Process Engineering Research Articles

Study on the Engineering Properties of Whole Garlic Bulbs and Garlic Cloves for Effective Design of Processing Machinery

The present experiment was conducted from February, 2024 to March, 2024 at the Department of Agricultural Process Engineering, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola, Maharashtra, India to investigate the various engineering properties of garlic bulbs and garlic cloves. The engineering properties viz., physical, gravimetric and frictional properties, were studied for whole garlic bulbs and garlic cloves by considering their importance in developing different agricultural and post-harvest processing machinery. The weight 11.41 g, length (polar diameter) 33.69 mm, width (equatorial diameter) 29.51 mm, thickness 27.58 mm, arithmetic mean diameter 30.26 mm, geometric mean diameter 30.10 mm, shape index 0.88, sphericity 0.90, surface area 63.05 cm2, cross-sectional area 53.75 cm2, bulk density 449.50 kg m-3, true density 930.45 kg m-3, porosity 51.69%, moisture content 56.70% and angle of repose of garlic bulb 50.54° were obtained. Similar properties were determined for garlic cloves. The L, a and b values for garlic bulb and garlic cloves were 82.95, 0.23, 10.66 and 74.42, 0.28, 12.48, respectively. The water activity of garlic was found to be 0.98±0.02. The coefficient of friction of the garlic bulb and garlic cloves was measured for the selected testing surfaces such as mild steel (MS), stainless steel (SS), wood, glass and fiberglass which was considered in hopper design. The compression force required to loosen the cloves from the garlic bulb was calculated as 61 N. By integrating these engineering properties into the design process, manufacturers can develop equipments or machineries that are more efficient, durable and capable of producing high-quality garlic products.

Efficient Simulation of Viral Transduction and Propagation for Biomanufacturing.

The design of biomanufacturing platforms based on viral transduction and/or propagation poses significant challenges at the intersection between synthetic biology and process engineering. This paper introduces vitraPro, a software toolkit composed of a multiscale model and an efficient numeric technique that can be leveraged for determining genetic and process designs that optimize transduction-based biomanufacturing platforms and viral amplification processes. Viral infection and propagation for up to two viruses simultaneously can be simulated through the model, considering viruses in either the lytic or lysogenic stage, during batch, perfusion, or continuous operation. The model estimates the distribution of the viral genome(s) copy number in the cell population, which is an indicator of transduction efficiency and viral genome stability. The infection age distribution of the infected cells is also calculated, indicating how many cells are in an infection stage compatible with recombinant product expression or viral amplification. The model can also consider the presence of defective interfering particles in the system, which can severely compromise the productivity of biomanufacturing processes. Model benchmarking and validation are demonstrated for case studies of the baculovirus expression vector system and influenza A propagation in suspension cultures.

Optimizing Motorcycle Manufacturing Sustainability through the Integration of Waste Heat Recovery and Metal Scrap Recycling: A Process Engineering Approach

The automotive industry manufacturing has experienced rapid growth 2–3 times by 2050, with motorcycles constituting around 30% of vehicles worldwide, but this increase in production has significantly heightened the demand for raw materials and energy. A major challenge arises in managing material waste and waste heat generated during the manufacturing process. This research aims to develop a framework that optimizes the synergy between material waste recycling and waste heat recovery to enhance the sustainability of the motorcycle industry, reduce waste, and lower energy consumption. The design leverages waste heat from the melting process to preheat raw materials, raising temperatures from around 50 °C to 350 °C before melting, thereby reducing additional energy needs, lowering emissions, and decreasing operational costs. Utilizing waste heat for preheating not only mitigates environmental impact and thermal load but also significantly improves energy efficiency, ultimately resulting in cost savings and optimized resource use. Utilizing waste heat directly for preheating raw materials has effectively lowered energy consumption by as much as 30%. This approach not only improves operational efficiency but also decreases production costs and minimizes environmental impact, offering a more sustainable solution for the manufacturing sector.

