Improved Process Understanding Research Articles

Dynamic Multiblock Regression for Process Modelling

ABSTRACTThe study introduces three novel strategies for incorporating capabilities for dynamic modelling into multiblock regression methods by integrating sequentially orthogonalised partial least squares (SO‐PLS) with different dynamic modelling techniques. The study evaluates these strategies using synthetic datasets and an industrial example, comparing their performance in predictive ability, identification of process dynamics, and quantification of block contributions. Results suggest that these approaches can effectively model the dynamics with performance comparable to state‐of‐the‐art methods, providing, at the same time, insight into the dynamic order and block contributions. One of the strategies, sequentially orthogonalised dynamic augmented (SODA)–PLS, shows promise by ensuring that redundant information in the time dimension is not included, resulting in simpler and more easily interpretable dynamic models. These multiblock dynamic regression strategies have potential applications for improved process understanding in industrial settings, especially where multiple data sources and inherent time dynamics are present.

ECOSENSE - Multi-scale quantification and modelling of spatio-temporal dynamics of ecosystem processes by smart autonomous sensor networks

Global climate change threatens ecosystem functioning worldwide. Forest ecosystems are particularly important for carbon sequestration, thereby buffering climate change and providing socio-economic services. However, recurrent stresses, such as heat waves, droughts and floods can affect forests with potential cascading effects on their carbon sink capacity, drought resilience and sustainability. Knowledge about the stress impact on the multitude of processes driving soil-plant-atmosphere interactions within these complex forest systems is widely lacking and uncertainty about future changes extremely high. Thus, forecasting forest response to climate change will require a dramatically improved process understanding of carbon and water cycling across various temporal (minutes to seasons) and spatial (leaf to ecosystem) scales covering atmosphere, biosphere, pedosphere and hydrosphere components. Many relevant processes controlling carbon and water exchange occur at small scales (e.g. rhizosphere, single leaf) with a high spatial and temporal variability, which is poorly constrained. However, interactions and feedback loops can be key players that amplify or dampen a system’s response to stress. Moreover, spatial and temporal scaling rules for these non-linear processes in structurally and functionally diverse ecosystems are unknown. Legacy effects, for example, altered response after previous stress and retarded recovery of forests after climate extremes, are not captured in state-of-the-art models. Currently, we are lacking the appropriate and interconnected measurement, data assimilation and modelling tools allowing for a comprehensive, real-time quantification of key processes at high spatio-temporal coverage in heterogeneous environments. Moreover, since climate impacts are highly unpredictable with respect to timing and location, future research will require novel mobile, easily deployable and cost-efficient approaches. ECOSENSE, therefore, assembles expertise from environmental and engineering sciences, both being excellently paired at the University of Freiburg. Our interdisciplinary research project will investigate all relevant scales in a next-generation ecosystem research assessment (ECOSENSE). Our vision is to detect and forecast critical changes in ecosystem functioning, based on the understanding of hierarchical process interaction. In the first phase, ECOSENSE will explore these process interactions by investigating pools and fluxes of water and carbon, i.e. CO2 exchange, isotope discrimination and volatile organic compounds (VOC), as well as stress indicators by remotely and in situ sensed chlorophyll fluorescence. To address these research tasks, ECOSENSE will develop, implement and test a distributed, autonomous, intelligent sensor network, based on novel microsensors tailored to the specific needs in remote and harsh forest environments. They will measure the spatio-temporal dynamics of ecosystem pools and fluxes in a naturally complex structured forest system with minimal physiological impact. Measured data will be transferred in real-time into a sophisticated database, which will be explored for process analysis, conducted by Artificial Intelligence and close to real-time process-based ecosystem models for now- and forecasting applications. Thereby, ECOSENSE will: i) break new ground for integrative ecosystem research by identifying hierarchies and interactions of abiotic and physiological processes of forest carbon and water exchange, ii) provide a profound understanding of complex ecosystem responses to environmental stressors and iii) enable the prediction of process-based alterations in ecosystem functioning and sustainability. Our novel ECOSENSE toolkit, tested and validated in controlled climate extreme experiments and our ECOSENSE Forest, will open new horizons for rapid assessment in vast and remote ecosystems. Thereby, ECOSENSE will allow for a unique avenue of data acquisition and, consequently, for unprecedented scale-crossing ecosystem understanding and modelling.

