calculated using the same data structure. This cross-platform application ofers a comprehensive way to collect and analyze the gained disassembly data. This solution provides a comprehensive approach for collecting and analyzing disassembly data, which can also be leveraged for machine learning applications based on force-torque data and for designing robotic disassembly cells. This application is tested in the following example of disassembly: a pump and a refrigerator.

The design of distributed industrial energy systems requires the simultaneous optimization of economic, environmental, and energetic objectives. Although evolutionary algorithms (EAs) are well-suited to approximate Pareto-optimal solution sets for such problems, their selection and parameterization are rarely addressed systematically in the energy system literature. This study evaluates three established multi-objective EAs, namely non-dominated sorting genetic algorithm (NSGA-III), strength-based Pareto EA (SPEA2), and multi-objective EA based on decomposition (MOEA/D), for a real industrial four-objective planning problem considering investment cost, net present value, CO₂ emissions, and degree of self-sufficiency. Each candidate system design is evaluated using a deterministic mixed integer linear programming (MILP) based operating model to ensure operational feasibility. Based on an initial benchmark under identical planning conditions, NSGA-III is identified as the most robust baseline algorithm. A two-stage, problem-specific parameter tuning procedure is then applied to separately analyze convergence- and diversity-oriented search behavior. Subsequently, two structural extensions, a two-phase NSGA-III system and an archive-assisted Two-stage Evolutionary Framework for Multi-objective Optimization (TEMOF) extension, are evaluated. The results show that problem-specific tuning substantially improves Pareto front quality compared with standard configurations. Moreover, the two structural extensions provide complementary benefits: the two-phase system improves local convergence and front density, whereas TEMOF enhances global spread and the exploration of extreme trade-off regions. By combining both extensions, these advantages can be integrated, resulting in a Pareto approximation that benefits from both stronger local refinement and broader global coverage. The study provides practical guidance for configuring EAs in optimization-based industrial energy system planning.

Lycopene is a red tetraterpenoid-carotenoid pigment in fruits and vegetables, highly available in tomatoes, and has a lipophilic nature, i.e., insoluble in water. A unique feature of this carotenoid is that it allows for various applications, such as nutritional supplements due to its antioxidant properties, age-defying treatments, and a natural red colorant for food and beverages. Its advantages result in an expanded global market size, expected to be around 215 million by 2033. The environmental impacts of conventional lycopene extraction from tomato by-products are investigated with a life cycle assessment. The Environmental Footprint method, developed by the European Commission, is applied to calculate the impact categories for the process. Various scenarios and extraction methods are considered to assess the impact of different variables and identify hotspots. The electricity source is a hotspot on the laboratory scale. Otherwise, the solvents used for the extraction have the most significant environmental impact. Therefore, methods involving solvents are the most harmful. Some measures to reduce the environmental impact include recycling solvents or using other low-carbon substances for the extraction.

The textile industry significantly contributes to global greenhouse gas emissions. Companies are thus increasingly forced to calculate carbon footprints at both the corporate (Corporate Carbon Footprint, CCF) and product (Product Carbon Footprint, PCF) level. While both approaches are commonly calculated separately, integrating them into a single analysis provides a more comprehensive understanding of emissions and facilitate targeted reduction measures. This article proposes an approach to calculate the CCF in the textile industry, which integrates a methodology for PCF calculation via mathematical optimization for homogeneous product portfolios. The methodology exploits synergies between PCF and CCF calculations to ensure a consistent and efficient emissions analysis. The practical application of this model is demonstrated through a case study of the German textile SME 3FREUNDE, which is based on primary data from the supply chain and production processes. The results show that an integrated calculation reduces the burden of environmental assessment and provides a solid basis for effective decarbonization strategies at the product and company level. This helps companies meet regulatory requirements and gain a sustainable competitive advantage.

The accelerating transition toward electromobility necessitates effective strategies to reduce greenhouse gas emissions and secure access to critical raw materials. Lithium-ion batteries, as the dominant energy storage technology in electric vehicles, play a central role in this transition but pose substantial challenges at end-of-life. Establishing robust and local recycling structures can mitigate raw material dependencies, strengthen supply chain resilience, and lower the overall environmental footprint of battery electric vehicles. This study provides a comprehensive examination of the emerging battery recycling landscape in Germany, one of Europe’s most influential automotive markets and a country characterized by limited domestic raw material availability. To achieve this, a systematic literature analysis is combined with field research, enabling an integrated assessment of both the scientific discourse and the industrial ecosystem. Firstly, the results include a descriptive analytics component that maps publication trends and industry visibility. Secondly, the article offers a structured content analysis that identifies key technological, economic, regulatory, and ecological emphases within the literature. Thirdly, the study encompasses an in-depth company analysis that elucidates the technological characteristics, material recovery strategies, and company figures of recycling actors in Germany. Building on these findings, the study proposes a recommendation framework that outlines targeted actions for research, industry, and policymakers. This framework aims to support the strategic development and scaling of the German battery recycling industry in the coming years and can serve as a blueprint for analogous investigations in other European countries.