Treatment Recommendation using BERT Personalization

This research work develops a new framework that combines patient feedback with evidence-based best practices across disease states to improve drug recommendations. It employs BERT as its free-text processing engine to deal with sentiment judgment and classification. The functionality of the system, named `PharmaBERT`, includes acceptance of drug review data as a comprehensive input, drug categorization when dealing with a wide range of treatments and fine-tuning the BERT-based model for gaining positive or negative sentiment towards specific medications. PharmaBERT categorizes various drugs and fine-tunes the BERT structure to perceive lots of possible sentiments for specific medications. Consequently, PharmaBERT brings all its training and optimization capabilities together and through this, the system reaches a higher accuracy of up to 91% thus showcasing the potency of the model in capturing patient sentiments. While being a BERT spin-off, PharmaBERT utilizes its own set of experienced techniques to comprehend and sense the health-related text input given by the patient, doctor, or pharmacist. It uses transfer learning, that is, it learns from language representations to adapt quickly to the intricacies of drug reviewing. Through PharmaBERT, healthcare professionals may expand their diagnoses with insights from patient feedback to constitute more neutral decisions.

Long-time prediction of multi-indicator water quality based on the improved WaveNet under time series decomposition and phase space reconstruction

Abstract Long–time prediction of water quality indicator such as chlorophyll–a (Chl–a) is crucial for water process engineering and environmental management. In order to capture the characteristics of long–time series and reduce the limitations of traditional long–time prediction strategies, this paper proposes a novel hybrid model by combining data decomposition, phase space reconstruction, feature fusion and improved WaveNet. Firstly, the original data is decomposed into several subsequences through time series decomposition. Then, the subsequences with chaotic characteristics are integrated with multiple features for phase space reconstruction. Next, the decomposed and reconstructed subsequences are fed back into the improved WaveNet model separately. Finally, the prediction results are obtained by summing the predicted values of the subsequences. In this paper, the reliability of the method is assessed using the dissolved oxygen, water temperature, pH and Chl–a data of a monitoring station in the Beihai coastal sea area, ablation experiments are conducted to demonstrate the effectiveness of each module in the proposed model, and comparisons with multiple benchmark and hybrid models show that the proposed model exhibits better performance in long–time prediction of coastal water quality in the next fourteen days.

Biosurfactants: Chemical Properties, Ecofriendly Environmental Applications, and Uses in the Industrial Energy Sector

The exploitation of nature and the increase in manufacturing production are the cause of major environmental concerns, and considerable efforts are needed to resolve such issues. Oil and petroleum derivatives constitute the primary energy sources used in industries. However, the transportation and use of these products have huge environmental impacts. A significant issue with oil-related pollution is that hydrocarbons are highly toxic and have low biodegradability, posing a risk to ecosystems and biodiversity. Thus, there has been growing interest in the use of renewable compounds from natural sources. Biosurfactants are amphipathic microbial biomolecules emerging as sustainable alternatives with beneficial characteristics, including biodegradability and low toxicity. Biosurfactants and biosurfactant-producing microorganisms serve as an ecologically correct bioremediation strategy for ecosystems polluted by hydrocarbons. Moreover, synthetic surfactants can constitute additional recalcitrant contaminants introduced into the environment, leading to undesirable outcomes. The replacement of synthetic surfactants with biosurfactants can help solve such problems. Thus, there has been growing interest in the use of biosurfactants in a broad gamut of industrial sectors. The purpose of this review was to furnish a comprehensive view of biosurfactants, classifications, properties, and applications in the environmental and energy fields. In particular, practical applications of biosurfactants in environmental remediation are discussed, with special focus on bioremediation, removal of heavy metals, phytoremediation, microbial enhanced oil recovery, metal corrosion inhibition, and improvements in agriculture. The review also describes innovating decontamination methods, including nanobioremediation, use of genetically modified microorganisms, enzymatic bioremediation, modeling and prototyping, biotechnology, and process engineering. Research patents and market prospects are also discussed to illustrate trends in environmental and industrial applications of biosurfactants.

Analysis of droplet motion in cavity zone of rotating packed bed

Droplet motion in outer cavity zone of rotating packed bed is a complex phenomenon that requires analysis of liquid and gas flow to clarify influences of factors such as rotation speed, liquid flow rate, or gas flow rate. In this study, hydrodynamics is described using laser Doppler velocimetry and high-resolution visualization for droplet motion characterization. The results reveal that droplet motion initially depends on the peripheral velocity of the packing, while further from the packing, it loses kinetic energy mainly due to the drag force caused by the surrounding gas. Two-way coupling is investigated as both phases interact with each other. Rankine vortex theory is applied to simulate gas flow in the outer cavity zone. A model, based on Newton’s second law, is developed to predict droplet dynamics, including impingement velocity, trajectory length, and total residence time. Validation of the model shows that it can predict the liquid velocity in the outer cavity zone accurately. Upon impingement of droplets on the casing, part of the liquid rebounds as secondary droplets into the cavity, where they circulate with the gas flow. Overall, this study provides insights into droplet dynamics in the rotating packed bed and offers a basis for optimizing process efficiency and design in various applications, including chemical processing and environmental engineering.