In-line image analysis of particulate processes with deep learning: Optimizing training data generation via copy-paste augmentation

Monitoring particle properties directly in the process using in-situ microscopes can provide valuable input for control loops, improve process understanding, and facilitate process optimization. However, obtaining reliable information from these images is still challenging, particularly for higher solids concentrations or agglomerating systems. Recent studies have successfully applied deep learning models to extract particle characteristics from in-situ image data. Despite these advances, the problem of generating training data has not been properly addressed. Manual annotation is time-consuming and prone to bias due to high particle counts, particle overlap, and out-of-focus objects. This paper introduces a new approach to generating training data for segmentation models by combining conventional segmentation methods with copy-paste augmentation. A case study was conducted in which depth filtration experiments were performed using irregularly shaped alumina particles. An instance segmentation model trained on data created with the proposed approach successfully detected and characterized particles, even at high solids concentrations.

Exploring the Potential of Artificial Intelligence as a Facilitating Tool for Formulation Development in Fluidized Bed Processor: a Comprehensive Review.

This in-depth study looks into how artificial intelligence (AI) could be used to make formulation development easier in fluidized bed processes (FBP). FBP is complex and involves numerous variables, making optimization challenging. Various AI techniques have addressed this challenge, including machine learning, neural networks, genetic algorithms, and fuzzy logic. By integrating AI with experimental design, process modeling, and optimization strategies, intelligent systems for FBP can be developed. The advantages of AI in this context include improved process understanding, reduced time and cost, enhanced product quality, and robust formulation optimization. However, data availability, model interpretability, and regulatory compliance challenges must be addressed. Case studies demonstrate successful applications of AI in decision-making, process outcome prediction, and scale-up. AI can improve efficiency, quality, and cost-effectiveness in significant ways. Still, it is important to think carefully about data quality, how easy it is to understand, and how to follow the rules. Future research should focus on fully harnessing the potential of AI to advance formulation development in FBP.

Multivariate data analysis of process parameters affecting the growth and productivity of stable Chinese hamster ovary cell pools expressing SARS-CoV-2 spike protein as vaccine antigen in early process development.

The recent COVID-19 pandemic revealed an urgent need to develop robust cell culture platforms which can react rapidly to respond to this kind of global health issue. Chinese hamster ovary (CHO) stable pools can be a vital alternative to quickly provide gram amounts of recombinant proteins required for early-phase clinical assays. In this study, we analyze early process development data of recombinant trimeric spike protein Cumate-inducible manufacturing platform utilizing CHO stable pool as a preferred production host across three different stirred-tank bioreactor scales (0.75, 1, and 10 L). The impact of cell passage number as an indicator of cell age, methionine sulfoximine (MSX) concentration as a selection pressure, and cell seeding density was investigated using stable pools expressing three variants of concern. Multivariate data analysis with principal component analysis and batch-wise unfolding technique was applied to evaluate the effect of critical process parameters on production variability and a random forest (RF) model was developed to forecast protein production. In order to further improve process understanding, the RF model was analyzed with Shapley value dependency plots so as to determine what ranges of variables were most associated with increased protein production. Increasing longevity, controlling lactate build-up, and altering pH deadband are considered promising approaches to improve overall culture outcomes. The results also demonstrated that these pools are in general stable expressing similar level of spike proteins up to cell passage 11 (~31 cell generations). This enables to expand enough cells required to seed large volume of 200-2000 L bioreactor.

A quality-by-design approach to improve process understanding and optimise the production and quality of CAR-T cells in automated stirred-tank bioreactors.

