Industrial Production Processes Research Articles

Enhancing Bioactive Cordycepin Production via Precision Fermentation with an Engineered Aspergillus oryzae

An optimal culture medium and highly stable biometabolites are important in industrial production processes. The response surface methodology with a Box–Behnken design was performed to determine the optimal culture medium of an engineered Aspergillus oryzae strain for cordycepin production by submerged fermentation. The influences of glucose, yeast extract, and adenine concentrations on cordycepin production were explored, and their concentrations were used for experimental design. The results reveal that the optimal culture components involved 30.0 g/L of glucose, 9.8 g/L of yeast extract, and 1.5 g/L of adenine. As predicted, the maximum cordycepin concentration (1724.53 ± 18.30 mg/L) was obtained with a short fermentation time (2 days). A significant increase in cordycepin yield (>50% increase) was observed in the culture grown in the optimized culture medium compared to that grown in the basal medium. A xanthine oxidase inhibitory activity assay demonstrated that the cordycepin product had a pharmacological function. It exhibited strong stability under high thermal and acidic conditions, with over 95% product recovery. The findings of this study are valuable for developing cost-effective processes for producing health-benefiting products.

N2 influences on CH4 accumulation and displacement in shale by molecular dynamics

N2 is generally employed as a displacement agent to enhance gas recovery in shale gas-bearing reservoirs. However, the primary displacement mechanism in the subsurface still needs to be clarified due to the characteristics of shale reservoirs with low porosity and abundant nanopores. This study employs the Molecular Dynamics (MD) simulation method to investigate the effects of N2 on the CH4 accumulation and displacement processes by adopting practical conditions in the subsurface environment. In equilibrium MD simulation processes, including the N2 from outside and inner kerogen matrix, keeping the gas ratio of 1:3 for CH4 and N2, the displacement is 52.4% and 65.3%, respectively, which suggests that CH4 cannot be entirely displaced by surrounding N2 particles, owing to the strong interaction between CH4 and the kerogen matrix. For the straightforward displacement process by N2, the displacement efficiency is enhanced by 71.7% at the 1:1 gas ratio. Another case of N2, in which generation is accompanied by displacement processes, at the ratio 1:2 for N2:CH4, shows a 47.1% displacement efficiency. This work evidences that the straightforward displacement process is more efficient on CH4 displacement, which enhances CH4 production at a pronounced scale and sheds light on the N2 displacement process in industrial shale gas reservoir production.

Thermodynamic Analysis of the Steam Reforming of Acetone by Gibbs Free Energy (GFE) Minimization

Steam reforming is an important industrial process for hydrogen production. Acetone, the by-product of phenol production from cumene peroxidation, is a useful source of hydrogen due to its availability and low value compared to hydrogen fuel. This study aimed to utilize the Gibbs free energy minimization method using the Soave–Redlich–Kwong (SRK) equation of state (EOS) to conduct a thermodynamic analysis of the steam reforming process for pure component acetone. The steam reforming process is temperature dependent, with increasing temperatures leading to higher hydrogen production. Competing reactions, particularly the exothermic reverse water–gas shift, impact hydrogen yields beyond 650 °C. The study identified 600 °C as the optimum temperature to strike a balance between maximizing hydrogen production and minimizing the reverse water–gas shift’s impact. The optimal hydrogen yield (70 mol%) was achieved at a steam-to-oil ratio (STOR) of 12. High STOR values shift the equilibrium of the water–gas shift reaction towards hydrogen production due to increased steam, effectively consuming acetone and favoring the desired product. Atmospheric pressure is optimum for hydrogen production because the equilibrium of gas phase reactions shifts in favor of the lighter components at lower pressures.

Efficient and Scalable Direct Regeneration of Spent Layered Cathode Materials via Advanced Oxidation.

