Factory Production Research Articles

Contribution to the design of prestressed hollow core purlins without transverse reinforcement

AbstractThe hollow core purlin is a structural element resulting from the sectioning of hollow core slabs into two ribs. This component can be classified as a prestressed hollow core beam without transverse reinforcement. Although this solution offers potential for increasing the productivity of precast concrete element factories, it currently lacks normative guidelines due to the high risk of shear failure. The aim of this study is to highlight an equation to underpin the design of hollow core purlins based on experimental verification. To achieve this objective, six samples of hollow core purlins were analyzed. Initially, the manufacturing characteristics and cross‐sectional geometry of each piece were observed to gather data on the production process. Subsequently, positive bending tests were conducted to characterize stiffness and determine the effective mean tensile strength. Later, shear strength tests were performed to identify the failure mechanisms of the elements. In parallel, the prestressing forces, stiffness, tensile strength, cracking moment, calculated shear strength using the flexural‐shear mechanism, and verification of shear strength due to diagonal tension were computed based on concrete compressive strength values and data provided by the manufacturer. Finally, the experimental values obtained were compared with the theoretical calculated values to establish a correlation that highlights the calculation procedure that best represents the behavior of the studied elements.

13 C-MFA helps to identify metabolic bottlenecks for improving malic acid production in Myceliophthora thermophila

BackgroundMyceliophthora thermophila has been engineered as a significant cell factory for malic acid production, yet strategies to further enhance production remain unclear and lack rational guidance. 13C-MFA (13C metabolic flux analysis) offers a means to analyze cellular metabolic mechanisms and pinpoint critical nodes for improving product synthesis. Here, we employed 13C-MFA to investigate the metabolic flux distribution of a high-malic acid-producing strain of M. thermophila and attempted to decipher the crucial bottlenecks in the metabolic pathways.ResultsCompared with the wild-type strain, the high-Malic acid-producing strain M. thermophila JG207 exhibited greater glucose uptake and carbon dioxide evolution rates but lower oxygen uptake rates and biomass yields. Consistent with these phenotypes, the 13C-MFA results showed that JG207 displayed elevated flux through the EMP pathway and downstream TCA cycle, along with reduced oxidative phosphorylation flux, thereby providing more precursors and NADH for malic acid synthesis. Furthermore, based on the 13C-MFA results, we conducted oxygen-limited culture and nicotinamide nucleotide transhydrogenase (NNT) gene knockout experiments to increase the cytoplasmic NADH level, both of which were shown to be beneficial for malic acid accumulation.ConclusionsThis work elucidates and validates the key node for achieving high malic acid production in M. thermophila. We propose effective fermentation strategies and genetic modifications for enhancing malic acid production. These findings offer valuable guidance for the rational design of future cell factories aimed at improving malic acid yields.

Rational and Semirational Approaches for Engineering Salicylate Production in Escherichia coli.

Salicylate plays a pivotal role as a pharmaceutical intermediate in drugs, such as aspirin and lamivudine. The low catalytic efficiency of key enzymes and the inherent toxicity of salicylates to cells pose significant challenges to large-scale microbial production. In this study, we introduced the salicylate synthase Irp9 into an l-phenylalanine-producing Escherichia coli, constructing the shortest salicylate biosynthetic pathway. Subsequent protein engineering increased the catalytic efficiency of Irp9 by 33.5%. Furthermore, by integrating adaptive evolution with transcriptome analysis, we elucidated the crucial mechanism of efflux proteins in salicylate tolerance. The elucidation of this mechanism guided us in the targeted modification of these transport proteins, achieving a reported maximum level of 3.72 g/L of salicylate in a shake flask. This study highlights the importance of efflux proteins for enhancing the productivity of microbial cell factories in salicylate production, which also holds potential for application in the green synthesis of other phenolic acids.

