Hasil untuk "Fermentation industries. Beverages. Alcohol"

Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas et al.

The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for industrial or societal applications the common issue of addressing the inter-relation between parameters describing the hardware used in data production and parameters used to analyse those data. While in many cases this coupling can be ignored -- when the problem can be successfully factored into simpler sub-tasks and the latter addressed serially -- there are situations in which that approach fails to converge to the absolute maximum of expected performance, as it results in a mis-alignment of the optimized hardware and software solutions. In this work we consider a few use cases of interest in fundamental science collected primarily from particle physics and related areas, and a pot-pourri of industrial and societal applications where the matter is similarly of relevance. We discuss the emergence of strong hardware-software coupling in some of those systems, as well as co-design procedures that may be deployed to identify the global maximum of their relevant utility functions. We observe how numerous opportunities exist to advance methods and tools for hardware-software co-design optimization, bridging fundamental science and industry through application- and challenge-driven projects, and shaping the future of scientific experiments and industrial systems.

Bo Peng, Liyue Fei, Ziyi Lu et al.

Sea buckthorn juice (SBJ) has a sour taste and can lead to the demineralization of tooth enamel when consumed over a long period of time, whereas fermentation reduces the acidity of sea buckthorn juice, improves its taste, and enhances its antioxidant activity. Flavor components are important factors that affect the quality of fermented beverages. Yeast is one of the most important factors affecting the flavor of beverages during the fermentation process, where yeast converts sugars into alcohol and produces flavor substances. Therefore, two commercial yeast strains, Angel RW and Angel RV171, were selected in this study for the single and mixed bacterial fermentation of sea buckthorn juice (FSBJ). Physicochemical analyses showed that RV171-FSBJ had the highest total reducing sugar (0.069 ± 0.02 g/L) and total acid content (1.86 ± 0.03 g/L), as well as the highest fermentation efficiency and free radical scavenging capacity (1,1-diphenyl-2-picrylhydrazyl (DPPH) 98.54 ± 0.03%, 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid (ABTS) 88.35 ± 0.14%, ·OH 48.61 ± 0.4%). RWRV-FSBJ had the highest content of functional compounds (total flavonoid content (TFC): 176.09 ± 0.44 μg/mL; total phenolic content (TPC): 157.9 ± 1.35 μg/mL; total anthocyanin concentration (TAC): 0.04 ± 0.004 μg/mL) and good color (<i>L*</i> 50.53 ± 0.04, <i>a*</i> 27.98 ± 0.04, <i>b*</i> 173.64 ± 0.34). Among the three FSBJs, a total of 54 volatile compounds were identified, with RV171-FSBJ having the highest content of volatile compounds. OAV analysis showed that 15, 14, and 11 volatile compounds of RW, RV, and RWRV, respectively, were greater than 1. Among them, ethyl hexanoate had the highest OAV, followed by ethyl isovalerate, phenylethyl alcohol, and 3-methylbutyl 3-methylbutanoate, which are characteristic flavor substances common to FSBJ.

Aniket Pramanik, Obaidullah Rahman, Singanallur V. Venkatakrishnan et al.

Cone-beam X-ray Computed Tomography (XCT) with large detectors and corresponding large-scale 3D reconstruction plays a pivotal role in micron-scale characterization of materials and parts across various industries. In this work, we present a novel deep neural network-based iterative algorithm that integrates an artifact reduction-trained CNN as a prior model with automated regularization parameter selection, tailored for large-scale industrial cone-beam XCT data. Our method achieves high-quality 3D reconstructions even for extremely dense thick metal parts - which traditionally pose challenges to industrial CT images - in just a few iterations. Furthermore, we show the generalizability of our approach to out-of-distribution scans obtained under diverse scanning conditions. Our method effectively handles significant noise and streak artifacts, surpassing state-of-the-art supervised learning methods trained on the same data.

Yelyzaveta Vodovozova, Abhishek Awasthi, Caitlin Jones et al.