The quality of weld seams is critical for the integrity and longevity of welded structures. Operator errors during the gas metal arc welding process can lead to significant quality deviations and defects, affecting the safety and reliability of manufactured products. This study presents a comprehensive approach for detecting operator errors by analyzing welding current, arc voltage, and wire feed speed data. We developed models capable of identifying operator errors with high accuracy by applying advanced feature engineering techniques and utilizing machine learning algorithms such as support vector machines and artificial neural networks. The primary research question addresses how operator errors in the gas metal arc welding process can be detected in real time using existing sensor data without the need for additional hardware. The paper is structured to provide a thorough background, detailed methodology, extensive results, and critical discussion. The results demonstrate that real-time monitoring and error detection are feasible without additional sensor technology, enhancing production quality and efficiency.

There is a lack of profound information concerning the economic and ecological performance of available technology options for hydrogen production, preventing investments in new production facilities. This article provides a database with economic and ecological parameters for nine hydrogen production technologies (alkaline-, proton exchange membrane-, high-temperature electrolysis, steam-, autothermal reforming, coal-, biomass gasification, supercritical water gasification, and dark fermentation) and three carbon capture technologies. Additionally, periphery such as biomass drying and product gas compression is considered. The standardized data is introduced into a parameterized calculation model that allows the assessment of the net present value, greenhouse gas (GHG) emissions over the considered lifetime, and the GHG emissions per kg hydrogen produced for each technology combination. The parameterization enables adopters of the model to consider location-specific circumstances by inserting temporal- and spatial-specific cost- and emission factors. The calculation model is translated into a low-threshold calculation tool provided as Supplementary Material.

This paper presents a trustworthy reinforcement learning approach for the control of industrial compressed air systems. We develop a framework that enables safe and energy-efficient operation under realistic boundary conditions and introduce a multi-level explainability pipeline combining input perturbation tests, gradient-based sensitivity analysis, and SHAP (SHapley Additive exPlanations) feature attribution. An empirical evaluation across multiple compressor configurations shows that the learned policy is physically plausible, anticipates future demand, and consistently respects system boundaries. Compared to the installed industrial controller, the proposed approach reduces unnecessary overpressure and achieves energy savings of approximately 4 % without relying on explicit physics models. The results further indicate that system pressure and forecast information dominate policy decisions, while compressor-level inputs play a secondary role. Overall, the combination of efficiency gains, predictive behavior, and transparent validation supports the trustworthy deployment of reinforcement learning in industrial energy systems.

Die Studie untersucht, wie ressourcenschonende Strategien zur Hyperparameteroptimierung die Genauigkeit und die Laufzeit industrieller Lastprognosen beeinflussen. Mit einem Taguchi-Design wurden zwei Modelle des maschinellen Lernens mit verschiedenen vereinfachenden Verfahren, sogenannten Pruning- und Subsampling-Methoden, getestet. Zufälliges Subsampling auf 30 % der Daten und Hyperband-Pruning erzielten teils bessere Prognosen bei deutlich geringerem Rechenaufwand.

Fostering a local and circular bioeconomy is expected to result in increased resilience and decreased environmental impacts. By converting biobased residuals into valuable products, biowaste-to-X (B2X) technologies are enablers of such value creation. However, as biowaste is characterized by limited availability, its use must be optimized based on local supply and demand. This article presents a site level optimization model for the design of regional bioeconomic cluster nodes based on economic and ecological key performance indicators. Decisions concern the capacity of feasible technology combinations to be installed and transport of biomass from supply regions and products to demanding locations respectively. As B2X technologies are currently an emergent research field, the model is extendable to allow the consideration of future technology developments. Furthermore, framework conditions of the system under consideration are adjustable to enable a location-and time-independent application. The work addresses research, private investors, companies and municipalities, planning to install B2X facilities on a specified property to establish a cost-efficient local circular bioeconomy. The model is validated with the use case of hydrogen production. This includes B2X-but also conventional technologies to prevent dogmatic decisions and enable the identification of an optimum based on two dimensions of sustainability. Case studies with two companies in Baden-W & uuml;rttemberg, Germany, exemplify that specific framework conditions are necessary to turn biobased hydrogen production with carbon capture and storage into a favorable technology-combination. A sensitivity analysis outlines the costs for water, electricity, biomass, and workers, as well as the selling price for hydrogen and carbon removal certificates as pivotal parameters.