Impact of smart factory adoption on manufacturing performance and sustainability: an empirical analysis

PurposePrevious studies have focused on explaining the developmental paths and the relevant skills necessary for smart factories, based on an extensive review of the literature. Unfortunately, there is a deficit of empirical analyses that present an in-depth overview of smart factory development. Although the literature supports the benefits of smart factories, it remains unclear whether there should be government intervention (GI) to facilitate or regulate such adoption. This study will provide an in-depth empirical analysis of smart factory adoption (SFA) and its role in manufacturing performance (MP) and sustainable manufacturing (SM).Design/methodology/approachThis study used non-probability convenience and referral sampling techniques for data collection. This approach considered production managers from each company that participated in the survey questionnaire; thus, each production manager represented one firm. A total of 240 managers from several manufacturing companies participated in the study. This study employed direct and moderating hypotheses tested using PROCESS Macro, which Andrew Hayes developed for SPSS.FindingsThe study identified three fundamental elements of a smart factory: manufacturing big data (MBD), process automation (PA) and supply chain integration (SCI) and analyzed them individually to see how they affect MP. According to the results, building a smart factory has positive and significant impacts on MP and SM. Furthermore, this study explores the role of GI in promoting smart factory deployment for both production performance and sustainable production. The study found that GI did not have a significant moderating effect but did have a positive relationship with SM.Research limitations/implicationsThis study contributes to the literature on smart factories by examining the impact of SFA on MP and SM. This provides a more comprehensive overview of the potential benefits of smart factories across various aspects, such as the application of big data, adoption of automation technology and integration of the supply chain. This study suggests that managers and decision-makers consider investing in smart factory implementation to improve factory productivity and enhance sustainability. Policymakers and government officials can promote the adoption of smart factories by providing incentives, funding, and resources to manufacturing firms.Originality/valueThere is a scarcity of research measuring the actual performance of manufacturing firms that have already adopted smart factories, and this study seeks to address this gap in the literature. This study focuses on the implementation of manufacturing big data, process automation and supply chain integration and how the adoption of these technologies improves MP and provides a SM environment by conducting a real-time study of manufacturing organizations. This study presents an initial effort to explore the role of government involvement in promoting smart factories.

Effects of Different LED Spectra on the Antioxidant Capacity and Nitrogen Metabolism of Chinese Cabbage (Brassica rapa L. ssp. Pekinensis)

Light quality optimization is a cost-effective method for increasing leafy vegetable quality in plant factories. Light-emitting diodes (LEDs) that enable the precise modulation of light quality were used in this study to examine the effects of red-blue (RB), red-blue-green (RBG), red-blue-purple (RBP), and red-blue-far-red (RBF) lights on the growth, antioxidant capacity, and nitrogen metabolism of Chinese cabbage leaves, while white light served as the control (CK). Results showed that the chlorophyll, carotenoid, vitamin C, amino acid, total flavonoid, and antioxidant levels of Chinese cabbage were all significantly increased under RBP combined light treatment. Meanwhile, RBG combined light treatment significantly increased the levels of amino acids but decreased the nitrite content of Chinese cabbage. In addition, RBF combined light treatment remarkably increased the amino acid levels but decreased the antioxidant capacity of Chinese cabbage. In conclusion, the addition of purple light to red-blue light was effective in improving the nutritional value and antioxidant capacity of Chinese cabbage. This light condition can be used as a model for a supplemental lighting strategy for leafy vegetables in plant factory production.

Internet of Things and Distributed Computing Systems in Business Models

The integration of the Internet of Things (IoT) and Distributed Computing Systems (DCS) is transforming business models across industries. IoT devices allow immediate monitoring of equipment and processes, mitigating lost time and enhancing efficiency. In this case, manufacturing companies use IoT sensors to monitor machinery, predict failures, and schedule maintenance. Also, automation via IoT reduces manual intervention, resulting in boosted productivity in smart factories and automated supply chains. IoT devices generate this vast amount of data, which businesses analyze to gain insights into customer behavior, operational inefficiencies, and market trends. In turn, Distributed Computing Systems process this data, providing actionable insights and enabling advanced analytics and machine learning for future trend predictions. While, IoT facilitates personalized products and services by collecting data on customer preferences and usage patterns, enhancing satisfaction and loyalty, IoT devices support new customer interactions, like wearable health devices, and enable subscription-based and pay-per-use models in transportation and utilities. Conversely, real-time monitoring enhances security, as distributed systems quickly respond to threats, ensuring operational safety. It also aids regulatory compliance by providing accurate operational data. In this way, this study, through a Bibliometric Literature Review (LRSB) of 91 screened pieces of literature, aims at ascertaining to what extent the aforementioned capacities, overall, enhance business models, in terms of efficiency and effectiveness. The study concludes that those systems altogether leverage businesses, promoting competitive edge, continuous innovation, and adaptability to market dynamics. In particular, overall, the integration of both IoT and Distributed Systems in business models augments its numerous advantages: it develops smart infrastructures e.g., smart grids; edge computing that allows data processing closer to the data source e.g., autonomous vehicles; predictive analytics, by helping businesses anticipate issues e.g., to foresee equipment failures; personalized services e.g., through e-commerce platforms of personalized recommendations to users; enhanced security, while reducing the risk of centralized attacks e.g., blockchain technology, in how IoT and Distributed Computing Systems altogether impact business models. Future research avenues are suggested.