Optimization is a crucial task in various industries such as logistics, aviation, manufacturing, chemical, pharmaceutical, and insurance, where finding the best solution to a problem can result in significant cost savings and increased efficiency. Tensor networks (TNs) have gained prominence in recent years in modeling classical systems with quantum-inspired approaches. More recently, TN generative-enhanced optimization (TN-GEO) has been proposed as a strategy which uses generative modeling to efficiently sample valid solutions with respect to certain constraints of optimization problems. Moreover, it has been shown that symmetric TNs (STNs) can encode certain constraints of optimization problems, thus aiding in their solution process. In this work, we investigate the applicability of TN- and STN-GEO to an industry relevant problem class, a multi-knapsack problem, in which each object must be assigned to an available knapsack. We detail a prescription for practitioners to use the TN-and STN-GEO methodology and study its scaling behavior and dependence on its hyper-parameters. We benchmark 60 different problem instances and find that TN-GEO and STN-GEO produce results of similar quality to simulated annealing.

Lu Xia, Kaiqi Zhao, Sunil Kadam et al.

Paired electrolysis at industrial current densities offers an energy-efficient and sustainable alternative to thermocatalytic chemical synthesis by leveraging anodic and cathodic valorization. However, its industrial feasibility remains constrained by system integration, including reactor assembly, asymmetric electron transfer kinetics, membrane selection, mass transport limitations, and techno-economic bottlenecks. Addressing these challenges requires an engineering-driven approach that integrates reactor architecture, electrode-electrolyte interactions, reaction pairing, and process optimization. Here, we discuss scale-specific electrochemical reactor assembly strategies, transitioning from half-cell research to full-scale stack validation. We develop reaction pairing frameworks that align electrocatalyst design with electrochemical kinetics, enhancing efficiency and selectivity under industrial operating conditions. We also establish application-dependent key performance indicators (KPIs) and benchmark propylene oxidation coupled with hydrogen evolution reaction (HER) or oxygen reduction reaction (ORR) against existing industrial routes to evaluate process viability. Finally, we propose hybrid integration models that embed paired electrolysis into existing industrial workflows, overcoming adoption barriers.

Junfeng Jiao, Saleh Afroogh, Kevin Chen et al.

The rise of Generative AI (GAI) and Large Language Models (LLMs) has transformed industrial landscapes, offering unprecedented opportunities for efficiency and innovation while raising critical ethical, regulatory, and operational challenges. This study conducts a text-based analysis of 160 guidelines and policy statements across fourteen industrial sectors, utilizing systematic methods and text-mining techniques to evaluate the governance of these technologies. By examining global directives, industry practices, and sector-specific policies, the paper highlights the complexities of balancing innovation with ethical accountability and equitable access. The findings provide actionable insights and recommendations for fostering responsible, transparent, and safe integration of GAI and LLMs in diverse industry contexts.

Jay Lee, Hanqi Su

Recent advancements in industrial artificial intelligence (AI) are reshaping the industry by driving smarter manufacturing, predictive maintenance, and intelligent decision-making. However, existing approaches often focus primarily on algorithms and models while overlooking the importance of systematically integrating domain knowledge, data, and models to develop more comprehensive and effective AI solutions. Therefore, the effective development and deployment of industrial AI require a more comprehensive and systematic approach. To address this gap, this paper reviews previous research, rethinks the role of industrial AI, and proposes a unified industrial AI foundation framework comprising three core modules: the knowledge module, data module, and model module. These modules help to extend and enhance the industrial AI methodology platform, supporting various industrial applications. In addition, a case study on rotating machinery diagnosis is presented to demonstrate the effectiveness of the proposed framework, and several future directions are highlighted for the development of the industrial AI foundation framework.