Among direct recycling methods for spent lithium-ion batteries, solid-state regeneration is the route with minimal bottlenecks for industrial application and is highly compatible with the current industrial cathode materials production processes. However, surface structure degradation and interfacial impurities of spent cathodes significantly hinder Li+ replenishment during restoration. Herein, we propose a unique advanced oxidation strategy that leverages the inherent catalytic activity of spent layered cathode materials to address these challenges. This strategy decomposes H2O2 to generate •OH and •O2 - free radicals, facilitating oxidation reactions with the surface of the spent cathode. As a result, this approach effectively elevates the Ni valence state, modifies the surface microstructure, and eliminates fluorine-containing interface impurities, thereby promoting the solid-state regeneration process. The regenerated LiNi0.83Co0.12Mn0.05O2 cathodes demonstrate a specific capacity of 206mAhg-1 at 0.1 C, comparable to commercially available cathodes. Meanwhile, this advanced oxidation strategy proves adaptable and scalable for treating industrial dismantled LiNi0.5Co0.2Mn0.3O2 black mass. A 3.1Ah pouch cell assembled with the regenerated LiNi0.5Co0.2Mn0.3O2 exhibits impressive capacity retention of 74% after 500 cycles. Additionally, a techno-economic analysis reveals that this strategy possesses low energy consumption, minimal environmental footprint, and high economic viability, suggesting its suitability for the battery recycling industry.

Automated inspection of the tip condition of a welding tungsten electrode

This article introduces an innovative inspection system designed to check the condition of the welding electrode tip. The system combines 2D laser scanning with advanced analytical algorithms, enabling fast, accurate, and automated inspection. Developed PC software controls the synchronized movement of welding electrodes and the scanner, allowing the creation of 3D point clouds representing the surface of the electrodes. These data are analysed to identify deformations, contamination, and surface imperfections. The inspection results are communicated with the welding control system, contributing to improved weld quality and efficiency in industrial production processes. This innovation has the potential to change how the condition of welding electrodes is monitored in industrial sectors.

Modeling of the qualitative state of oilseeds from soybean seeds by multifactorial analysis of factor areas

The production of oil products in Ukraine is a leader not only in the European but also in the world market of food and processing industry products, therefore, the priority areas of scientific research in this field are the effective assessment of the quality of the oil obtained. Existing methods for assessing this product by organoleptic characteristics are subjective and do not allow the use of parameters that require more complex measurement technologies; which in general makes it impossible to conduct a comprehensive analysis of the quality of the studied product. The purpose of this study was to develop and implement a mathematical modeling method for numerical analysis of the quality of soybean oil product varieties. Qualitative assessment of the studied oil varieties was carried out using such indicators as the mass fraction of fat, peroxide, and acid number; content of free fatty acids, phosphatides, moisture, and impurities; it allowed to perform a numerical analysis using dimensionless complexes regarding the condition of the studied oil product varieties and compliance with regulatory indicators. When processing experimental data and calculating the developed quality criteria, programming methods, Excel, and COMPASS mathematical environments were used, which allowed the construction of geometric quality models and the necessary assessment criteria. According to the results of the research, it turned out that the characteristics of the studied varieties "OAS Avatar", "ES Mentor", "Sigalia", "ES Mentor" and "Gallek" do not meet the requirements for compliance with the quality thresholds by 160 - 220%, however, their current characteristics, which were selected for assessment, are within the average normative indicators. The proposed mathematical models can be implemented for practical application in assessing the condition of any complex object of research using an unlimited number of characteristics. At the same time, the main requirements for these parameters are their breadth of coverage of the physical, mechanical, and chemical-biological properties of the product and the ability to provide an objective and thorough assessment of its condition.