A Computer Vision Model for Seaweed Foreign Object Detection Using Deep Learning

Seaweed foreign object detection has become crucial for food consumption and industrial use. This process not only can prevent potential health issues, but also maintain the overall marketability of seaweed production in the food industry. Traditional methods of inspecting seaweed foreign objects heavily rely on human judgment, which deals with large volumes with diverse impurities and can be inconsistent and inefficient. An automation system for real-time seaweed foreign object detection in the inspection process should be adopted. However, automated seaweed foreign object detection has several challenges due to its dependency on visual input inspection, such as an uneven surface and undistinguishable impurities. In fact, limited access to advanced technologies and high-cost equipment would also influence visual input acquisition, thereby hindering the advancement of seaweed foreign object detection in this field. Therefore, we introduce a computer vision model utilizing a deep learning-based algorithm to detect seaweed impurities and classify the samples into ‘clean’ and ‘unclean’ categories. In this study, we managed to identify six types of seaweed impurities including sand sticks, shells, discolored seaweed, grass, worm shells, and mixed impurities. We collected 1204 images and our model’s performance was thoroughly evaluated based on comparisons with three pre-trained models, i.e., Yolov8, ResNet, and MobileNet. Our experiment shows that Yolov8 outperforms the other two models with an accuracy of 98.86%. This study also included the development of an Android application to validate the deep learning engine to ensure its optimal performance. Based on our experiments, the mobile application managed to classify 50 pieces of seaweed samples within 0.2 s each, showcasing its potential use in large-scale production lines and factories. This research demonstrates the impact of Artificial Intelligence on food safety by offering a scalable and efficient solution that can be deployed in other food production processes facing similar challenges. Our approach paves the way for broader industry adoption and advancements in automated foreign object detection systems by optimizing detection accuracy and speed.

Adjustment of the main biosynthesis modules to enhance the production of l-homoserine in Escherichia coli W3110.

l-homoserine is an important platform compound of many valuable products. Construction of microbial cell factory for l-homoserine production from glucose has attracted a great deal of attention. In this study, l-homoserine biosynthesis pathway was divided into three modules, the glucose uptake and upstream pathway, the downstream pathway, and the energy supply module. Metabolomics of the chassis strain HS indicated that the supply of ATP was inadequate, therefore, the energy supply module was firstly modified. By balancing the ATP supply module, the l-homoserine production increased by 66% to 12.55 g/L. Further, the results indicated that the upstream pathway was blocked, and increasing the culture temperature to 37°C could solve this problem and the l-homoserine production reached 21.38 g/L. Then, the downstream synthesis pathways were further strengthened to balance the fluxes, and the l-homoserine production reached the highest reported level of 32.55 g/L in shake flasks. Finally, fed-batch fermentation in a 5-L bioreactor was conducted, and l-homoserine production could reach to 119.96 g/L after 92 h cultivation, with the yield of 0.41 g/g glucose and productivity of 1.31 g/L/h. The study provides a well research foundation for l-homoserine production by microbial fermentation with the capacity for industrial application.

