Efficient Factories Research Articles

Stability of a Mutualistic Escherichia coli Co-Culture During Violacein Production Depends on the Kind of Carbon Source.

The L-tryptophan-derived purple pigment violacein (VIO) is produced in recombinant bacteria and studied for its versatile applications. Microbial synthetic co-cultures are gaining more importance as efficient factories for synthesizing high-value compounds. In this work, a mutualistic and cross-feeding Escherichia coli co-culture is metabolically engineered to produce VIO. The strains are genetically modified by auxotrophies in the tryptophan (TRP) pathway to enable a metabolic division of labor. Therein, one strain produces anthranilate (ANT) and the other transforms it into TRP and further to VIO. Population dynamics and stability depend on the choice of carbon source, impacting the presence and thus exchange of metabolites as well as overall VIO productivity. Four carbon sources (D-glucose, glycerol, D-galactose, and D-xylose) were compared. D-Xylose led to co-cultures which showed stable growth and VIO production, ANT-TRP exchange, and enhanced VIO production. Best titers were ∼126mgL-1 in shake flasks. The study demonstrates the importance and advantages of a mutualistic approach in VIO synthesis and highlights the carbon source's role in co-culture stability and productivity. Transferring this knowledge into an up-scaled bioreactor system has great potential in improving the overall VIO production.

Modeling and Analyzing the Strategy Game “Factorio” Using Modular Petri Nets and the General-Purpose Petri Net Simulator

The video game Factorio is a popular real-time strategy game that hosts a variety of gameplay scenarios analogous to real-world problems such as factory building, pipeline optimization, and conveyor belt logistics. It also considers environmental costs such as pollution and energy consumption. This game focuses on automation and resource management within the context of creating and maintaining factories. Players are challenged to design efficient factories, automate production, control energy consumption, and balance resource utilization, all while defending against environmental challenges such as pollution and hostile enemies. Factorio is a complex software; however, studies have demonstrated its ability to enhance student satisfaction when integrated as an educational module at the university level. This paper aims to utilize the power of Petri Net theory and the practical tool GPenSIM to model and simulate several Factorio pipelines and score them against one another, exploring which pipeline configuration is optimal. The primary objective is to make a mathematical model (Petri Net) to better understand the game’s complex mechanisms, identify bottlenecks within the system, and explore opportunities for optimization within the network of modules. The insights gained from this paper may find practical applications in real-world scenarios.

Reevaluating the Origin of Detectable Cataclysmic Variables in Globular Clusters: Testing the Importance of Dynamics

Based on the current detectable cataclysmic variable (CV) population in Galactic globular clusters (GCs), we show that there is not a clear relation between the number of sources per unit of mass and the stellar encounter rate, the cluster mass, or the cluster central density. If any, only in the case of core-collapsed GCs could there be an anticorrelation with the stellar encounter rate. Our findings contrast with previous studies where clear positive correlations were identified. Our results suggest that correlations between faint X-ray sources, from which often conclusions for the CV population are drawn, and the GC parameters considered here, are likely influenced by other type of X-ray sources, including other types of compact binaries, which have X-ray luminosities similar to CVs. The findings presented here also suggest that the role of primordial systems is more important than previously believed and that dynamical formation has less influence in the current detectable CV population. The long-standing paradigm that GCs are efficient factories of CVs formed via dynamical interactions does not seem to be supported by current observations.

Occurrence and influencing factors of high groundwater manganese in the oxbow lakes of the middle reaches of Yangtze River

Oxbow lake, acting as an efficient factory, plays an important role in the attenuation and accumulation of groundwater contamination. However, there are few concerns on the enrichment of manganese in the oxbow lakes. Based on 113 groundwater samples collected from Wuhan Geological Survey Center, China Geological Survey in 2019, the distribution and influencing factors of high groundwater Mn were studied in the oxbow lakes of the middle reaches of Yangtze River. The results showed that the elevated groundwater Mn were mainly distributed in the concave bank with 75 % of Mn concentration exceeded 0.10 mg/L, and up to 3.79 mg/L. Moreover, groundwater Mn concentrations were related to the depth and the well types. According to principal component analysis, Mn and HCO3–, Mg2+, Fe, NH4-N, Ca2+, As, Eh and NO3– dominated in PC1 with significantly correlations which may result from natural attenuation processes. The sedimentary environments including geological properties and reducing conditions in the studied area were responsible for the enrichment. Not only the dissolution of rich Mn-bearing minerals in the studied area driven by redox conditions provided the source of groundwater Mn but also the unique sedimentary architecture characteristics and the slow flow limited the transfer of dissolved Mn in groundwater, resulting in the enrichment of Mn. This study provides us understand the occurrence and influencing factors of high groundwater manganese in the oxbow lakes/wetlands.