Mélida del Pilar Anzola-Rojas, Lucas Tadeu Fuess, Marcelo Zaiat

Inhibiting homoacetogens is one of the main challenges in fermentative hydrogen production because these hydrogen consumers have similar growth features to hydrogen producers. Homoacetogens have been related to the excessive accumulation of biomass in fermentative reactors. Therefore, a suitable food/microorganism ratio has the potential to minimize the homoacetogenic activity. In this work, the specific organic loading rate (SOLR) was controlled in two fermentative fixed-bed up-flow reactors through scheduled biomass discharges. Reactors were differentiated by the bed arrangement, namely, packed and structured conformation. The SOLR decay along the time in both reactors was previously simulated according to the literature data. The volume and volatile suspended solids (VSS) concentration of discharges was estimated from the first discharge, and then additional discharges were planned. Biomass discharges removed 21% of the total biomass produced in the reactors, maintaining SOLR values of 3.0 ± 0.4 and 3.9 ± 0.5 g sucrose g⁻¹ VSS d⁻¹ in the packed-bed and structured-bed reactors, respectively. Such a control of the SOLR enabled continuous and stable hydrogen production at 2.2 ± 0.2 L H₂ L⁻¹ d⁻¹ in the packed-bed reactor and 1.0 ± 0.3 L H₂ L⁻¹ d⁻¹ in the structured-bed one. Controlling biomass was demonstrated to be a suitable strategy for keeping the continuous hydrogen production, although the fermentative activity was impaired in the structured-bed reactor. The homoacetogenic was partially inhibited, accounting for no more than 30% of the total acetic acid produced in the reactor. Overall, the high amount of attached biomass in the packed-bed reactor provided more robustness to the system, offsetting the periodic suspended biomass losses via the planned discharges. Better characterizing both the VSS composition (aiming to differentiate cells from polymeric substances) and the bed hydrodynamics could be useful to optimize the online SOLR control.

Raquel Fernández-Varela, Anders Holmgaard Hansen, Birgit Albrecht Svendsen et al.

This article explores the transformative potential of fermentation in elevating the quality of plant-based matrices to match the desirable attributes of traditional dairy and meat products. As the demand for sustainable products without animal welfare issues increases, fermentation has emerged as a key process to enhance the organoleptic properties and nutritional content of plant-based analogs. This study explores the effect of fermentation when applied to legume matrices, focusing on the resulting texture, flavor, and nutritional value. A selection of <i>Bacillus subtilis</i>, lactic acid bacteria (LAB) strains, and combinations thereof showed potential for improving the aforementioned organoleptic and nutritional characteristics of fermented plant bases. In four different legume-derived matrices, fermentation improved texture, degraded undesirable plant carbohydrates, and removed off-flavor compounds, while producing desirable dairy-associated compounds. The degradation of the undesirable beany off-flavor-causing compound hexanal appears to be a universal phenomenon, as every tested strain as well as their combinations exhibited the capability to decrease the hexanal content, albeit with varying efficiency. Some LAB strains were found to be capable of producing carotenoids and might hence have the potential for tailoring fermented plant-based matrices for specific applications, such as yellow cheese or red meat analogs.

Jicong Lin, Jingxian Zhang, Gen Zou et al.

Defatted cottonseed meal (DCSM), a byproduct of the cotton industry, is highly regarded for its high protein content, making it a source of nutrients in animal feed. Traditional physical and chemical treatments of DCSM can lead to a reduction in nutrient content and the presence of residual organic solvents. Probiotic fermentation of DCSM offers several advantages, including degradation of anti-nutritional factors, an increase in nutrient content, and production of beneficial metabolites. This study employed probiotic fermentation of DCSM using a probiotic microbe collection composed of <i>Saccharomyces cerevisiae</i>, <i>Enterococcus faecium</i>, and <i>Lactiplantibacillus plantarum</i>. This fermentation process significantly enhanced the nutritional quality of DCSM. Specifically, the contents of crude protein, free amino acid, total phosphorus, and moisture increased by 1.14-fold, 1.14-fold, 1.24-fold, and 3-fold, respectively. In the meanwhile, there was a substantial reduction in the content of dry matter, crude ash, and crude fat, with decreases of 27.83%, 25.74%, and 88.23%, respectively. Probiotic fermentation of DCSM resulted in an overall enhancement of the palatability of DCSM. This study provides valuable insights into the potential of mixed probiotic fermentation as a promising approach for improving the nutritional quality of DCSM.

Keivan Faghih Niresi, Hugo Bissig, Henri Baumann et al.