Competition between homologous chromosomal DNA and exogenous donor DNA to repair CRISPR/Cas9-induced double-strand breaks in Aspergillus niger

BackgroundAspergillus niger is well-known for its high protein secretion capacity and therefore an important cell factory for homologous and heterologous protein production. The use of a strong promoter and multiple gene copies are commonly used strategies to increase the gene expression and protein production of the gene of interest (GOI). We recently presented a two-step CRISPR/Cas9-mediated approach in which glucoamylase (glaA) landing sites (GLSs) are introduced at predetermined sites in the genome (step 1), which are subsequently filled with copies of the GOI (step 2) to achieve high expression of the GOI.ResultsHere we show that in a ku70 defective A. niger strain (Δku70), thereby excluding non-homologous end joining (NHEJ) as a mechanism to repair double-stranded DNA breaks (DSBs), the chromosomal glaA locus or homologous GLSs can be used to repair Cas9-induced DSBs, thereby competing with the integration of the donor DNA containing the GOI. In the absence of exogenously added donor DNA, the DSBs are repaired with homologous chromosomal DNA located on other chromosomes (inter-chromosomal repair) or, with higher efficiency, by a homologous DNA fragment located on the same chromosome (intra-chromosomal repair). Single copy inter-chromosomal homology-based DNA repair was found to occur in 13–20% of the transformants while 80–87% of the transformants were repaired by exogenously added donor DNA. The efficiency of chromosomal repair was dependent on the copy number of the potential donor DNA sequences in the genome. The presence of five homologous DNA sequences, resulted in an increased number (35–61%) of the transformants repaired by chromosomal DNA. The efficiency of intra-chromosomal homology based DSB repair in the absence of donor DNA was found to be highly preferred (85–90%) over inter-chromosomal repair. Intra-chromosomal repair was also found to be the preferred way of DNA repair in the presence of donor DNA and was found to be locus-dependent.ConclusionThe awareness that homologous chromosomal DNA repair can compete with donor DNA to repair DSB and thereby affecting the efficiency of multicopy strain construction using CRISPR/Cas9-mediated genome editing is an important consideration to take into account in industrial strain design.

Engineering of HEK293T Cell Factory for Lentiviral Production by High-Throughput Selected Genes.

Lentiviral vectors (LVs) are crucial tools in gene therapy and bioproduction, but high-yield LV production systems are urgently needed. Using clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 high-throughput screening, we identified nine critical genes (LDAH, GBP3, BPIFC, NHLRC1, NHLRC3, ZNF425, TTC37, LRRC4B, and SPINK6) from 17,501 genes that limit LV packaging and formation. Knocking out these genes in HEK293T cells significantly increased virus production, with LDAH knockout exhibiting a 6.63-fold increase. Studies on multigene knockouts demonstrated that the cumulative effects of different gene knockouts can significantly enhance lentivirus production in HEK293T cells. Triple knockout of GBP3, BPIFC, and LDAH increased LV titer by ∼8.33-fold, and knockout (or knockdown) of GBP3, NHLRC1, and NHLRC3 increased LV titer by ∼6.53-fold. This study established HEK293T cell lines with multiple genes knockout for efficient LV production, providing reliable technical support for LV production and application and offering new perspectives for studying LV packaging mechanisms and related virus research.

Safety-risk assessment system for prefabricated building construction in China

PurposePrefabricated building (PB) uses factory production and onsite assembly, which differs from traditional construction methods. This special construction approach may lead to dissimilar safety risks and challenges. Traditional safety assessment methods may not adequately and accurately assess the safety risks of PB construction. This paper aims to develop a new concept and methodology for targeted improvement in assessing PB safety risks.Design/methodology/approachRisk factors and indicators were established based on literature review and expert inputs. A structural equation model (SEM) was developed to investigate the relationships among three main risk categories: objects, workers and management. SEM analyzed the intricate associations between indicators and deepened understanding of safety risks. The model was tailored for China’s PB construction projects to enhance safety-risk management.FindingsThe cloud model evaluation validated the SEM model. A PB case study project tested and verified the model, evaluated its efficacy and quantified its safety performance and grade. We identified significant safety risk impacts across the three risk categories, safety-control level and specific areas that require improvement. The SEM model established a robust safety evaluation indicator system for comprehensive safety assessment of PB construction.Practical implicationsPractical recommendations provide valuable insights for decision-makers to enhance construction efficiency without compromising safety. This study contributed to the conceptual foundation and devised a novel method for evaluating safety performance in PB construction for safer and more efficient practices.Originality/valueThis study departed from the traditional method of calculating weights, opting instead for the SEM method to determine the weights of individual risk indicators. Additionally, we leveraged the cloud model to mitigate the influence of subjective factors in analyzing questionnaire survey responses. The feasibility and reliability of our proposed method were rigorously tested and verified by applying it to the PB case.

