Industrial Production Processes Research Articles

Leakage identification and correlation coefficient method for industrial workshop production process combining with computational fluid dynamics

Identifying leakage sources in industrial factory production is crucial to improving air quality, ensuring people’s health and safety and preventing safety accidents. In this study, a method for leakage source identification in industrial factories combining with computational fluid dynamics (CFD) and correlation coefficient was proposed and validated. The study first experimentally validated the numerical methods, which were fundamental to the leakage identification method. Then impacts of leakage sources, sensor errors and number of sensors on the source identification results were evaluated. The results showed that the identification accuracy could be significantly improved by refining the step size of the coefficient a r in this method. When the number of leakage sources was unknown, the accuracy of this method in identifying the number and location of leakages was 93.5%. The computation time spent on source identification depended on the maximum number of leakage sources. Using four sensors with errors were enough to identify the number of unknown leakage sources. The number of leakage sources did not exceed three at the same time. Overall, coupled CFD and correlation coefficients method could effectively identify the number, location and intensity of leakages.

Spatial distribution, source apportionment, and risk assessment of perfluoroalkyl substances in urban soils of a typical densely urbanized and industrialized city, Northeast China

As an important capital city of intensive urbanization and industrialization in Northeast China, Changchun has experienced extremely rapid development, with diverse sectors such as automobile manufacturing, equipment manufacturing, optoelectronics, and pharmaceutical decoration. However, data on the levels and profiles of perfluoroalkyl substances (PFASs) in urban soils of Changchun is limited. This study investigated 17 PFASs across various functional zones within the main urban area of Changchun. ∑PFAS concentrations in the soils ranged from 0.236 to 6.483 ng/g, averaging 1.820 ng/g. Perfluorocarboxylic acids (PFCAs) were more prevalent than perfluorosulfonic acids (PFSAs), and short-chain PFASs (C ≤ 6) were the predominant residues. PFAS concentrations varied across functional zones, with commercial markets exhibiting the highest levels, followed by industrial areas, residential areas, suburban zones, and transportation areas. Molecular diagnostic ratio and PCA-MLR analysis identified industrial production processes of consumer goods and wastewater treatment plants as the primary sources of soil PFAS contamination. There were no obvious health risks of soil ∑PFASs, while soil PFOS and PFHxS may have an impact on the richness and diversity of soil microbial communities in some certain locations. This study provides new data on PFAS residues in soils influenced by diverse contamination sources within a key industrial city in Northeast China, offering valuable insights for prioritizing remediation and restoration efforts.

Multi-Dimensional Resource Allocation for Covert Communications in Multi-Beam Low-Earth-Orbit Satellite Systems

Satellite communication systems, especially multi-beam low-Earth-orbit (LEO) satellites, could cater to the needs of different industrial applications through flexible resource allocation. Unfortunately, due to the wide coverage of LEO satellites, the data exchange within the LEO satellite networks suffers from the risk of eavesdropping and malicious jamming, which could severely degrade the performance of the industrial production process. To address such challenges, this paper introduces a multi-dimensional resource allocation strategy to facilitate covert communication within the multi-beam LEO satellite network. Our approach ensures the rate requirements of different user equipments while preventing the detection of communication signals by an eavesdropping geostationary orbit (GEO) satellite. Specifically, we formulate an optimization problem that jointly optimizes satellite beam-hopping scheduling, frequency band allocation, and the transmit power of different user equipments, under the covertness constraint. By introducing auxiliary binary variables, we transform this optimization problem into a Mixed-Integer Linear Programming (MILP) problem, which allows us to utilize machine learning-based techniques for efficient solution finding. The simulation results demonstrate the effectiveness of our proposed scheme.

Yam protein ameliorates cyclophosphamide-induced intestinal immunosuppression by regulating gut microbiota and its metabolites

Yam is a dual-purpose crop used in both medicine and food that is commonly used as a dietary supplement in food processing. Since yam proteins are often lost during the production of yam starch, elucidating the functionally active value of yam proteins is an important guideline for fully utilizing yam in industrial production processes. This study aimed to explore the potential protective effect of yam protein (YP) on cyclophosphamide (CTX)-induced immunosuppression in mice. The results showed that YP can reduce immune damage caused by CTX by reversing immunoglobulins (IgA, IgG and IgM), cytokines (TNF-α, IL-6, etc.) in the intestines of mice. Moreover, YPs were found to prevent CTX-induced microbiota dysbiosis by enhancing the levels of beneficial bacteria within the microbiome, such as Lactobacillus, and lowering those of Desulfovibrio_R and Helicobacter_A. Metabolomics analyses showed that YP significantly altered differential metabolites (tryptophan, etc.) and metabolic pathways (ABC transporter protein, etc.) associated with immune responses in the gut. Furthermore, important connections were noted between particular microbiomes and metabolites, shedding light on the immunoprotective effects of YPs by regulating gut flora and metabolism. These findings deepen our understanding of the functional properties of YPs and lay a solid foundation for the utilization of yam.