The Industrial Internet of Things (IIoT) is reshaping manufacturing, industrial processes, and infrastructure management. By fostering new levels of automation, efficiency, and predictive maintenance, IIoT is transforming traditional industries into intelligent, seamlessly interconnected ecosystems. However, achieving highly reliable IIoT can be hindered by factors such as the cost of installing large numbers of sensors, limitations in retrofitting existing systems with sensors, or harsh environmental conditions that may make sensor installation impractical. Soft (virtual) sensing leverages mathematical models to estimate variables from physical sensor data, offering a solution to these challenges. Data-driven and physics-based modeling are the two main methodologies widely used for soft sensing. The choice between these strategies depends on the complexity of the underlying system, with the data-driven approach often being preferred when the physics-based inference models are intricate and present challenges for state estimation. However, conventional deep learning models are typically hindered by their inability to explicitly represent the complex interactions among various sensors. To address this limitation, we adopt Graph Neural Networks (GNNs), renowned for their ability to effectively capture the complex relationships between sensor measurements. In this research, we propose physics-enhanced GNNs, which integrate principles of physics into graph-based methodologies. This is achieved by augmenting additional nodes in the input graph derived from the underlying characteristics of the physical processes. Our evaluation of the proposed methodology on the case study of district heating networks reveals significant improvements over purely data-driven GNNs, even in the presence of noise and parameter inaccuracies.

Lolitta Ammann, Jorge Martinez-Gil, Michael Mayr et al.

Industrial processes generate vast amounts of time series data, yet extracting meaningful relationships and insights remains challenging. This paper introduces a framework for automated knowledge graph learning from time series data, specifically tailored for industrial applications. Our framework addresses the complexities inherent in industrial datasets, transforming them into knowledge graphs that improve decision-making, process optimization, and knowledge discovery. Additionally, it employs Granger causality to identify key attributes that can inform the design of predictive models. To illustrate the practical utility of our approach, we also present a motivating use case demonstrating the benefits of our framework in a real-world industrial scenario. Further, we demonstrate how the automated conversion of time series data into knowledge graphs can identify causal influences or dependencies between important process parameters.

Kwanruthai Malairuang, Pumin Nutaratat, Borworn Werapan et al.

Vip3A (vegetative insecticidal protein) is a representative member of the Vip3 family, which is widely used for lepidopteran pest control. This Vip3A protein, a non-growth-associated protein, is an effective bioinsecticide against insect pests, but there is relatively little information about its production processes at large scales. Hence, the effects of environmental factors on Vip3A production by <i>Bacillus thuringiensis</i> Bt294 (antifoam agents, shaking speeds, agitation and aeration rates), as well as controlling physical conditions such as the lowest point of dissolved oxygen and controlling of culture pH, were observed in shaking flasks and bioreactors. The results showed that antifoam agents, flask types and shaking speeds had significant effects on Vip3A and biomass production. Cultivation without pH control and DO control in 5 L bioreactors at lower agitation and aeration rates, which was not favorable for biomass production, resulted in a high Vip3A protein production of 5645.67 mg/L. The scale-up studies of the Vip3A protein production in a pilot-scale 750 L bioreactor gave 3750.0 mg/L. Therefore, this study demonstrated the significant effects of agitation, aeration rates and culture pH on Vip3A production by <i>B. thuringiensis Bt294</i>. Balancing of physical conditions was necessary for obtaining the highest yield of Vip3A by slowing down the production rate of biomass. Moreover, this Vip3A protein has high potential as a bioinsecticide for lepidopteran pest control in organic crops. This information will be important for significantly increasing the Vip3A protein concentration by the bacterium and will be useful for field application at a lower cost.

Iara Ferreira, Dirceu de Sousa Melo, Marly Silveira Santos et al.

This work aimed to develop a non-dairy functional beverage fermented with probiotic strains and fortified with Brazilian red propolis (microencapsulated and extracted). The non-dairy matrix consisted of oats (75 g), sunflower seeds (175 g), and almonds (75 g). It was fermented by a starter co-culture composed of <i>Lactiplantibacillus plantarum</i> CCMA 0743 and <i>Debaryomyces hansenii</i> CCMA 176. Scanning electron microscopy analysis was initially performed to verify the integrity of the microcapsules. The viability of the microorganisms after fermentation and storage, chemical composition (high performance liquid chromatography (HPLC) and gas chromatography coupled to mass spectrometry (GC-MS) analyses), rheology, antioxidant activity, and sensory profile of the beverages were determined. After fermentation and storage, the starter cultures were well adapted to the substrate, reducing the pH (6.50 to 4) and cell count above 7.0 log CFU/mL. Lactic acid was the main organic acid produced during fermentation and storage. In addition, 39 volatile compounds were detected by gas chromatography coupled to mass spectrometry (GC-MS), including acids, alcohols, aldehydes, alkanes, alkenes, esters, ethers, phenols, terpenes, and others. The addition of propolis extract increased the antioxidant and phenolic activity and the presence of volatile esters but reduced the beverage’s acceptability. The addition of microencapsulated propolis was more associated with the presence of higher alcohols and had similar acceptance to the control beverage. The combination of a non-dairy substrate, a starter co-culture, and the addition of propolis led to the development of a probiotic beverage with great potential for health benefits.