Increasing Vitamin K2 Synthesis in Bacillus subtilis by Controlling the Expression of MenD and Stabilizing MenA.

As an indispensable member of the family of lipid vitamins, vitamin K2 (MK-7) plays an important role in blood coagulation, cardiovascular health, and kidney health. Microbial fermentation is favored due to its high utilization rate of raw materials, simple operation, and moderate conditions. However, the biosynthesis pathway of vitamin K2 in microorganisms is highly complex, which hinders its industrial production in microbial cell factories. One of the major challenges is the stable expression and deregulation of key enzymes in the vitamin K2 biosynthesis pathway, which remains unclear and has undergone little investigation. In this study, 2-succinyl-5-enolpyruvyl-6-hydroxy-3-cyclohexene-1-carboxylic-acid synthase (MenD) and 1,4-dihydroxy-2-naphthoate polyprenyltransferase (MenA) were identified as pivotal enzymes in the biosynthesis of vitamin K2. To investigate the catalytic efficiency of MenD in the biosynthesis pathway of vitamin K2, structure-based mutation design and site-directed mutagenesis were performed. Three mutation sites were identified in MenD: A115Y, R96 M, and R323M, which improve the expression level and protein stability. Meanwhile, the MenA mutant T290M, which exhibits improved protein stability, was obtained by modifying its hydrophobic stacking structure. Finally, an engineered strain noted ZQ13 that combinatorially overexpressed MenD (A115Y) and MenA (T290M) mutants was constructed and achieved 338.37 mg/L vitamin K2 production in a 3-L fermenter.

Optimizing light intensity and airflow for improved lettuce growth and reduced tip burn disease in a plant factory

In practical plant factory production, light intensity and airflow were major environmental factors that significantly impacted lettuce growth and tip burn disease. This study investigated the effects of light intensity (100, 200, and 300 μmol·m-2·s-1) and airflow rate (100, 200, and 300 m³/h) on the comprehensive quality of lettuce. The Analytic Hierarchy Process (AHP) was employed to determine the weight of various growth and physiological indices of lettuce, while fuzzy mathematics principles were used to evaluate its comprehensive indices. The findings revealed that higher light intensity increased Ca2+ demand and decreased stomatal conductance, exacerbating tip burn disease. In contrast, increasing the airflow rate reduced leaf temperature, promoted transpiration, and facilitated calcium ion transport, thereby alleviating the occurrence of tip burn. Increasing the airflow rate to 300 m³/h effectively improved plant transpiration and Ca²⁺ transport, which further reduced tip burn. The comprehensive results indicated that under the conditions of a light intensity of 200 μmol·m-2·s-1 and an airflow rate of 300 m³/h, lettuce achieved the best comprehensive quality score of 0.6490, with a Ca2+ content of 21.83 mg/100 g, and tip burn was effectively suppressed. The study provided valuable scientific support for the precise control of environmental conditions in plant factories.

Intelligent Fault Diagnosis in Industrial Machinery: Leveraging AI with LSTM Autoencoder for Enhanced Fault Detection

Machinery Fault Detection (MFD) is an important process in contemporary industrial systems, where it predicts possible physical failures before they lead to a serious problem. This uses multiple technologies to monitor machine statuses (algorithms, data gathering systems and sensors) Using a servo-motor driven actuator for deployment, the Locking Mechanism is pre-assembled into an OEM ATE and will enable predictive failure mode identification (via monitoring and warnings of operational parameters i.e., vibration, temperature or auditory signals in-built to MFD systems) leading to Prophylactic maintenance before critical bottlenecks can occur. The dataset we used in our study was collected from Kaggle and it is called the SpectraQuest Machinery Fault Simulator (MFS) Alignment-Balance-Vibration (ABVT). We used LSTM Autoencoder, KNN, SVM and DNN to analyzed the data. Our LSTM Autoencoder model was very accurate and achieved a precision, recall, accuracy and F-score of 99%. We worked on very large scale datasets. It will help the system detect faults and predict their evolution over time, so you save maintenance costs and increase production in your factory. More research on the practical efficiency of these models in real-time across different industrial settings can create a path towards improved and scalable MFD solutions.