Ex vivo genetically-modified cellular immunotherapies, such as chimeric antigen receptor T cell (CAR-T) therapies, have generated significant clinical and commercial outcomes due to their unparalleled response rates against relapsed and refractory blood cancers. However, the development and scalable manufacture of these novel therapies remains challenging and further process understanding and optimisation is required to improve product quality and yield. In this study, we employ a quality-by-design (QbD) approach to systematically investigate the impact of critical process parameters (CPPs) during the expansion step on the critical quality attributes (CQAs) of CAR-T cells. Utilising the design of experiments (DOE) methodology, we investigated the impact of multiple CPPs, such as number of activations, culture seeding density, seed train time, and IL-2 concentration, on CAR-T CQAs including, cell yield, viability, metabolism, immunophenotype, T cell differentiation, exhaustion and CAR expression. Initial studies undertaken in G-Rex® 24 multi-well plates demonstrated that the combination of a single activation step and a shorter, 3-day, seed train resulted in significant CAR-T yield and quality improvements, specifically a 3-fold increase in cell yield, a 30% reduction in exhaustion marker expression and more efficient metabolism when compared to a process involving 2 activation steps and a 7-day seed train. Similar findings were observed when the CPPs identified in the G-Rex® multi-well plates studies were translated to a larger-scale automated, controlled stirred-tank bioreactor (Ambr® 250 High Throughput) process. The single activation step and reduced seed train time resulted in a similar, significant improvement in CAR-T CQAs including cell yield, quality and metabolism in the Ambr® 250 High Throughput bioreactor, thereby validating the findings of the small-scale studies and resulting in significant process understanding and improvements. This study provides a methodology for the systematic investigation of CAR-T CPPs and the findings demonstrate the scope and impact of enhanced process understanding for improved CAR-T production.

Understanding complex processes better-A case study on increasing patient safety and efficiency in a central operating room

Various professional groups are involved in the daily work of the central operating room with the aim of providing the best possible treatment for each individual using modern medical technology (sociotechnical system) in acost-effective manner. Ensuring perioperative patient safety is of particular importance. At the same time, the efficient use of the central operating room is essential for the economic success of ahospital. Preoperative preparation is acomplex process with many substeps that are often difficult to manage. Historically, the focus has been on retrospective learning from errors and incidents. More recent approaches take asystemic view. Acentral idea is to consider the mostly positive course of treatment and the adjustments to daily work that are currently required by the people involved (Safety-II). By taking greater account of how the many components of the system interact, processes can be better understood and specific measures derived. This strengthens the system's ability to adapt to changes and disturbances, thus ensuring that goals are achieved. The functional resonance analysis method (FRAM) is an internationally recognized method for modelling work as done compared to work as imagined. This paper presents the application of FRAM to preoperative preparation in a major regional hospital. Is FRAM suitable for improving process understanding in preoperative preparation? An interdisciplinary project team identified relevant functions of preoperative preparation through document analysis and walkthroughs. Based on this, more than 30guided interviews were conducted with functionaries. The results were presented graphically and specific information, such as safety-related statements or reasons for the variability of functions, were also presented textually. In the next phase, statements were evaluated and compared with the target model and the job descriptions. The FRAM revealed the process as acomplex network of relationships. During the modelling process, avarying degree of centrality and variability of certain functions became apparent. From the observations, the project team selected those with high relevance for patient safety and for the efficiency of the overall process in order to prioritize starting points for deriving measures to increase resilience. These starting points relate either to single functions, such as surgical site marking or to multiple functions that are variable in their execution, such as delays due to nonsynchronized duty times. The FRAM conducted provides valuable new insights into the functioning of complex sociotechnical systems that go far beyond classical linear methods. The awareness of operational processes gained and the resulting dynamic view of interactions within the system enable specific measures to be derived that promote resilient behavior and reduce critical variability, thus contributing to increased patient safety and efficiency.