Planning industrial energy systems requires balancing conflicting economic, environmental, and technical objectives. Multi-Criteria Decision-making (MCDM) methods can support this process, yet it remains unclear how strongly the choice of method affects the final recommendation. This study, therefore, examines the extent to which preferred energy system configurations vary across MCDM methods and how robust these selections are under uncertainty in criteria weights. The analysis is based on a real industrial case study in southern Germany. First, about 10,000 Pareto-optimal energy system configurations were generated using NSGA-III and a Python-based energy system model. These alternatives were then evaluated with seven MCDM methods: SAW, WPM, MAVT, TOPSIS, VIKOR, PROMETHEE II, and ELECTRE III. Criteria weights were derived from expert judgments using the Analytic Hierarchy Process (AHP). To assess robustness of the chosen industrial energy systems, the weights were perturbed through Dirichlet-based Monte Carlo simulation and evaluated using Stochastic Multicriteria Acceptability Analysis. The results show that the preferred energy systems differ only marginally across methods. All methods consistently identify a similar technological core comprising a natural gas boiler, pellet boiler, photovoltaic system, battery storage, and thermal storage. Differences occur mainly in the sizing of individual components, especially photovoltaic and thermal storage capacities. SAW, WPM, and TOPSIS show particularly stable recommendations, whereas VIKOR is more sensitive to weight changes. Overall, the study indicates that simple compensatory methods can already provide robust and practically relevant decision support for industrial energy system planning.

Zusammenfassung Vor dem Hintergrund endlicher Ressourcen und unsicherer Lieferketten wird die kreislauffähige Wertschöpfung immer wichtiger. Doch erfordern kreislauffähige Geschäftsmodelle entlang des gesamten Lebenszyklus von Produkten Informationen über Art und Qualität der enthaltenen Rohstoffe, die Nutzungshistorie sowie den aktuellen Zustand. Die Erfassung und Verarbeitung dieser Informationen sind momentan ein Haupthindernis bei der Umsetzung der kreislauffähigen Wertschöpfung. Die Digitalisierung bietet das Potenzial, diese Informationsdefizite zu überwinden. So ermöglichen digitale Technologien es Unternehmen, Ressourcen effizienter zu nutzen, indem sie den gesamten Lebenszyklus von Produkten analysieren. Auch das Recycling und die Wiederverwendung kann durch die Nachverfolgung von Materialien und dem Austausch von Informationen entlang der Lieferkette unterstützt werden. Darüber hinaus fördert die Digitalisierung die Entwicklung von innovativen Geschäftsmodellen, die den Übergang zu einer nachhaltigen kreislauffähigen Wertschöpfung beschleunigen können.

Zusammenfassung Trotz der angestrebten Entkopplung von Wirtschaftswachstum und Ressourcenverbrauch ist eine Einhaltung der gesteckten Klimaziele unter den derzeitigen Rahmenbedingungen nicht absehbar. Grund dafür ist die weitgehende Linearität der industriellen Wertschöpfung. Demgegenüber steht die kreislauffähige Wertschöpfung, deren Prozesse und Stakeholder ganzheitlich darauf ausgelegt sind, Produkte im Kreislauf zu führen und dabei die verschiedenen Strategien zur Kreislaufführung kaskadenartig zu durchlaufen. Dieses Kapitel führt in das Konzept der kreislauffähigen Wertschöpfung ein, definiert grundlegende Begrifflichkeiten, zeigt Chancen für Unternehmen auf und diskutiert gegenwärtige Herausforderungen. Dabei wird deutlich, dass die kreislauffähige Wertschöpfung ein zentraler Enabler für die Realisierung einer nachhaltigen Wirtschaft ist. Im Zusammenspiel mit zielgerichteten Maßnahmen zur Erhöhung der Effizienz und Etablierung von Suffizienz ergeben sich viele Vorteile für Gewerbe und Gesellschaft.