Engineering of Aspergillus niger for efficient production of d-xylitol from l-arabinose

d-Xylitol is a naturally occurring sugar alcohol present in diverse plants that is used as an alternative sweetener based on a sweetness similar to sucrose and several health benefits compared to conventional sugar. However, current industrial methods for d-xylitol production are based on chemical hydrogenation of d-xylose, which is energy-intensive and environmentally harmful. However, efficient conversion of l-arabinose as an additional highly abundant pentose in lignocellulosic materials holds great potential to broaden the range of applicable feedstocks. Both pentoses d-xylose and l-arabinose are converted to d-xylitol as a common metabolic intermediate in the native fungal pentose catabolism.To engineer a strain capable of accumulating d-xylitol from arabinan-rich agricultural residues, pentose catabolism was stopped in the ascomycete filamentous fungus Aspergillus niger at the stage of d-xylitol by knocking out three genes encoding enzymes involved in d-xylitol degradation (ΔxdhA, ΔsdhA, ΔxkiA). Additionally, to facilitate its secretion into the medium, an aquaglyceroporin from Saccharomyces cerevisiae was tested. In S. cerevisiae, Fps1 is known to passively transport glycerol and is regulated to convey osmotic stress tolerance but also exhibits the ability to transport other polyols such as d-xylitol. Thus, a constitutively open version of this transporter was introduced into A. niger, controlled by multiple promoters with varying expression strengths. The strain expressing the transporter under control of the PtvdA promoter in the background of the pentose catabolism-deficient triple knock-out yielded the most favorable outcome, producing up to 45% d-xylitol from l-arabinose in culture supernatants, while displaying minimal side effects during osmotic stress. Due to its additional ability to extract d-xylose and l-arabinose from lignocellulosic material via the production of highly active pectinases and hemicellulases, A. niger emerges as an ideal candidate cell factory for d-xylitol production from lignocellulosic biomasses rich in both pentoses.In summary, we are showing for the first time an efficient biosynthesis of d-xylitol from l-arabinose utilizing a filamentous ascomycete fungus. This broadens the potential resources to include also arabinan-rich agricultural waste streams like sugar beet pulp and could thus help to make alternative sweetener production more environmentally friendly and cost-effective.

Identification of transformer overload and new energy planning for enterprises based on load forecasting.

The new energy system constructed by energy storage and photovoltaic power generation system can effectively solve the problem of transformer overload operation in some enterprises. It can not only reduce the cost of electricity, but also realize low-carbon emission reduction. However, due to its low return on investment, the willingness of enterprises to install new energy is not high. In this paper, we first establish a load forecasting model to users whose transformers are overloaded or about to be overloaded, which are potential customers with new energy installation needs. Then, Optimal configuration models of PV and energy storage systems based on nonlinear programming are developed for these potential customers. The optimal installed capacity of the PV energy storage and the optimal charging and discharging strategy for the energy storage system can be obtained. This optimization strategy ensures that the electricity consumption of the enterprise does not exceed the rated capacity, and effectively reduces the enterprise's basic tariff and electricity price to achieve cost reduction and efficiency. Finally, taking a building materials production factory as an example, we obtain the optimal plan for the new energy capacity, as well as the economic benefits of the plan and the specific strategy of energy storage charging and discharging for this factory.

Metabolic Engineering of Saccharomyces cerevisiae for Fermentative Production of Heme.

Heme is a key ingredient required to mimic the color and flavor of meat in plant-based alternatives. This study aimed to develop a yeast-based microbial cell factory for efficient and sustainable production of heme. To this end, first, Hem12p (uroporphyrinogen decarboxylase) was identified as the rate-limiting enzyme in the heme biosynthetic pathway present in Saccharomyces cerevisiae D452-2. Next, we investigated the effects of disruption of the genes involved in the competition for heme biosynthesis precursors, transcriptional repression, and heme degradation (HMX1) on heme production efficiency. Of the knock-out strains constructed in this study, only the HMX1-deficient strain produced heme at a higher concentration than the background strain without gene disruption. In addition, overexpression of PUG1 encoding a plasma membrane transporter involved in protoporphyrin IX (the precursor to heme biosynthesis) uptake led to a significant increase in intracellular heme concentration. As a result, among the various engineered strains constructed in this study, the ΔHMX1/H3&12+PUG1 strain, the HMX1-deficient strain overexpressing HEM3, HEM12, and PUG1, produced the highest concentration of heme (4.6mg/L) in batch fermentation, which was 3.9-fold higher than that produced by the wild-type D452-2 strain. In a glucose-limited fed-batch fermentation, the ΔHMX1/H3&12+PUG1 strain produced 28mg/L heme in 66h.