Hot Circumsingle Disks Drive Binary Black Hole Mergers in Active Galactic Nucleus Disks

Abstract Binary black hole (BBH) mergers, particularly those with component masses in the pair-instability gap, may be produced by hierarchical mergers in the disks surrounding Active Galactic Nuclei (AGNs). While the interaction of an embedded BBH with an AGN disk is typically assumed to facilitate a merger, recent high-resolution hydrodynamical simulations challenge this assumption. However, these simulations often have simplified treatments for gas thermodynamics. In this work, we model the possible consequence of various feedback from an embedded BBH with a simple model that maintains an enhanced temperature profile around each binary component. We show that when the minidisks around each BH become hotter than the background by a factor of three, the BBH orbital evolution switches from expansion to contraction. By analyzing the gravitational torque profile, we find that this change in direction is driven by a weakening of the minidisk spirals and their positive torque on the binary. Our results highlight the important role of thermodynamics around BBHs and its effect on their orbital evolution, suggesting that AGN disks could be efficient factories for BBH mergers.

A CO2 sink in a tropical coastal lagoon impacted by cultural eutrophication and upwelling

Tropical coastal lagoons are recognized as very productive ecosystems. However, very little is known on the direction and magnitude of their CO2 exchange with the atmosphere. We report on air-water CO2 fluxes and carbon concentrations in a tropical shallow lagoon impacted by cultural eutrophication. During two contrasting field surveys, the first in Nov. 2019 during dry conditions and the second in Feb. 2020 during wet summer condition and intrusion of coastal upwelling waters inside the lagoon. At both seasons, spatial in situ monitoring of temperature, salinity and partial pressure of CO2 (pCO2) reveal the presence of undersaturated pCO2 values (between 60 and 300 ppmv), uptake of atmospheric CO2 (between 0.9 and 9.2 mmol m−2 h−1) inside the lagoon, at salinities between 10 and 32. Temporal monitoring data at the mouth of the lagoon throughout tidal cycles revealed the entrance of cold South Atlantic Central Waters (SACW) from the Cabo Frio's upwelling during flood tides in Feb. 2020 but not in Nov. 2019. The cold SACW with temperatures down to 16 °C were supersaturated in CO2 (pCO2 about 520 ppmv) and a CO2 source to the atmosphere (between 0.2 and 1.4 mmol m−2 h−1). During mixing of this SACW in the most marine compartment of the lagoon, the biological CO2 uptake and enhanced autotrophy overcame the effect of degassing SACW waters by heating. For both cruises, a consistent landward trend consisted in a decrease in pCO2 and dissolved inorganic carbon (DIC) mirrored by an increase of dissolved organic carbon (DOC) (between 4.6 and 19.8 mg L−1), particulate organic carbon (POC) (between 3.3 and 12.3 mg L−1), and chlorophyll a (Chl a) (between 7.6 and 217 μg L−1). POC, DOC and Chl a and the POC:Chl a ratio suggest that, organic carbon in the lagoon is a mixture of phytoplankton biomass produced in the lagoon, domestic wastewater loaded from multiple point sources and pulsated inputs from small rivers. Our study in the Saquarema lagoon confirms recent works in other densely populated tropical coastal ecosystems, revealing that eutrophication enhances the autotrophy of shallow tropical lagoons, making them act as efficient factories for atmospheric CO2 absorption and organic carbon storage in sediments and export to the ocean.

Insights into cyanobacterial alkane biosynthesis.