DIAERESIS: RDF data partitioning and query processing on SPARK

The explosion of the web and the abundance of linked data demand effective and efficient methods for storage, management, and querying. Apache Spark is one of the most widely used engines for big data processing, with more and more systems adopting it for efficient query answering. Existing approaches exploiting Spark for querying RDF data, adopt partitioning techniques for reducing the data that need to be accessed in order to improve efficiency. However, simplistic data partitioning fails, on one hand, to minimize data access and on the other hand to group data usually queried together. This is translated into limited improvement in terms of efficiency in query answering. In this paper, we present DIAERESIS, a novel platform that accepts as input an RDF dataset and effectively partitions it, minimizing data access and improving query answering efficiency. To achieve this, DIAERESIS first identifies the top-k most important schema nodes, i.e., the most important classes, as centroids and distributes the other schema nodes to the centroid they mostly depend on. Then, it allocates the corresponding instance nodes to the schema nodes they are instantiated under. Our algorithm enables fine-tuning of data distribution, significantly reducing data access for query answering. We experimentally evaluate our approach using both synthetic and real workloads, strictly dominating existing state-of-the-art, showing that we improve query answering in several cases by orders of magnitude.

A holistic view over ontologies for Streaming Linked Data

Streaming Linked Data represents a domain within the Semantic Web dedicated to incorporating Stream Reasoning capabilities into the Semantic Web stack to address dynamic data challenges. Such applied endeavours typically necessitate a robust data modelling process. To this end, RDF Stream Processing (RSP) engines frequently utilize OWL 2 ontologies to facilitate this requirement. Despite the rich body of research on Knowledge Representation (KR), even concerning time-sensitive data, a notable gap exists in the literature regarding a comprehensive survey on KR techniques tailored for Streaming Linked Data. This paper critically overviews the key ontologies employed in RSP applications, evaluating their data modelling and KR abilities specifically for Streaming Linked Data contexts. We analyze these ontologies through three distinct KR perspectives: the conceptualization of streams as Web resources, the structural organization of data streams, and the event modelling within the streams. An analytical framework is introduced for each perspective to ensure a thorough and equitable comparison and deepen the understanding of the surveyed ontologies.

Time scale analysis of enzymatic reduction of uric acid in a microfluidic biomedical device

Time Scale Analysis (TSA) is an investigative tool used in engineering design to identify locations in processes that should be a focus of Process Intensification (PI). Furthermore, TSA points to process variables and parameters that could be used to advance and measure PI improvement. However, TSA cannot suggest any specific design solution to intensify process performance. Instead, design engineers should use their fundamental knowledge and creative intelligence to specify detailed design transformations. TSA will then provide a specific quantitative measure of the improvement. TSA implementation improves an explicitly defined process performance, thus helping achieve process intensification goals. TSA is based on first principles, and it utilizes Characteristic Times (CT) such as diffusion, mean residence, and reaction times to improve an existing process. In this study, we specifically consider microfluidic biomedical devices. To illustrate the genesis of CT and TSA, we start by developing a mathematical model of an enzymatic degradation process in a biomedical device called iCore based on mass, momentum, and kinetic equations. After introducing user-defined scaling parameters, we extract CTs pertinent to the enzymatic degradation of uric acid in this microfluidic biomedical device. Diffusion coefficients, microchannel architectural characteristics, enzyme loading, hydrogel thickness, and characteristic parameters of enzyme kinetics are the parameters and process variables incorporated in this analysis. Finally, we compared the extracted CTs with a COMSOL Multiphysics parametric study to demonstrate how time scale analysis as a design tool and adjusting design parameters, such as diffusion coefficient, hydrogel layer thickness, substrate concentration, and enzyme concentration, can enhance the enzymatic reaction process without a need for complex computational modeling. It is crucial to recognize that pertinent CTs can be determined by understanding the type and nature of the observed process, previous experience, published data, and other foundational engineering design work. There is no need for mathematical modeling and numerical simulations to identify and acknowledge the CTs relevant and essential to the observed process; in this work, we only illustrate the principal origin of CTs via a detailed mathematical model of the process, as previously reported by Jovanovic et al. Therefore, in a routine application of TSA, it is important to remember that mathematical modeling and detailed numerical simulations are not necessary. This is a very comforting fact when TSA is deployed as a tool in higher-level process design functions. The investigations on how best to apply TSA in these higher level design functions such as Process Intensification, scale-up/numbering-up, change of device architecture, change of operating conditions, change of process feed characteristics, change of material physical and chemical properties, parametric optimization of the system for various objective functions, and techno-economic analysis, are yet to be studied and reported.