Optimization of tensile strength of AlSi10Mg material in the powder bed fusion process using the Taguchi method

This study examined scan speed, hatch distance, and scan rotation angle parameters to determine the effects of the powder bed fusion process on the tensile strength of AlSi10Mg material. The Taguchi L9 experimental design was applied to evaluate the effects of the parameters systematically. The experimental results revealed the importance of the parameters affecting the tensile strength of the AlSi10Mg material. While the scan speed was determined to be the parameter that most affected the tensile strength with a contribution rate of 81.4%, the scan rotation angle and hatch distance were effective at 7.8% and 6.1%, respectively. In the optimization performed using signal/noise analysis, 1200 mm/s scan speed, 80 μm hatch distance, and 17° scan rotation angle were found to be the most suitable parameters to ensure the highest tensile strength. The tensile strength obtained in the verification test was measured at 598 MPa. The findings increase the application potential of SLM technology with AlSi10Mg material and provide valuable information for process optimization. The results can significantly contribute to strategies to increase tensile strength and improve material performance in industrial production processes.

Research Progress on Oil-Water Separation Materials Based on Polyurethane Modification.

Numerous oil-water mixtures produced through industrial production processes and daily activities pollute the ecological environment and pose risks to human health. The development of materials with high oil-water mixture separation efficiency can promote the recycling of oil and water resources and effectively prevent environmental pollution caused by their direct discharge. Most of the current oil-water separation materials consist of foam, aerogel, and other porous materials. Among these materials, polyurethane exhibits good biodegradability, mechanical properties, large pore volume, low cost, wear resistance, and water resistance in oil-water mixture separation applications. However, pure polyurethane foam is characterized by low adsorption separation efficiency, insufficient recyclability, and high flammability. Therefore, modifying polyurethane to improve the oil-water mixture separation efficiency is vital. In this review, the methods and mechanisms of polyurethane modified materials used for oil-water mixture separation are reviewed, and their future research and application directions are prospected.

Применение технологий BigData для оптимизации производственных процессов в горнодобывающей промышленности России: анализ внедрения и эффективности

The article presents a comprehensive analysis of using the BigData technologies to optimize production processes in the Russian mining industry during the period from 2015 to 2024. The main trends and areas of the BigData application, technical and organizational solutions used, barriers and economic effects of their implementation are investigated using an extensive empirical base, including company data, industry surveys and expert assessments. An interdisciplinary approach was applied that combines the methods of statistical analysis, case studies, expert interviews and economic and mathematical modeling. The key success factors of the BigData projects in various segments of the mining industry were identified, and the drivers and barriers of digital transformation of the industry were determined. It is shown that implementation of the BigData leads to a significant increase in productivity, cost reduction and improved safety of mining operations. Recommendations on spreading the best practices were developed and promising directions of the BigData development were justified in conditions of import substitution and transition to Industry 4.0. The results of the study are of high value for the academic community, public policy in the field of digitalization and management practices of mining companies.

Layer porosity in powder-bed fusion prediction using regression machine learning models and time-series features

Additive manufacturing (AM) using laser powder-bed fusion (L-PBF) has become a common industrial process for high-end component production. The uptake of the process has been accelerated through the broad acceptance of the L-PBF process toward achieving high-quality parts with complex geometry. However, the L-PBF process faces challenges from the process’s sensitivity to the process build parameters, which, when incorrectly set, can cause defects such as porosity, which in turn have a detrimental effect on the produced part properties. On the other hand, the AM processing equipment generates a vast amount of data captured through in situ sensors such as pyrometers and imaging cameras. Having such an abundance of process data facilitates the employment of advanced machine learning (ML) tools to understand and extract patterns and information about the underlying AM process and gain “predictive control.” Driven by this idea, we aimed to employ ML tools over pyrometer time-series data from an L-PBF process to predict the porosity percentage of layers of an AM-built part. Sensor data are naturally modeled by time series; however, most ML algorithms work with tabular data (i.e., one single vector describes a feature). In the work presented here, feature engineering tools were used to transform the time-series data into informative features. These features were fed into the tabular ML algorithms for evaluation, broadening the selection of ML algorithms available in the literature. It was hypothesized that the time-series summary features would capture the interaction of melt-pool temperature with resulting porosity, from which the resulting models could better predict porosity occurrence. The dataset contains layer porosity values in the range of 0.00175 – 7.160%, to which we divide the data into “low” and “high” porous layers using a splitting threshold value of 1%. From evaluating these algorithms, it was concluded that classifying “low” versus “high” porosity layers is relatively easier than predicting the layer’s porosity percentage.