Alkanes are high-energy molecules that are compatible with enduring liquid fuel infrastructures, which make them highly suitable for being next-generation biofuels. Though biological production of alkanes has been reported in various microorganisms, the reports citing photosynthetic cyanobacteria as natural producers have been the most consistent for the long-chain alkanes and alkenes (C15–C19). However, the production of alkane in cyanobacteria is low, leading to its extraction being uneconomical for commercial purposes. In order to make alkane production economically feasible from cyanobacteria, the titre and yield need to be increased by several orders of magnitude. In the recent past, efforts have been made to enhance alkane production, although with a little gain in yield, leaving space for much improvement. Genetic manipulation in cyanobacteria is considered challenging, but recent advancements in genetic engineering tools may assist in manipulating the genome in order to enhance alkane production. Further, advancement in a basic understanding of metabolic pathways and gene functioning will guide future research for harvesting the potential of these tiny photosynthetically efficient factories. In this review, our focus would be to highlight the current knowledge available on cyanobacterial alkane production, and the potential aspects of developing cyanobacterium as an economical source of biofuel. Further insights into different metabolic pathways and hosts explored so far, and possible challenges in scaling up the production of alkanes will also be discussed.

FOMs 패키지 내 FOM 시스템을 활용한 제조현장 빅데이터 기반의 생산성 분석 방법에 관한 연구

The key to transforming the manufacturing industry is the appropriate application of information technology and the utilization of data. Therefore, a method for efficient factory operation management, as well as IT system introduction, is important. From this, it is possible to create data-driven value and strengthen the competitiveness of enterprises. In this paper, we propose a FOMs (smart-Factory Operation Managements) Package method based on 4M data of manufacturing sites to improve productivity and competitiveness. In addition, we performed a case study by applying part of the FOMs Package to the manufacturer. We performed a detailed and multi-dimensional analysis of the MES/POP data that the manufacturer managed, and were able to derive not only the products that needed improvement but also related facilities, workers, downtimes, and defects. As a result, we found that the proposed method was effective in analyzing 4M manufacturing data and making decisions for factory operation management.

Bibliometric Study on the Use of Machine Learning as Resolution Technique for Facility Layout Problems

Abstract Facility Layout Problems (FLP) are concerned with finding efficient factory layouts. Numerous resolution approaches are known in literature for layout optimization. Among those, intelligent approaches are less researched than solutions from exact or approximating approaches. The recent surge of research interest in Artificial Intelligence, and specifically Machine Learning (ML) techniques, presages an increase of such techniques’ usage in FLP. However, previous reviews on FLP research induce that, to date, this trend has not yet emerged. Utilizing a systematic literature review coupled with a k-Means based clustering algorithm, we analyzed 25 relevant publication full-texts from an original sample of 1,425 papers. Our findings corroborate the statement that ML techniques have attracted substantially less research interest than most other resolution approaches. While a few papers used Unsupervised Learning algorithms directly as a solution to the FLP, Supervised and Reinforcement Learning were found to be practically irrelevant. ML usage was significantly higher in FLP-adjacent planning tasks such as group technology. Drawing from experiences with other NP-hard combinatorial optimization problems in manufacturing research, we conclude that Reinforcement Learning is most promising to bridge the evident gap between FLP and ML research. Our study further contributes to FLP research by extending established classification frameworks.

Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints

The industry 4.0 concepts are moving towards flexible and energy efficient factories. Major flexible production lines use battery-based automated guided vehicles (AGVs) to optimize their handling processes. However, optimal AGV battery management can significantly shorten lead times. In this paper, we address the scheduling problem in an AGV-based job-shop manufacturing facility. The considered schedule concerns three strands: jobs affecting machines, product transport tasks’ allocations and AGV fleet battery management. The proposed model supports outcomes expected from Industry 4.0 by increasing productivity through completion time minimization and optimizing energy by managing battery replenishment. Experimental tests were conducted on extended benchmark literature instances to evaluate the efficiency of the proposed approach.

Employee Engagement and Achievement Perception of Thai Synchrotron Institute

It is a purpose of this work to figure out engagement level of the employees of Synchrotron Light Research Institute (SLRI), a public organization owned by the Government of Thailand. We used a questionnaire survey to explore the issue. Another useful point of interest is employees’ perception of organization achievement. So, we launched a 3-part questionnaire to inquire personal information, level of engagement, and perception of achievement. SPSS package was used to analyze the results. It is found that the level of engagement of our employees at large is high. According to the employees’ job functions, different groups of employees have different sub-group norms. Scientists and researchers are engaged in creative and challenging jobs; engineers, technicians, and safety personnel are engaged to team, well-planned jobs, success, and reputation; back-office staff are engaged to well-planned jobs, accountability, and ethicality. Accordingly, scientists and researchers perceive the institute as a learning organization (LO), as an effective and efficient factory line for engineers and technicians, and as a high performance organization (HPO) for back-office staff.