Perspective on pathways towards responsible surface engineering

In this perspective sustainability-relevant aspects of modern surface engineering technologies, which enable improved structural and functional surface properties, are critically evaluated. Although plasma-assisted physical vapour deposition (PVD) is increasingly employed to address global challenges, such as energy efficiency and reduction of CO2 emissions, their inherently resource-intensive nature is often not considered.Surface engineering research should thus embrace sustainability-relevant aspects from a processes and materials design point of view. While we are convinced that sustainability-relevant surface engineering has to be based on synchronised process and materials solutions, we will discuss processes and materials separately.In terms of processes, we are going to describe the challenges of state-of-the-art technology, including energy and mass balances as well as product cycles. With respect to materials, the coating and process purity as well as chemical and microstructural complexity are discussed.Such approaches are fully in line with the United Nations Sustainable Development Goal 12 Responsible Consumption and Production. We expect that the here discussed urgently needed pathways towards responsible surface engineering will become important for the surface engineering community and adopted within the near future. Responsible surface engineering includes the human behaviour and necessitates a change in mindset of materials scientists and process engineers. Hence, two main questions are critically evaluated in this perspective:1)What are sustainability-relevant aspects of PVD processes and materials?2)Which pathways are available to move towards responsible surface engineering?

BIG DATA, BUSINESS PROCESSES, AND MATHEMATICAL ALGORITHMS FOR MULTI-OBJECTIVE MANAGERIAL DECISION OPTIMIZATION

The use of Big Data and the engineering of business processes are becoming increasingly important for managing the business of companies and large international corporate groups. For more successful outcomes, even for complex interventions such as those of the PNRR (National Recovery and Resilience Plan), the use of mathematical models to assist management decisions represents a significant lever to optimize time, costs, and planned overall performance. This analytical approach strengthens traditional qualitative techniques like SWOT analysis and finds useful application in the optimal definition of any form of PPP (Public-Private Partnership), such as the recent “Partnership for Innovation” (PPI) included in the new procurement code. The scientific work developed from the systematic analysis of processes proposes a particular multi-objective mathematical model that uses a system of vectors in an m-dimensional reference space.

Ultrasonic Enhancement for Mineral Flotation: Technology, Device, and Engineering Applications

In the past five years, the number of articles related to ultrasonic mineral flotation has increased by about 50 per year, and the overall trend is on the rise. The most recent developments in ultrasonics for flotation process intensification are reviewed herein, including effects of ultrasound treatment on an aqueous slurry, improvement in flotation methods and technological processes, device development tracking, and application effects in mineral process engineering. At this point in time, there are pilot-scale flotation tests to evaluate the feasibility of ultrasonic pretreatment technology for industrial use to enhance residue flotation separation, and the results showed that the recovery rate of concentrate is increased by about 10%. Four aspects of ultrasonic flotation process improvement are summarized, namely, changing the ultrasonic parameters, the synergistic effect of ultrasound and reagents, the ultrasonic effect of particles with different-sized fractions, and application to new systems. In addition, the effect of ultrasonic flotation mechanisms is explored through a quadratic model and numerical simulation. The combination of ultrasonic flotation with other fields, such as magnetic fields, to enhance the separation efficiency and recovery of minerals is also a future trend. It is also proposed that ultrasonic flotation technology will be used with big data, industrial Internet of Things, and automatic control technology to achieve deep bundling, optimizing the flotation process by implementing remote monitoring and control of the flotation process.