Improving trans‐cinnamic acid production in a model cyanobacterium

Abstracttrans‐Cinnamic acid (tCA) is a precursor in the synthesis of many high‐value compounds with bio‐active qualities useful in applications like medicine, polymers, and cosmetics. Currently tCA is produced by industrial chemical synthesis from fossil fuels or cost‐prohibitive isolation from terrestrial plants. Cyanobacteria, a type of photosynthetic bacteria, can be readily engineered to convert sunlight and carbon dioxide into metabolites of interest at relatively high amounts compared to terrestrial plants. The purpose of this study is to advance the industrial and commercial value of cyanobacteria as a biological factory for renewable production of tCA. Production of tCA has previously been demonstrated in the model cyanobacterium Synechocystis sp. PCC 6803 (S. 6803) via expression of non‐native phenylalanine ammonia lyase (PAL) from various organisms. This project focuses on developing and characterizing a new high‐titer strain of S. 6803 expressing a plant PAL gene controlled by an inducible promoter. We assessed production in shake flasks under constant light, a 12 h:12 h light:dark cycle, and environmental photobioreactors (ePBRs) with a sinusoidal, rapidly fluctuating light environment. Our strain demonstrates a four‐fold increase in tCA production to ~500 mg L−1 by 14 days compared to previously reported titers in S. 6803 under shake flask cultivation and a 30–50% improved average tCA production per culture density (60 mg·L−1·OD730−1) in ePBRs over comparable previously reported culture methods. Our study progresses S. 6803 tCA bioproduction into higher culture volumes, up to 500 mL, while further validating the strength of an inducible system for tCA production in S. 6803.

Impact of 6S (5S+Safety) Implementation in Machine Workshops on Occupational Safety

In this current study, the impact of implementing 6S (5S+Safety) in the machining industry on occupational safety was investigated by selecting the chip machining section of a factory producing flywheels. To facilitate the comparison of risk levels before and after the implementation of 6S, a Fine-Kinney risk evaluation was conducted in the sawdust department before the application of 6S. The 6S implementation process took 28 days, and subsequently, a Fine-Kinney risk evaluation was performed in the sawdust department, revealing a 77.4% reduction in risks. As a result, the 77.4% reduction in accident risks indicates that the implementation of 6S, which emphasizes safety within the framework of the 5S principles, leads to a more organized, cleaner, and safer machine workshop environment. We can indicate that this approach not only reduces the risk of accidents but also enhances work efficiency, preventing time loss and material waste.

Glycerol as a Feedstock for Chemical Synthesis

AbstractGlycerol, defined simply as a colorless, sweet syrupy liquid extracted from fatty substances through saponification, is an alcohol with three hydroxyl (OH–) groups in its structure. Glycerol has many uses in the consumer market. It is used primarily in personal care products, as an adhesive and sealing agent and many applications. Glycerol, whose name is propane‐1,2,3‐triol, standardized by the International Union of Pure and Applied Chemistry (IUPAC), CHO open formula CH2OH–CHOH–CH2OH. It can be said that glycerol, a by‐product of biodiesel, is produced in very high quantities. Retention of the produced glycerol will lead to cost increases and environmental problems that may directly affect the development of the biodiesel market. Due to the supply of glycerol to the market in large quantities, glycerol prices have hit the bottom, and therefore, the income and profitability of biodiesel production factories from the sale of glycerol have decreased. This situation clearly shows that the excess of glycerol now poses an obstacle to developing the biodiesel market. This article aims to list the valuable chemicals into which glycerol, produced in large quantities as a biodiesel by‐product, can be converted under a single heading and to detail the studies carried out on this subject.