New Approach for Digital Factory Using Virtual Reality Technology

The digital factory represents an indispensable method for factory layout planning. Based on digital factory methods numerous tools for layout planning have been developed in the last years, which enable the space organization of the assets of a factory based on 2D/3D data. Such tools have a difficulty to show the whole layout plan and to interact and to handle with the factory assets in intuitive and realistic manner. This paper presents and discuss a VR-based approach for efficient factory planning. This approach enables fully immersion in the virtual environment represented factory and an intuitive interaction with factory equipment. Hence, the layout planning can be assessed in real time regarding e.g. efficiency of surface use, martial flow, ergonomics. The presented approach has been designed based on the many years experience of the author as digital factory engineer in the industry and based on intensive discussion with industry experts.

Post-Newtonian dynamics in dense star clusters: Binary black holes in the LISA band

The dynamical processing of black holes in the dense cores of globular clusters (GCs), makes them efficient factories for producing binary black holes (BBHs). Here we explore the population of BBHs that form dynamically in GCs and may be observable at mHz frequencies or higher with LISA. We use our Monte Carlo stellar dynamics code, which includes gravitational radiation reaction effects for all BH encounters. By creating a representative local universe of GCs, we show that up to dozens of these systems may be resolvable by LISA with signal-to-noise ratios of at least 5. Approximately one third of these binaries will have measurable eccentricities ($e > 10^{-3}$) in the LISA band and a small number ($\lesssim 5$) may evolve from the LISA band to the LIGO band during the LISA mission.

Exploring the potential of Operator 4.0 interface and monitoring

In the context of smart factories, where intelligent machines share data and support enhanced functionalities at a factory level, workers are still seen as spectators rather than active players (Hermann, Pentek, & Otto, 2017). Instead, Industry 4.0 represents a great opportunity for workers to become part of the intelligent system; on one hand, operators can generate data to program machines and optimize the process flows, on the other hand they can receive useful information to support their work and cooperate with smart systems (Romero et al., 2016). Diversely from machines, humans are naturally smart, flexible and intelligent, so putting the operators in the digital loop can bring more powerful and efficient factories. The paper aims at defining a theoretical human-centered framework for Operator 4.0, and testing its feasibility and impact on companies, thanks to the integration of human factors in 4.0 computerized industrial contexts. The proposed framework is based on data collection about the workers’ performance, actions and reactions, with the final objective to improve the overall factory performance and organization. Data are used to assess the workers’ ergonomics performance and perceived comfort and to build a proper knowledge about the human asset of the factory, to be integrated with the knowledge derived from machine data collection. The framework is cased on the adoption of an Operator 4.0 monitoring system, which consists of an eye tracking and a wearable biosensor, combined to a proper protocol analysis to interpret data and create a solid knowledge. Virtual prototypes are used to make the workers interact with the digital factory to conveniently simulate the human–machine interaction (HMI) in order to avoid bottlenecks at the shop floor, to optimize the workflows, and to improve the workstations’ design and layout. The study represents a step toward the design of human-centred industrial systems, including human factors in the digital twin. The research approach has been successfully tested on an industrial case study, developed in collaboration with CNH Industrial, for the re-design of assembly workstations.

Türkiye de Kamu Şeker Fabrikaları İçin Bir Etkinlik Analizi: 2003-2016 - An Efficiency Analysis for the State-Owned Sugar Factorıes in Turkey: 2003-2016

The agricultural sector, as a whole, is among the strategic sectors, especially for developing countries. Sugar production and the sub-products provide very important contribution to the economy. The purpose of this study is to analyze the efficiency of 25 sugar factories belongs Turkish Sugar Industry between the years 2003-2016. Data Envelopment Analysis and Malmquist Index were used for calculation and evaluation of the efficiency scores. Six inputs (arable beet area, farmers, factory workers, amount of processed beet, fuel consumption and accumulated depreciation) and two outputs (crystal sugar and molasses) were used for measurement of efficiency. According to results, Corum, Eregli, Erzurum and Kars are the most efficient factories during the related periods. In addition to this, especially after 2012, as a whole, a decline in the efficiency of the factories is remarkable.

Endoplasmic Reticulum redox pathways: in sickness and in health.