A SYSTEMATIC REVIEW: EXPLORATION OF PROCESS ANALYTICAL TECHNOLOGY TECHNIQUES (PAT) AND THEIR MULTIFACETED ADVANTAGES IN INDUSTRIAL PROCESSES

FDA initiated the PAT technology framework in the year of 2004 with the guidelines of “A framework of innovative pharmaceutical development, manufacturing and quality assurance. With that, the International Council for Harmonisation has also initiated continuous process verification to overcome the limitations of traditional methods and improve the understanding of the process and quality of the product throughout the product lifecycle. Since the year of implementation, the advancement of analytical and chemometric tools has evolved to deliver consistent quality products by understanding their process and product performance. However, the pharmaceutical industry was lacking in this technicality and implementation of highly regulated specifications. To this respect, we have stated some of the PAT tools, including NIR, Raman and Terahertz spectroscopy, as they will transfer to the futuristic prospects of analyzing the drug product with non-destructive, improved process understanding, real-time monitoring, and enhanced data integrity. This review article emphasizes the importance of PAT technology with different monitoring processes with their historical background and regulatory framework. Special attention was given to strategies, challenges, opportunities, and the compatibility of PAT tools with data fusion. Further, this will give a high-priority disciplinary scientific topic to Pharma 4.0.

Consistent Modeling of Transport Processes and Travel Times—Coupling Soil Hydrologic Processes With StorAge Selection Functions

AbstractUnderstanding the transport processes and travel times of pollutants in the subsurface is crucial for an effective management of drinking water resources. Transport processes and soil hydrologic processes are inherently linked to each other. In order to account for this link, we couple the process‐based hydrologic model RoGeR with StorAge Selection (SAS) functions. We assign to each hydrological process a specific SAS function (e.g., power law distribution function). To represent different transport mechanisms, we combined a specific set of SAS functions into four transport model structures: complete‐mixing, piston flow, advection‐dispersion and advection‐dispersion with time‐variant parameters. In this study, we conduct modeling experiments at the Rietholzbach lysimeter, Switzerland. All modeling experiments are benchmarked with HYDRUS‐1D. We compare our simulations to the measured hydrologic variables (percolation and evapotranspiration fluxes and soil water storage dynamics) and the measured water stable isotope signal (18O) in the lysimeter seepage for a period of ten years (1997–2007). An additional virtual bromide tracer experiment was used to benchmark the models. Additionally, we carried out a sensitivity analysis and provided Sobol indices for hydrologic model parameters and SAS parameters. Our results indicate that the advection‐dispersion transport model produces the best results. And thus, advective‐dispersive transport processes play a dominant role at Rietholzbach lysimeter. Our modeling approach provides the capability to test hypotheses of different transport mechanisms and to improve process understanding and predictions of transport processes. Overall, the combined model allows a very effective simulation of combined flux and transport processes at various temporal and spatial scales.

In situ process analytical technology for real time viable cell density and cell viability during live-virus vaccine production

Viable cell density (VCD) and cell viability (CV) are key performance indicators of cell culture processes in biopharmaceutical production of biologics and vaccines. Traditional methods for monitoring VCD and CV involve offline cell counting assays that are both labor intensive and prone to high variability, resulting in sparse sampling and uncertainty in the obtained data. Process analytical technology (PAT) approaches offer a means to address these challenges. Specifically, in situ probe-based measurements of dielectric spectroscopy (also commonly known as capacitance) can characterize VCD and CV continuously in real time throughout an entire process, enabling robust process characterization. In this work, we propose in situ dielectric spectroscopy as a PAT tool for real time analysis of live-virus vaccine (LVV) production. Dielectric spectroscopy was collected across 25 discreet frequencies, offering a thorough evaluation of the proposed technology. Correlation of this PAT methodology to traditional offline cell counting assays was performed, in which VCD and CV were both successfully predicted using dielectric spectroscopy. Both univariate and multivariate data analysis approaches were evaluated for their potential to establish correlation between the in situ dielectric spectroscopy and offline measurements. Univariate analysis strategies are presented for optimal single frequency selection. Multivariate analysis, in the form of partial least squares (PLS) regression, produced significantly higher correlations between dielectric spectroscopy and offline VCD and CV data, as compared to univariate analysis. Specifically, by leveraging multivariate analysis of dielectric information from all 25 spectroscopic frequencies measured, PLS models performed significantly better than univariate models. This is particularly evident during cell death, where tracking VCD and CV have historically presented the greatest challenge. The results of this work demonstrate the potential of both single and multiple frequency dielectric spectroscopy measurements for enabling robust LVV process characterization, suggesting that broader application of in situ dielectric spectroscopy as a PAT tool in LVV processes can provide significantly improved process understanding. To the best of our knowledge, this is the first report of in situ dielectric spectroscopy with multivariate analysis to successfully predict VCD and CV in real time during live virus-based vaccine production.

