04 Fakultät Energie-, Verfahrens- und Biotechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/5
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Item Open Access Process model and life cycle assessment of biorefinery concept using agricultural and industrial residues for biohydrogen production(2024) Gamero, Edgar; Ruppert, Sophia; Miehe, Robert; Sauer, AlexanderSustainable waste management strategies are urgently needed due to an increasing global population and increased waste production. In this context, biorefineries have recently emerged as a promising approach to valorize waste streams and supply a broad range of products. This study presents the process model and life cycle assessment (LCA) of a biorefinery concept using a novel biochemical method, a so-called “dark photosynthesis” conversion. This process is coupled to a photo-fermentation using microalgae. Overall, the biorefinery concept can produce hydrogen, lutein, β-carotene, and proteins for animal feed. Apple pomace from apple juice production is used as feedstock for the primary conversion step. A process model was created with the process simulation software Aspen Plus ® using experimental and literature data. Results from this model were then used in an LCA. The environmental impacts of the proposed biorefinery concept are relatively high, showing the need for process optimization in several areas. Energy system integration, stream recycling, and higher hydrogen yields are recognized as especially important for improving the environmental performance of this concept. Despite these findings, the model shows the feasibility of implementing the biochemical conversion technologies in a biorefinery concept for effectively utilizing residue streams.Item Open Access Improvement of delivery reliability by an intelligent control loop between supply network and manufacturing(2021) Bauer, Dennis; Bauernhansl, Thomas; Sauer, AlexanderManufacturing companies operate in an environment characterized as increasingly volatile, uncertain, complex and ambiguous. At the same time, their customer orientation makes it increasingly important to ensure high delivery reliability. Manufacturing sites within a supply network must therefore be resilient against events from the supply network. This requires deeper integration between the supply network and manufacturing control. Therefore, this article presents a concept to connect supply network and manufacturing more closely by integrating events from the supply network into manufacturing control’s decisions. In addition to the requirements, the concept describes the structure of the system as a control loop, a reinforcement learning-based controlling element as the central decision-making component, and the integration into the existing production IT landscape of a company as well as with latest internet of things (IoT) devices and cyber-physical systems. The benefits of the concept were elaborated in expert workshops. In summary, this approach enables an effective and efficient response to events from the supply network through smarter manufacturing control, and thus more resilient manufacturing.Item Open Access Complex job shop simulation “CoJoSim” : a reference model for simulating semiconductor manufacturing(2023) Bauer, Dennis; Umgelter, Daniel; Schlereth, Andreas; Bauernhansl, Thomas; Sauer, AlexanderThe manufacturing industry is facing increasing volatility, uncertainty, complexity, and ambiguity, while still requiring high delivery reliability to meet customer demands. This is especially challenging for complex job shops in the semiconductor industry, where the manufacturing process is highly intricate, making it difficult to predict the consequences of changes. Although simulation has proven to be an effective tool for optimizing manufacturing processes, reference data sets and models often produce disparate and incomparable results. CoJoSim is introduced in this article as a reference model for semiconductor manufacturing, along with an associated reference implementation that accelerates the implementation and application of the reference model. CoJoSim can serve as a testbed and gold standard for other implementations. Using CoJoSim, different dispatching rules are evaluated to demonstrate an improvement of almost 15 percentage points in adherence to delivery dates compared to the reference. Findings emphasize the importance of optimizing setup time, particularly in products with high variance, as it significantly impacts adherence to delivery dates and throughput. Moving forward, future applications of CoJoSim will evaluate additional dispatching rules and use cases. Combining CoJoSim with dispatching methods that integrate manufacturing and supply networks to optimize production planning and control through reinforcement-learning-based agents is also planned. In conclusion, CoJoSim provides a reliable and effective tool for optimizing semiconductor manufacturing and can serve as a benchmark for future implementations.Item Open Access A conceptual framework for biointelligent production : calling for systemic life cycle thinking in cellular units(2021) Miehe, Robert; Buckreus, Lorena; Kiemel, Steffen; Sauer, Alexander; Bauernhansl, ThomasA sustainable design of production systems is essential for the future viability of the economy. In this context, biointelligent production systems (BIS) are currently considered one of the most innovative paths for a comprehensive reorientation of existing industrial patterns. BIS are intended to enable a highly localized on-demand production of personalized goods via stand-alone non-expert systems. Recent studies in this field have primarily adopted a technical perspective; this paper addresses the larger picture by discussing the essential issues of integrated production system design. Following a normative logic, we introduce the basic principle of systemic life cycle thinking in cellular units as the foundation of a management framework for BIS. Thereupon, we develop a coherent theoretical model of a future decentralized production system and derive perspectives for future research and development in key areas of management.