The Endoplasmic Reticulum (ER) is the major site for secretory protein production in eukaryotic cells and like an efficient factory, it has the capacity to expand or contract its output depending on the demand for its services. A primary function of the ER is to co-ordinate the quality control of proteins as they enter this folding factory at the base of the secretory pathway. Reduction-oxidation (redox) reactions have an important role to play in the quality control process, through the provision of disulphide bonds and by maintaining a favourable redox environment for oxidative protein folding. The ER is also a major contributor to calcium homeostasis and is a key site for lipid biosynthesis, two processes that additionally impact upon, and are influenced by, redox in the ER compartment.

DCEO Biotechnology: Tools To Design, Construct, Evaluate, and Optimize the Metabolic Pathway for Biosynthesis of Chemicals.

Chemical synthesis is a well established route for producing many chemicals on a large scale, but some drawbacks still exist in this process, such as unstable intermediates, multistep reactions, complex process control, etc. Biobased production provides an attractive alternative to these challenges, but how to make cells into efficient factories is challenging. As a key enabling technology to develop efficient cell factories, design-construction-evaluation-optimization (DCEO) biotechnology, which incorporates the concepts and techniques of pathway design, pathway construction, pathway evaluation, and pathway optimization at the systems level, offers a conceptual and technological framework to exploit potential pathways, modify existing pathways and create new pathways for the optimal production of desired chemicals. Here, we summarize recent progress of DCEO biotechnology and examples of its application, and provide insights as to when, what and how different strategies should be taken. In addition, we highlight future perspectives of DCEO biotechnology for the successful establishment of biorefineries.

Digital Manufacturing Systems: A Framework to Improve Social Sustainability of a Production Site

The topic of digital manufacturing is increasingly emerging in industry. One of the main scope of data digitalization is achieving more efficient factories. Different techniques and tools under the Industry 4.0 paradigm were already discussed in literature. These are aimed mostly at boosting company efficiency in terms of costs and environmental footprint. However, from a sustainability point of view, the social theme must be equally considered. While energy flows or costs can be already monitored in a production plant, this is not valid for data related to human effort. Monitoring systems aimed at supervising factory social sustainability were not already discussed in literature. The aim of this paper is to propose a method to acquire social related data in a production plant. The method is supported by a smart architecture within the concept of IoT factory. Such architecture permits to monitor the parameters that could influence social sustainability in a production site. After a discussion on production plants facilities and features, the parameters that need to be considered to guarantee socially sustainable manufacturing processes are identified. A set of sensors controls these data taken from different sources, including operator vital signs. Operations as well as humans are monitored. Data acquired by sensors are collected by a central server. A decision maker can interpret the data and improve the production system from a social point of view, implementing corrective actions. Data can be exploited not only for social assessments but even for other analyses on the production system. Guaranteeing social sustainability could boost the factory productivity.A case study is included in the paper: smart sensors are implemented in a production line to understand the operations efficiency in terms of social sustainability.

Transient Performance Analysis of Serial Production Lines With Geometric Machines

A production system is characterized by both its steady state and transient properties. While extensive research efforts have been spent in the analysis of the steady state of production systems, very few results, especially analytical ones, have been reported regarding their transient behavior. Indeed, transient behavior of production systems has significant practical and theoretical implications. A better understanding of the transient properties of production systems is critical to effective utilization of real-time production data for efficient factory floor operation and management. In the framework of serial production lines with geometric machines and finite buffers, this paper develops mathematical models for transient analysis and derives closed-form expressions for evaluating the production rate, consumption rate, work-in-process, and probabilities of machine starvation and blockage, during transients. In addition, a computationally efficient algorithm based on aggregation is developed to approximate the transient performance measures with high accuracy. Numerical experiments show that the methods developed can be applied to systems with time-varying machine parameters as well.

ETA Learning Factory: A Holistic Concept for Teaching Energy Efficiency in Production

Considering the fact that industrial processes account for 19.1% of the German primary energy demand, it is necessary to raise awareness and promote energy efficiency technologies in this sector to reach the climate targets of the European Union. In the ETA research factory (Energy efficiency, Technology and Application center) in Darmstadt a holistic approach for an energy efficient factory is examined. The interdisciplinary research areas include several aspects of energy efficiency measures in the building, supply technology and production machines. This paper presents a training concept for transferring the multidisciplinary technical and methodical know-how about the proposed energy efficiency measures to industry and engineering students with an adapted didactical concept.