Digital design of an integrated purification system for continuous pharmaceutical manufacturing

In this work dynamic models of the continuous crystallization, filtration, deliquoring, washing, and drying steps are introduced, which are developed in the open-source pharmaceutical modeling tool PharmaPy. These models enable the simulation and digital design of an integrated continuous two-stage crystallization and filtration-drying carousel system. The carousel offers an intensified process that can manufacture products with tailored properties through optimal design and control. Results show that improved crystallization design enhances overall process efficiency by improving critical material attributes of the crystal slurry for downstream filtration and drying operations. The digital design of the integrated process achieves enhanced productivity while satisfying multiple design and product quality constraints. Additionally, the impact of model uncertainty on the optimal operating conditions is investigated. The findings demonstrate the systematic process development potential of PharmaPy, providing improved process understanding, design space identification, and optimized robust operation.

Nature reserves (zapovedniks) in the Volga catchment: protection, research and management

AbstractIn the Russian Federation, the protection category “Zapovednik” represents a strictly protected area, where the access is restricted and the aim is to keep the area “forever wild”. We provide an overview about the organization of these nature reserves and highlight their contribution to fundamental research. Aquatic ecosystems in zapovedniks provide pristine sites of great importance for long-term monitoring and research, which fosters process understanding regarding interannual variability and development of aquatic coenosis. We present a long term monitoring and research project about abiotic and biotic parameters in the Central Forest State Nature Biosphere Reserve. Examples from the Privolzhskaya Lesostep Nature Reserve include monitoring of invasive fish as well as analyses about the influence of beavers on fish. The Astrakhan Biosphere Nature Reserve in the Volga delta is a hotspot for fish biodiversity; thus, there the state nature reserve security service plays an important role to avoid poaching. Our case studies from the Volga Basin exemplify the structure and function of nature reserves and highlight their contribution to research, protection and management of aquatic ecosystems. Long-term datasets from the zapovedniks can (i) improve process understanding, (ii) ensure protection of aquatic systems and sustainable water management and (iii) support knowledge-based decisions.

Sap flux and stable isotopes of water show contrasting tree water uptake strategies in two co‐occurring tropical rainforest tree species

AbstractThe short‐term dynamics of tree water use strategies for neighbouring co‐occurring species are poorly understood. Here, we quantify the high frequency changes in water sources and sap flux patterns of two commonly co‐occurring tropical rainforest tree species: Dendrocnide photinophylla (Kunth; Chew) and Argyrodendron peralatum (F.M. Bailey; Edlin ex J.H. Boas). A combination of continuous sap flux measurements and hourly sampling of xylem water stable isotope composition (δD and δ18O) were used to observe water use strategies during a 24‐h transpiration cycle. Sap flux ranged between 2.82 and 28.50 L day−1, with A. peralatum recording a 67% higher rate than D. photinophylla. For both tree species, sap flux increased with tree size and diurnal sap flux increase resulted in more isotopically enriched xylem water. A Bayesian Mixing Model analysis, which used sampled soil water isotopic composition from five soil depths ranging from of 0 to 1 m, revealed that D. photinophylla primarily used water from very shallow depth or soil surface layer (2–60 cm), while A. peralatum sourced its water mostly from deeper layers (60–100 cm). We propose that these differences in species' water consumption patterns are related to plant water storage capacity and wood anatomical features. This research demonstrates that combining xylem isotope composition and sap flux measurements can help reveal species‐specific water use strategies, which can be beneficial for improved process understanding in ecohydrological modelling.