Peter Lorenz, Ilona Zilkowski, Lilo K. Mailänder et al.

Lactic acid (LA) fermentation of dog’s mercury (<i>M. perennis</i> L.) herbal parts was investigated in samples inoculated with either Lactobacteria (<i>Lactobacillus plantarum</i> and <i>Pediococcus pentosaceus</i>, LBF) or whey (WF). Depending on fermentation time, LA concentrations were monitored in a range of 3.4–15.6 g/L with a concomitant pH decline from 6.5 to 3.9. A broad spectrum of cinnamic acids depsides containing glucaric, malic and 2-hydroxyglutaric acids along with quercetin and kaempferol glycosides were detected by LC-DAD-ESI-MSⁿ. Moreover, in this study novel constituents were also found both in unfermented and fermented extracts. Furthermore, amino acids and particular Lactobacteria metabolites such as biogenic amines (e.g., putrescine, 4-aminobutyric acid, cadaverine) and 5-oxoproline were assigned in WF extracts by GC-MS analysis after silylation. Enhanced NFκB and cytokine expression (IL-6, TNFα, IL-8 and IL-1β) was induced by all extracts, both non-fermented and fermented, in NFκB-THP-1 reporter cells, showing a concentration-dependent immunostimulatory effect. The WF extracts were tested for micronuclei formation in THP-1 cells and toxicity in luminescent bacteria (<i>V. fischeri</i>), whereby no mutagenic or toxic effects could be detected, which corroborates their safe use in pharmaceutical remedies.

Borja Lagoa-Costa, Christian Kennes, María C. Veiga

Agroindustrial by-products hold an enormous potential to be bioconverted into high-value-added products such as polyhydroxyalkanoates (PHA), a cost-effective alternative to conventional plastics. In this study, cheese whey, a highly abundant side stream of the cheese making process, was explored as a feasible substrate for the selection of a mixed culture highly enriched in PHA-storing bacteria using a sequencing batch reactor under an aerobic dynamic feeding regime. For that, the absence/presence of thiourea, magnesium and iron, as well as the application of two different organic loading rates (OLR), i.e., 60 and 80 CmM d⁻¹, were tested. The results showed an improved culture selection when thiourea, magnesium and iron were added to the culture medium as well as when the highest OLR was applied. Under these conditions, the biomass achieved a maximum PHA storage of 54% and a PHA production rate of 4.81 Cmmol-PHA L⁻¹ h⁻¹. Additionally, the study of the microbial community showed that during this period of maximum productivity, the biomass was enriched in <i>Azoarcus</i> and <i>Amaricoccus</i> bacterial species. Conclusively, cheese whey can be considered a good feedstock to efficiently select a mixed culture with high potential to accumulate PHA and a good way to give this by-product added value.

Eric Banan-Mwine Daliri, Fred Kwame Ofosu, Ramachandran Chelliah et al.