Machine Learning Algorithms for Prediction of Boiler Steam Production

The continuous increase in global electricity demand has resulted in boiler power plants becoming a significant energy source. The production of steam is a principal indicator of boiler efficiency, and the accurate prediction of steam production is paramount importance for the enhancement of boiler efficiency and the reduction of operational costs. In this study employs a boiler dataset with a steam production capacity of 420 tons per hour. A total of 25 independent variables were extracted from the original 39 variables through data processing and feature engineering for the purpose of prediction analysis. Subsequently, 8 machine learning models were used for modeling predictions. Grid search cross-validation was employed in order to optimise the performance of the model. The models were analysed and assessed using the Mean Squared Error (MSE) metrics. The results show that random forest achieves the highest accuracy among the 8 single models. Based on 8 models, New Bagging ensemble model is proposed, which combined predictions from 8 single models, demonstrated the optimal overall fit and the lowest MSE, achieved the purpose of the research. The present study demonstrates the ability to analyse and predict complex industrial systems with machine learning algorithms, and provides insights into the use of machine learning algorithms for industrial big data analytics and Industry 4.0. Further work could explore using larger datasets and deep learning to make predictions more accurate.

Prediction of pure and mixture thermodynamic properties and phase equilibria using an optimized equation of state – part 1: Parameter estimation

This study presents a novel three-parameter equation of state (EOS) wherein attraction parameter polynomial coefficients are optimized to enhance predictive capabilities. The performance of the proposed EOS is systematically compared with established models including Peng-Robinson, Patel-Teja, and Twu-Coon-Cunningham equations. Through comprehensive evaluation, it is demonstrated that the proposed EOS exhibits superior accuracy in vapor pressure and latent enthalpy predictions, while maintaining comparable precision in liquid density estimations. Notably, the fugacity expression of the proposed EOS closely resembles that of a two-parameter equation, resulting in significantly reduced computational overhead. Additionally, a comprehensive table of equation of state parameters for all four equations is available in an online repository, facilitating easy implementation and comparison. Furthermore, a reliable generalized polynomial correlation is provided for the proposed EOS parameters against the true compressibility factor and acentric factor, leveraging data accessibility and enhancing its applicability and versatility. These findings underscore the potential of the optimized attraction parameter polynomial coefficients approach in advancing the accuracy and efficiency of EOS modeling, thereby offering promising avenues for diverse applications in thermodynamics and process engineering.

A Review on Liquid Hydrogen Storage: Current Status, Challenges and Future Directions

The growing interest in hydrogen (H2) has motivated process engineers and industrialists to investigate the potential of liquid hydrogen (LH2) storage. LH2 is an essential component in the H2 supply chain. Many researchers have studied LH2 storage from the perspective of tank structure, boil-off losses, insulation schemes, and storage conditions. A few review studies have also been published considering LH2 storage; however, most are simply collections of previous articles. None of these review articles have critically evaluated the research articles. In this review study, recent reports, conceptual studies, and patents have been included and critically discussed. Further, challenges and recommendations have been listed based on the literature review. Our results suggest that the multi-layer insulation scheme and integrated refrigeration system can effectively reduce boil-off losses. However, boil-off losses from storage tanks during transportation are the least discussed and must be addressed. The cost of an LH2 storage tank is high, but it can be reduced with advancements in materials and the utilization of latest technologies. The present challenges and future directions for LH2 storage include minimizing and utilizing boil-off losses, improving insulation schemes, and ensuring cost-effective large-scale LH2 storage. This review study can be fundamental for process engineers and new academic researchers to design energy-efficient and cost-effective LH2 storage systems.

Enhanced biocatalytic production of cortisol by protein engineering and process engineering

Cortisol, the primary glucocorticoid in humans, plays crucial physiological functions and serves as an intermediate for synthesizing other glucocorticoids. Currently, cortisol production mainly relies on a semi-synthetic route, where the key step of introducing 11β-OH into 11-deoxycortisol is catalyzed by the filamentous fungi Curvularia lunata and Absidia orchidis. This method, however, generates by-products and involves lengthy cultivation. To achieve specific and efficient production of cortisol, we constructed a recombinant biocatalyst by expressing and engineering the human mitochondrial 11β-hydroxylase CYP11B1 in Escherichia coli. Firstly, the balance between CYP11B1 and its redox partners AdR and Adx was regulated through ribosome binding site (RBS) engineering, resulting in a slight increase in cortisol productivity (from 344±19 mg·L−1·d−1 to 407±7 mg·L−1·d−1). Subsequently, the heterologous expression of CYP11B1 was improved through application of the computational design tool PROSS, generating a triple mutant S169V/H354D/L463F with 87.5 % higher cortisol yield than the wild type. Finally, the catalytic performance was improved by optimizing the recombinant protein expression conditions and enhancing the substrate solubility in the reaction system, further elevating the productivity of cortisol to 2.8±0.1 g·L−1·d−1. To our knowledge, this is the highest ever reported cortisol productivity using a human 11β-hydroxylase-based biocatalyst.