A systematic literature review on machine learning applications at coal-fired thermal power plants for improved energy efficiency

ABSTRACT Power generation comprises high environmental and ecological impacts. The global power industry is under pressure to develop more efficient ways to operate and reduce the impacts of inherent process variability. With the rapid development of technologies within the energy sector, large volumes of data are available due to in-time operational measurements. With increased computer processing capabilities, machine learning is applied to explore these in-time operational measurements for improved process understanding. This research paper investigates machine learning algorithms for energy efficiency improvement at coal-fired thermal power plants by conducting a systematic literature review. This research is essential since it provides guidelines for applying machine learning towards sustainable energy supply and improved decision-making. Subsequently, efficient processes result in the reduction of fuel usage, which results in lower emission levels for equivalent power generation capacity. Furthermore, this study contributes towards future research by providing valuable insights from academic and industry-related studies.

Cross‐Equatorial Surges Boost MJO's Southward Detour Over the Maritime Continent

AbstractThe influence of the cross‐equatorial northerly surge (CES) on the eastward propagation of Madden‐Julian oscillation (MJO) during boreal winter is evaluated through the analysis of the column integrated moisture budget. Results show that the CES reinforces MJO's southward detour by increasing horizontal moisture convergence over the southern Maritime Continent (MC) region. Further analysis reveals that the zonal convergence by intraseasonal zonal wind anomalies acting upon background moisture is intensified in the presence of CES events, causing a stronger convective activity in the southern MC (SMC). The stronger moisture convergence in the SMC is associated with the CES‐induced intensification of low‐level northwesterly and westerly winds, which, in turn, strengthen zonal wind convergences and positive wind‐evaporation feedbacks onto the MJO convection. An improved process understanding of the link between the CES and MJO detours can help engender improvements in extreme weather forecasts and aid investigation biases in simulating MJO in climate models.

Hochauflösende numerische Grundlagenuntersuchungen als Basis für ein verbessertes Prozessverständnis von Schwebstoff- und Geschiebetransport

ZusammenfassungIn dieser Arbeit werden zwei numerische Grundlagenuntersuchungen vorgestellt, die das Prozessverständnis von Schwebstoff- und Geschiebetransport erweitern. Die Modellierungen werden mittels der hochauflösenden Large-Eddy-Simulation durchgeführt. Im Fall des Schwebstofftransports werden die ersten Sekunden nach der Verklappung von Feinsediment simuliert und die Geschwindigkeiten der Partikel in der Schwebstoffwolke im Vergleich zur Fließgeschwindigkeit analysiert. Es zeigt sich, dass der Schwebstoff bereits nach wenigen Sekunden keine nennenswerten Geschwindigkeitsunterschiede zur Strömungsgeschwindigkeit in Hauptströmungsrichtung mehr aufweist und somit eine Modellierung mithilfe einer Advektions-Diffusions-Gleichung ab diesem Zeitpunkt zulässig ist. Hinsichtlich des Geschiebetransports werden die Einwirkungen von turbulenten kohärenten Strukturen auf die Geschiebekörner untersucht. Hierbei wird deutlich, dass insbesondere asymmetrische turbulente Strukturen in der Größe der Körner die Druckverteilung und das Verhältnis von Strömungswiderstands- zu Auftriebsbeiwerten so zu ändern vermögen, dass eine Bewegung des Geschiebes eintreten kann. Diese Erkenntnisse werden es zukünftig erlauben, Geschiebetransportmodelle mithilfe hydromechanischer Ansätze zu verbessern.

Heat and carbon uptake in the Southern Ocean: the state of the art and future priorities.

Heat and carbon uptake in the Southern Ocean: the state of the art and future priorities.