Multi-flow integration process safety management at steelmaking production site through wireless sensor network and AI-enabled data prediction

Industrial production process includes complex and high-risk operational procedures. Maintaining process safety and stability is essential to prevent casualties, equipment damage, and asset loss. Traditional process safety management (PSM) on production site heavily relies on manual inspections and video surveillance which inevitably overlook some hidden risks. Therefore, this paper proposes a novel multi-flow integration PSM framework to overcome the trouble. Firstly, risk factors of the material flow (MF) and energy flow (EF) are systematically analyzed and organized based on the 4M1E method. The wireless sensor network (WSN) is established by deploying multiple sensors which facilitate the data flow (DF), including data perception, collection, transmission and aggregation of different risk variables. Additionally, the analysis model to extract the information flow (IF) is obtained by comparing prediction performance of different deep learning (DL) models. Finally, response strategies against potential dangers are formulated and the control flow (CF) across production layers are achieved relying on the circulation of safety directives, the execution of control measures and the feedback. The effectiveness of this framework is verified in a steel continuous casting scenario through the acquisition of DF, the extraction of IF and the loop of CF. Results indicates that the Bi-LSTM model can achieve the most outstanding prediction performance with relative root mean square error of 4.2428%, root mean square error of 0.0551 and R-square of 0.9816. This study can aid in advancing the digitization and intelligence of the PSM and providing a practical research perspective and application mode.

Fluorescent determination of nitroaniline isomers based on ultrasensitive hydrophilic TPE sensors

Abstract Nitroaniline compounds are widely used as explosives and also in the industrial production process. However, as one group of rich-nitro materials, it is hard to discriminate nitroaniline isomers from other energetic materials. Hereby, TPE derivatives with mono-, di-, tetra-substituted hydrophobic alkyl chain and hydrophilic quaternary ammonium salt chain were synthesized and characterized. Their fluorescence properties and AIE characteristics were comprehensively studied through fluorescence spectra, fluorescence lifetime, and fluorescence yield. The hydrophilicity and number of substituents both have a great impact on their AIE effect. The obtained hydrophobic TPE2Br and di-substituted hydrophilic TPE2N were investigated as explosive sensors. TPE2Br has fluorescent turn-on response to the tested explosives. TPE2N has unique fluorescent turn-off response to the nitroaniline isomers. By adding nitroaniline isomers were added to the TPE2N solution, the fluorescence intensity quenched to varying degrees. While to other explosives, it demonstrates fluorescent turn-on effect. The quenching constant Ksv of TPE2N to 4-NA is 1.24 *107 and its limit of detection (LOD) is 0.47 μM. TPE2N could work as an ultrasensitive, low-cost and rapidly responsive sensor for the recognition of nitroanilines.

Thermodynamics in the system "refractory clay–glass cullet–quartz sand" when predicting the phase composition of engobe coatings on ceramic bricks

A thermodynamic analysis of the reactions involved in the formation of the phase composition of engobe coatings in the system "refractory clay–glass cullet–quartz sand" on ceramic bricks during firing has been performed. The study highlights the features of glass crystallization during prolonged exposure within the temperature range of 690–7700C, characteristic of the slow firing process in industrial ceramic brick production. Devitrite and -cristobalite are identified as the primary crystalline phases formed in the cullet, playing an active role in solid-phase reactions that shape the structure of the engobe coatings. The phase composition of the coatings depends on the maximum firing temperature and is influenced by the thermal transformation products of the raw materials (metakaolin, mullite, devitrite, -wollastonite) as well as the crystalline phases generated through chemical interactions between the engobe components. Among the significant phases formed, anorthite and -cristobalite are particularly notable for contributing to the strength and hardness of the brick's covering layer. The findings of this work provide a foundation for designing engobe compositions and selecting technological parameters to achieve coatings with superior performance characteristics.

