Process and Chemical Engineering - Journal Articles

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    Integrating graph neural network-based surrogate modeling with inverse design for granular flows
    (American Chemical Society, 2024-05-12) Jiang, Yu; Byrne, Edmond; Glassey, Jarka; Chen, Xizhong; National Natural Science Foundation of China; University College Cork; Eli Lilly and Company
    Granular flows are central to a wide range of natural phenomena and industrial processes such as landslides, industrial mixing, and material handling and present intricate particle dynamics challenges. This study introduces a novel approach utilizing a Graph Neural Network-based Simulator (GNS) integrated with an inverse design for optimizing Discrete Element Method (DEM) parameters in granular flow simulations. The GNS model, trained on data sets generated from high-fidelity DEM simulations, exhibits enhanced predictive accuracy and generalization capabilities across various materials and granular collapse scenarios. Methodologically, the study contrasts the GNS approach with conventional Design of Experiment (DoE) methods, highlighting its enhanced computational efficiency and dynamic optimization capacity for complex parameter interactions in granular flows. The results demonstrate the GNS method superiority over the DoE in terms of computational speed and handling intricate parameter relationships. This work offers an advancement in computational techniques for granular flow studies, showing the potential of using differential simulations for realistic problems.
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    Design and development of an edible coating for a ready-to-eat fish product
    (MDPI, 2024-01-27) Bremenkamp, Ina; Sousa-Gallagher, Maria J.; Interreg
    The application of chitosan and alginate coatings for a ready-to-eat (RTE) baked fish product was studied. An experimental design was used to investigate the effect of coating a polysaccharide concentration and glycerol addition on the safety (microbial growth) and quality (water loss and lipid oxidation) of an RTE fish product under optimal and abused storage conditions. The results showed that a chitosan coating with 1% (w/v) chitosan in 1% (v/v) acetic acid and 15% (w/w chitosan) glycerol, or a 1% (w/v) alginate coating with no glycerol and no crosslinking, showed the best performance in controlling the tested safety and quality parameters. The desirability method was used to identify the shelf lives of chitosan, alginate, and double-coated RTE products. The chitosan-coated samples showed the best performance with a three-fold shelf-life extension compared to the uncoated products stored at 4 °C. Moreover, the tested coatings demonstrated their ability to provide protective functions under abused storage conditions. These results strongly suggest that edible coatings have significant potential in enhancing the shelf life and safety of ready-to-eat (RTE) fish products.
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    A roadmap for model-based bioprocess development
    (Elsevier Inc., 2024-05-22) Mu’azzam, Khadija; da Silva, Francisco Vitor Santos; Murtagh, Jason; Sousa Gallagher, Maria Jose
    The bioprocessing industry is undergoing a significant transformation in its approach to quality assurance, shifting from the traditional Quality by Testing (QbT) to Quality by Design (QbD). QbD, a systematic approach to quality in process development, integrates quality into process design and control, guided by regulatory frameworks. This paradigm shift enables increased operational efficiencies, reduced market time, and ensures product consistency. The implementation of QbD is framed around key elements such as defining the Quality Target Product Profile (QTPPs), identifying Critical Quality Attributes (CQAs), developing Design Spaces (DS), establishing Control Strategies (CS), and maintaining continual improvement. The present critical analysis delves into the intricacies of each element, emphasizing their role in ensuring consistent product quality and regulatory compliance. The integration of Industry 4.0 and 5.0 technologies, including Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), and Digital Twins (DTs), is significantly transforming the bioprocessing industry. These innovations enable real-time data analysis, predictive modelling, and process optimization, which are crucial elements in QbD implementation. Among these, the concept of DTs is notable for its ability to facilitate bi-directional data communication and enable real-time adjustments and therefore optimize processes. DTs, however, face implementation challenges such as system integration, data security, and hardware-software compatibility. These challenges are being addressed through advancements in AI, Virtual Reality/ Augmented Reality (VR/AR), and improved communication technologies. Central to the functioning of DTs is the development and application of various models of differing types – mechanistic, empirical, and hybrid. These models serve as the intellectual backbone of DTs, providing a framework for interpreting and predicting the behaviour of their physical counterparts. The choice and development of these models are vital for the accuracy and efficacy of DTs, enabling them to mirror and predict the real-time dynamics of bioprocessing systems. Complementing these models, advancements in data collection technologies, such as free-floating wireless sensors and spectroscopic sensors, enhance the monitoring and control capabilities of DTs, providing a more comprehensive and nuanced understanding of the bioprocessing environment. This review offers a critical analysis of the prevailing trends in model-based bioprocessing development within the sector.
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    Traditional and contemporary eco-cosmologies within Western and Christian traditions: Green shoots for integral and integrative sustainability transformation
    (Sage Publications, 2024-02-06) Byrne, Edmond P.
    This article traces persistent and important counter-narratives within Western and Christian traditions, which correlate more closely with many indigenous worldviews from across the globe than with dominant narratives from across these traditions. It posits that a paradigmatic transformation is required toward an integral and integrative eco-cosmology, one that embraces cosmic interconnection and complexity. This may emerge from green shoots emanating from a diverse range of traditions, including, importantly, from within the dominant Western tradition.
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    Application of broadband acoustic resonance dissolution spectroscopy (BARDS) to the gas release behaviour during rehydration of milk protein isolate agglomerates
    (Elsevier Ltd, 2019) Wu, Shaozong; Fitzpatrick, John; Cronin, Kevin; Ahmed, M. Rizwan; Fitzpatrick, Dara; Miao, Song; China Scholarship Council; Teagasc
    The BARDS technique was applied in this study to acoustically assess the rehydration behaviour of milk protein isolate (MPI) agglomerates and to compare with regular MPI powder. The results showed that BARDS has potential to monitor the rehydration behaviour of agglomerates. The greater porosity (>70%) of agglomerated powders introduced more compressible gas into the water. The BARDS profile showed that there was faster initial gas release from the agglomerates, indicating better wetting and dispersion ability of the agglomerates with shorter t M (time of maximum gas volume in solution). At 0.10% powder addition, agglomerated MPI reached t M within 109 s, which was significantly less than the control MPI at 140 s. MPI with lactose binder (MPI-L) had a t M of 80 s at 0.10% powder addition and, larger size MPI-L had a t M of 60 s. At 0.20% and 0.30% powder addition, more time was required to wet and disperse the powders. © 2019 Elsevier Ltd