In-Line Near-Infrared Spectroscopy Gives Rapid and Precise Assessment of Product Quality and Reveals Unknown Sources of Variation—A Case Study from Commercial Cheese Production

Quality testing in the food industry is usually performed by manual sampling and at/off-line laboratory analysis, which is labor intensive, time consuming, and may suffer from sampling bias. For many quality attributes such as fat, water and protein, in-line near-infrared spectroscopy (NIRS) is a viable alternative to grab sampling. The aim of this paper is to document some of the benefits of in-line measurements at the industrial scale, including higher precision of batch estimates and improved process understanding. Specifically, we show how the decomposition of continuous measurements in the frequency domain, using power spectral density (PSD), may give a useful view of the process and serve as a diagnostic tool. The results are based on a case regarding the large-scale production of Gouda-type cheese, where in-line NIRS was implemented to replace traditional laboratory measurements. In conclusion, the PSD of in-line NIR predictions revealed unknown sources of variation in the process that could not have been discovered using grab sampling. PSD also gave the dairy more reliable data on key quality attributes, and laid the foundation for future improvements.

Multi-sensor monitoring and data integration reveal cyclical destabilization of the Äußeres Hochebenkar rock glacier

Abstract. This study investigates rock glacier destabilization based on the results of a unique in situ and remote-sensing-based monitoring network focused on the kinematics of the rock glacier in Äußeres Hochebenkar (Austrian Alps). We consolidate, homogenize, and extend existing time series to generate a comprehensive dataset consisting of 14 digital surface models covering a 68-year time period, as well as in situ measurements of block displacement since the early 1950s. The digital surface models are derived from historical aerial imagery and, more recently, airborne and uncrewed-aerial-vehicle-based laser scanning (ALS and ULS, respectively). High-resolution 3D ALS and ULS point clouds are available at annual temporal resolution from 2017 to 2021. Additional terrestrial laser scanning data collected in bi-weekly intervals during the summer of 2019 are available from the rock glacier front. Using image correlation techniques, we derive velocity vectors from the digital surface models, thereby adding rock-glacier-wide spatial context to the point-scale block displacement measurements. Based on velocities, surface elevation changes, analyses of morphological features, and computations of the bulk creep factor and strain rates, we assess the combined datasets in terms of rock glacier destabilization. To additionally investigate potential rotational components of the movement of the destabilized section of the rock glacier, we integrate in situ data of block displacement with ULS point clouds and compute changes in the rotation angles of single blocks during recent years. The time series shows two cycles of destabilization in the lower section of the rock glacier. The first lasted from the early 1950s until the mid-1970s. The second began around 2017 after approximately 2 decades of more gradual acceleration and is currently ongoing. Both destabilization periods are characterized by high velocities and the development of morphological destabilization features on the rock glacier surface. Acceleration in the most recent years has been very pronounced, with velocities reaching 20–30 m a−1 in 2020–2021. These values are unprecedented in the time series and suggest highly destabilized conditions in the lower section of the rock glacier, which shows signs of translational and rotational landslide-like movement. Due to the length and granularity of the time series, the cyclic destabilization process at the Äußeres Hochebenkar rock glacier is well resolved in the dataset. Our study highlights the importance of interdisciplinary, long-term, and continuous high-resolution 3D monitoring to improve process understanding and model development related to rock glacier rheology and destabilization.

Intensification of bioprocesses with filamentous microorganisms

Abstract Many industrial biotechnological processes use filamentous microorganisms to produce platform chemicals, proteins, enzymes and natural products. Product formation is directly linked to their cellular morphology ranging from dispersed mycelia over loose clumps to compact pellets. Therefore, the adjustment and control of the filamentous cellular morphology pose major challenges for bioprocess engineering. Depending on the filamentous strain and desired product, optimal morphological shapes for achieving high product concentrations vary. However, there are currently no overarching strain- or product-related correlations to improve process understanding of filamentous production systems. The present book chapter summarizes the extensive work conducted in recent years in the field of improving product formation and thus intensifying biotechnological processes with filamentous microorganisms. The goal is to provide prospective scientists with an extensive overview of this scientifically diverse, highly interesting field of study. In the course of this, multiple examples and ideas shall facilitate the combination of their acquired expertise with promising areas of future research. Therefore, this overview describes the interdependence between filamentous cellular morphology and product formation. Moreover, the currently most frequently used experimental techniques for morphological structure elucidation will be discussed in detail. Developed strategies of morphology engineering to increase product formation by tailoring and controlling cellular morphology and thus to intensify processes with filamentous microorganisms will be comprehensively presented and discussed.