Crystallization of CoWB ternary boride in nickel-based metallic glass

The crystallization kinetics of CoWB ternary boride were investigated through non-isothermal differential scanning calorimetry experiments, using a Ni-based metallic glass precursor as the starting material. The effects of isothermal heat treatment temperature on the phase evolutions were also systematically studied. The results showed that the melt crystallization and glass crystallization behaviors of the alloy differ from each other. The continuous heating transformation diagram plotted from non-isothermal analyses is consistent with the results of isothermal heat treatments. A broad ternary boride precipitation zone was demonstrated, involving the gradual precipitation of CoWB crystals. Avrami exponent values for the early crystallization stage showed that the crystallization mechanism of CoWB is governed by the interface-controlled three-dimensional growth mechanism, and nucleation rate is high. As the crystallization progresses, a diffusion-controlled growth occurs and the nucleation rate decreases. This study offers insights into the heat treatment of CoWB-reinforced nickel matrix nanocomposites, benefiting future research and industrial production processes.

Exploring the Training of Film and Video Talents in Higher Vocational Colleges under the Background of AIGC

With the rapid development of Artificial Intelligence Generated Content (AIGC) technology, the film and video industry's production process and creation mode are profoundly transforming. This technological advancement has put forward new requirements for film and video talent cultivation in higher vocational colleges, prompting the education system to make targeted optimisation in key areas such as curriculum, practical teaching, teacher construction and school-enterprise cooperation. Based on the talent demand of the film and video industry in the context of AIGC, this study analyses the main challenges facing film and Video education in higher vocational colleges, including the disconnection between the curriculum content and the development of the industry, the insufficiency of practical teaching resources, and the weakness of the faculty. Because of these problems, the study proposes the strategies of building an interdisciplinary curriculum system, strengthening practical teaching, deepening university-enterprise cooperation and improving the ‘dual-teacher’ teaching team to provide effective references for the cultivation of high-quality film and video talents with innovative ability and practical skills in higher vocational colleges.

Experimental study on trace moisture control of lithium iron phosphate

LiFePO4 is considered as one of the mainstream positive electrodes materials for power batteries. Its moisture content is closely related to the battery cycle performance and multiplier performance. When the moisture content of LiFePO4 exceeds 600 ppm, the electrochemical performance is seriously degraded. However, it is fairly difficult to reduce its moisture content to below 1000 ppm with the conventional processes. Focusing on the requirements for the trace moisture content of LiFePO4, this study investigated the airflow grinding process inside the closed nitrogen drying system at the first time. The performance in reducing the moisture content in LiFePO4 was also compared. The results demonstrated the moisture content of LiFePO4 powder was lower than 600 ppm after crushing to the particle size of d50 = 1∼1.2 μm and d100 = 6.7–7.1 μm with closed nitrogen drying system under the conditions of the classifier rotation speed 1072 r∙min−1, the steam temperature 120 °C and the pressure 0.5 MPa, and the output capacity reached 210–230 kg h−1. The process gives full play to grinding and drying advantages and break through the drying limit of the traditional processes. It could be thought as a new process for LiFePO4 industrial production, achieving trace moisture drying and relatively high output simultaneously.

Development of QD-OLED and Thin Film Encapsulation Technology Using Inkjet Printing

In the display industry, inkjet printing has the advantages of high material use efficiency, digital control, and non-contact printing. Additionally, it is advantageous for large-area processing and flexible/stretchable device applications. Due to these attractive advantages, technology development to apply the printing process to the display mass production process has continued for a long time. The quantum dot organic light-emitting diode (QD-OLED) panel recently developed by Samsung Display showed that inkjet printing technology can stably implement not only the encapsulation process but also light-emitting pixels.In this study, we first describe the encapsulation technology for OLED devices developed using inkjet printing used in industrial mass production processes. Specifically, TFE (Thin Film Encapsulation) with a 1.5-dyad structure in which organic and inorganic layers exist alternately was implemented and the structure was analyzed. We then introduce the results of applying this TFE to flexible OLED devices and panels.Next, we describe QD-OLED manufacturing technology using inkjet printing. Specifically, QD-OLED integration technology is explained focusing on the top-emitting OLED process, QD pixel definition layer process, and QD inkjet printing process. We introduce the electrical and optical analysis results of the QD-OLED produced in this way.