Autotrophic biological nitrogen removal in anammox system at elevated temperature: EPS regulation and metagenomic analysis

Carbon reduction in wastewater treatment industry has been attracted global attention. Anaerobic ammonium oxidation (anammox) process is known as a promising and energy-efficient technology. In this study, the effects of elevated temperature on anammox system were thoroughly investigated. Analyses of effluent and microbial community indicated that long-term exposure to 45℃ led to system collapse and a huge shift in community structure. Network analysis revealed that the sudden drop in abundance of AnAOB at 45℃ (from 36.19 % to 10.71 %) made them uncompetitive with heterotrophic bacteria. The decrease of EPS concentration (a minimum of 7.22 mg·g−1-VSS at 45℃) combined with the enhancement of EPS metabolism at higher temperature suggested a shift in the role of EPS from protective function at 35℃ to utilization as a carbon source for heterotrophic bacteria at higher temperatures. Through constructing the cross-feeding relationships associated with amino acids, polysaccharides, and cofactors, we found the dependence of heterotrophs on AnAOB became weaker at high temperature, and the disturbances in interspecific relationships might be responsible for the collapse of the anammox system. This work presents significant insights into the engineering of anammox process under high temperature.

Understanding the Variety of Domain Models: Views, Programs, Animations, and Other Models

Humanity has long since used models, in different shapes and forms, to understand, redesign, communicate about, and shape, the world around us; including many different social, economic, biological, chemical, physical, and digital aspects. This has resulted in a wide range of modeling practices. When the models as used in such modeling practices have a key role to play in the activities in which these practices are ‘embedded’, the need emerges to consider the effectiveness and efficiency of such processes, and speak about modeling capabilities. In the latter situation, it also becomes relevant to develop a thorough understanding of the artifacts involved in modeling practices/capabilities. One context in which models play (an increasingly) important role is model-driven systems development, including software engineering, information systems engineering, business process engineering, enterprise engineering, and enterprise architecture management. In such a context, we come across a rich variety of modeling related artifacts, such as views, diagrams, programs, animations, specifications, etc. In this paper, which is actually part of an ongoing ‘journey’ in which we aim to gain deeper insights into the foundations of modeling, we take a fundamental look at the variety of modeling related artifacts as used in the context of model-driven (systems) development, while also presenting an associated framework for understanding, synthesizing the insights we obtained during the ‘journey’ so-far. In doing so, we will also argue that the aforementioned artifacts are actually specific kinds of models, albeit for fundamentally different purposes. The provided framework for understanding involves definitions of domain model, the Return on Modeling Effort (RoME), the conceptual fidelity of domain models, as well as views as a mechanism to manage the complexity of domain models.

Optimising 4D imaging of fast-oscillating structures using X-ray microtomography with retrospective gating

Imaging the internal architecture of fast-vibrating structures at micrometer scale and kilohertz frequencies poses great challenges for numerous applications, including the study of biological oscillators, mechanical testing of materials, and process engineering. Over the past decade, X-ray microtomography with retrospective gating has shown very promising advances in meeting these challenges. However, breakthroughs are still expected in acquisition and reconstruction procedures to keep improving the spatiotemporal resolution, and study the mechanics of fast-vibrating multiscale structures. Thereby, this works aims to improve this imaging technique by minimising streaking and motion blur artefacts through the optimisation of experimental parameters. For that purpose, we have coupled a numerical approach relying on tomography simulation with vibrating particles with known and ideal 3D geometry (micro-spheres or fibres) with experimental campaigns. These were carried out on soft composites, imaged in synchrotron X-ray beamlines while oscillating up to 400 Hz, thanks to a custom-developed vibromechanical device. This approach yields homogeneous angular sampling of projections and gives reliable predictions of image quality degradation due to motion blur. By overcoming several technical and scientific barriers limiting the feasibility and reproducibility of such investigations, we provide guidelines to enhance gated-CT 4D imaging for the analysis of heterogeneous, high-frequency oscillating materials.