Soy consumption may reduce hypertension but the impact of food processing on the antihypertensive effect is unclear. Hence, we ascertained the effects of lacto-fermented (FSB) and unfermented soybean (USB) consumption on serum atherogenic lipids, hypertension and gut microbiota of spontaneous hypertensive rats (SHR). FSB displayed a strong in vitro angiotensin converting enzyme (ACE) inhibitory ability of 70 ± 5% while USB inhibited 5 ± 3% of the enzyme activity. Consumption of USB reduced serum ACE activity by 19.8 ± 12.85 U while FSB reduced the enzyme activity by 47.6 ± 11.35 U, respectively. FSB significantly improved cholesterol levels and reduced systolic and diastolic blood pressures by 14 ± 3 mmHg and 10 ± 3 mmHg, respectively, while USB only had a marginal impact on blood pressure. Analysis of FSB showed the abundance of ACE inhibitory peptides EGEQPRPFPFP and AIPVNKP (which were absent in USB) and 30 phenolic compounds (only 12 were abundant in USB). Feeding SHR with FSB promoted the growth of <i>Akkermansia</i>, <i>Bacteroides</i>, <i>Intestinimonas</i>, <i>Phocaeicola</i>, <i>Lactobacillus</i> and <i>Prevotella</i> (short chain fatty acid producers) while USB promoted only <i>Prevotellamassilia</i>, <i>Prevotella</i> and <i>Intestimonas</i> levels signifying the prebiotic ability of FSB. Our results show that, relative to USB, FSB are richer in bioactive compounds that reduce hypertension by inhibiting ACE, improving cholesterol levels and mitigating gut dysbiosis.

Yujiao Hu, Qingmin Jia, Yuao Yao et al.

The fiercely competitive business environment and increasingly personalized customization needs are driving the digital transformation and upgrading of the manufacturing industry. IIoT intelligence, which can provide innovative and efficient solutions for various aspects of the manufacturing value chain, illuminates the path of transformation for the manufacturing industry. It's time to provide a systematic vision of IIoT intelligence. However, existing surveys often focus on specific areas of IIoT intelligence, leading researchers and readers to have biases in their understanding of IIoT intelligence, that is, believing that research in one direction is the most important for the development of IIoT intelligence, while ignoring contributions from other directions. Therefore, this paper provides a comprehensive overview of IIoT intelligence. We first conduct an in-depth analysis of the inevitability of manufacturing transformation and study the successful experiences from the practices of Chinese enterprises. Then we give our definition of IIoT intelligence and demonstrate the value of IIoT intelligence for industries in fucntions, operations, deployments, and application. Afterwards, we propose a hierarchical development architecture for IIoT intelligence, which consists of five layers. The practical values of technical upgrades at each layer are illustrated by a close look on lighthouse factories. Following that, we identify seven kinds of technologies that accelerate the transformation of manufacturing, and clarify their contributions. The ethical implications and environmental impacts of adopting IIoT intelligence in manufacturing are analyzed as well. Finally, we explore the open challenges and development trends from four aspects to inspire future researches.

Shusen Wang

Recommender system (RS) is an established technology with successful applications in social media, e-commerce, entertainment, and more. RSs are indeed key to the success of many popular APPs, such as YouTube, Tik Tok, Xiaohongshu, Bilibili, and others. This paper explores the methodology for improving modern industrial RSs. It is written for experienced RS engineers who are diligently working to improve their key performance indicators, such as retention and duration. The experiences shared in this paper have been tested in some real industrial RSs and are likely to be generalized to other RSs as well. Most contents in this paper are industry experience without publicly available references.

Cuiqin Li, Qing Zhang, Chan Wang et al.

Hongsuantang (HST) is a traditional Chinese and famous sour soup. However, the quality of naturally fermented HST is not controllable. We investigated the effects of different lactic acid bacteria starters on HST acid production, color, antioxidant capacity, total phenols, total carotenoids, organic acids, volatile substances, and sensory properties to determine the most suitable strain for HST production. The results showed that among the seven lactic acid bacteria strains used to inoculate fermented HST, <i>Lactiplantibacillus plantarum</i> SQ-4 exhibited the most excellent fermentation characteristics. SQ-4 rapidly reduced the HST’s pH by 0.77. It significantly increased the HST’s color, organic acids, total phenols, carotenoids, lycopene, and free radical scavenging ability. <i>Lactiplantibacillus plantarum</i> SQ-4 was an excellent starter for preparing HST with good acid production capacity, moderate sourness and spiciness, and good sensory and other characteristics. Each starter produces its distinct flavor components. α-Pinene, myrcene, α-copaene, and guaiol were vital aroma compounds in HST fermentation by the starter. This study laid a foundation for selecting HST starters and potential industrial production.