CTF-Net: Partial Focus Searching within Holistic Structure for Fine-Grained Object Recognition

Most fine-grained visual recognition methods endeavor to directly locate discriminative regions in intricate environments, but tend to overlook the object’s holistic structure, which may lead to misclassification due to overemphasizing incorrect areas. In this paper, we propose a coarse-to-fine paradigm, which prioritizes locating holistic structural regions of the target object, followed by a gradual search to locate discriminative areas. Specifically, we first design the “look into object” module to locate the areas encompassing the target’s holistic structure using prior information. Subsequently, without introducing additional parameters, we design a partial focus searching module to enhance feature representations of discriminative regions within the target’s structural composition. Ultimately, we segregate the foreground components from the original image, attaining a more precise characterization of the target. Furthermore, we demonstrate the practical application potential of our model in real-world industries through our self-constructed DHU-Fine-grained-6000 dataset. Comparative experiments on three public datasets indicate that the superiority of our approach over many recent methods and holds promising application potential in industrial production processes.

Dynamic linkages between element input structure, global value chain, and carbon emissions: evidence from a heterogeneous panel cointegration model

ABSTRACT Industrial CO2 constitutes a dominant component of global CO2 emissions. Therefore, modifications in industrial production processes are crucial. We analyse global cross-national panel datasets from 1995 to 2018 using the panel autoregressive distributed lag (ARDL) model to provide comprehensive evidence of the combined effects of input-side element structures and production-side global value chain (GVC) divisions on output-side CO2 emissions. The results show that: (i) Renewable energy and service elements in production factors can reduce CO2 emissions, though their abatement effects vary. Both can produce sustained carbon mitigation effects in the long term, but only renewable energy is effective in the short term. (ii) The GVC division pattern can induce a CO2 emission increase in both the short and long term, though these effects are heterogeneous depending on the differences in forward-backward GVC divisions. (iii) A path dependence analysis shows that input-side element structures can change production-side GVC divisions, thereby affecting output-side emissions. This study extends our understanding of the environmental effects of various production links and helps coordinate high-quality economic development and impending environmental constraints.

Polydopamine-incorporated MOF membrane with hydrophilicity for effective oil/water separation and fouling-resistant model analysis and prediction

A large number of oil spills and oily wastewater are discharged, in the modern industrial production process, resulting in serious water pollution. Oily wastewater can be extremely harmful to ecosystems and human health. Oil-water separation has become a major challenge at present, and membrane separation has aroused more and more concern in recent years due to its high economic efficiency. This paper fabricated PES filtration membranes using the novel nanoparticlesbased on both metal–organic frameworks-5 (MOF-5) and polydopamine (PDA) layers as dopants, and then adequately explored the porosity, morphology, separation, hydrophilicity, and fouling-resistant performance of the resultant membranes. In addition, the prepared membranes were used for oil–water separation, including soybean-in-water, petroleum ether-in-water, and gasoline-in-water emulsions. MOF@PDA membranes exhibit high separation efficiency of up to 99.8%. Subsequently, membrane fouling mechanisms were investigated using the Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory, and the molecular mode of interaction with the three oil–water emulsions with the membrane was compared. Newly prepared MOF@PDA membranes have better stability, fouling-resistant, and self-cleaning properties. Finally, an optimal machine learning model for oil–water separation efficiency was developed with a high prediction accuracy of 98%. The obtained results indicate that mixed matrix membranes exhibit excellent oil–water separation performance, demonstrating great application prospects in the remediation of oily wastewater.