Optimizing the System Life Cycle in Manufacturing Industry Through Implementation of IoT

This paper aims to explore how the implementation of the Internet of Things (IoT) can optimize the system life cycle in the manufacturing industry, enhancing efficiency, sustainability, and profitability. A qualitative research method with a case study approach was employed. Data collection techniques included observation, documentation, and in-depth interviews with management, supervisors, engineers, and operators. The data were analyzed using an interactive model, consisting of data reduction, data presentation, and conclusion drawing/verification. It was found that the implementation of IoT in the manufacturing industry's production process significantly improves production planning and control, asset and equipment maintenance, production cost reduction, product quality, and work environment safety. IoT-enabled real-time monitoring and data analytics facilitate better decision-making, predictive maintenance, and optimized resource utilization. It was found that. the implementation of IoT in the manufacturing industry's production process significantly improves production planning and control, asset and equipment maintenance, production cost reduction, product quality, and work environment safety. IoT-enabled real-time monitoring and data analytics facilitate better decision-making, predictive maintenance, and optimized resource utilization

Separation of valuable metals from low nickel matte to prepare NiCo2O4 materials: Optimization of oxidative leaching process and improvement of electrochemical properties of materials

The economical and efficient utilization of intermediate low nickel matte has become an important goal of nickel smelting industry. In this work, an innovative process of oxidative leaching of valuable metal from low nickel matte to prepare NiCo2O4 materials was proposed based on the materials utilization of mineral. The addition method and speed of ammonium persulfate in the oxidative leaching process was investigated to improve the extraction of valuable metals. The key factors affecting the leaching of valuable metals and the optimum leaching condition were determined by orthogonal experiments, and the extraction of Ni, Cu, and Co was 92.3 %, 97.1 % and 96.6 %, respectively. The leaching mechanism was clarified and the advantages of the leaching process were compared and analyzed. The kinetics of oxidative leaching process was studied by timing sampling method, and the control model, activation energy and kinetic equation of Ni, Cu and Co leaching were determined by analyzing the data with unreactive shrinkage model. The effects of Ni:Co ratio and calcination temperature of precursor on the properties of NiCo2O4 were investigated using the leaching solution removing iron and copper as raw materials. The NiCo2O4 material with the Ni:Co ratio of 1:2 prepared by calcining precursor at 750 ℃ for 3 h has a specific capacitance of 864F/g when the current density is 1 A/g. The prepared NiCo2O4 materials show good capacitance performance and has potential to be used as capacitor electrodes. This work combines metal extraction with product materialization, which is expected to help the metallurgical industry effectively shorten the industrial production process and reduce production costs.

Geranylgeraniol: Bio-based platform for teprenone, menaquinone-4, and α-tocotrienol synthesis

By utilizing the conformational selectivity of biosynthesis and the flexibility of chemical synthesis, researchers have formulated metabolic engineering-based semi-synthetic approaches that initiate with the final product’s structure and identify key biosynthesis intermediates. Nonetheless, these tailored semi-synthetic routes focused on end-products, neglecting the possibility of biobased intermediates as a platform for derivatization. To address this challenge, this studyproposed a novel strategy resembling chemosynthesis-style divergent exploration to amplify the significance of biobased intermediates, in the case of geranylgeraniol (GGOH). Using the novel bifunctional terpene synthase PTTC066 and systematic metabolic engineering modifications, the engineered yeast straindemonstrated high GGOH production levels (3.32 g/L, 0.039 g/L/h). This platformenabled the semi-synthesis of various pharmaceuticals, including the anti-ulcer drug teprenone, the osteoporosis treatment drug menaquinone-4, and introduced a novel route for synthesizingα-tocotrienol. This study offers a fresh outlook on semi-synthetic approaches, opening avenues for improvements, substitutions, and innovations in industrial production processes.

In-situ remediation of cadmium contamination in paddy fields: from rhizosphere soil to rice kernel.

Cadmium (Cd) has become an important heavy metal pollutant because of its strong migration and high toxicity. The industrial production process aggravated the Cd pollution in rice fields. Human exposure to Cd through rice can cause kidney damage, emphysema, and various cardiovascular and metabolic diseases, posing a grave threat to health. As modern technology develops, the Cd accumulation model in rice and in-situ remediation of Cd pollution in cornfields have been extensively studied and applied, so it is necessary to sort out and summarize them systematically. Therefore, this paper reviewed the primary in-situ methods for addressing heavy metal contamination in rice paddies, including chemical remediation (inorganic-organic fertilizer remediation, nanomaterials, and composite remediation), biological remediation (phytoremediation and microbial remediation), and crop management remediation technologies.The factors that affect Cd transformation in soil and Cd migration in crops, the advantages and disadvantages of remediation techniques, remediation mechanisms, and the long-term stability of remediation were discussed. The shortcomings and future research directions of in situ remediation strategies for heavily polluted paddy fields and genetic improvement strategies for low-cadmium rice varieties were critically proposed. To sum up, this review aims to enhance understanding and serve as a reference for the appropriate selection and advancement of remediation technologies for rice fields contaminated with heavy metals.

A coordination polymer based on ionic-type ligand for efficient separation of C2H2/C2H4 and C3H4/C3H6 mixtures

C2–C3 alkyne/alkene adsorption and separation is a critical process in industrial production, but it remains challenging due to the similar size of gas molecules. Herein, based on an ionic-type ligand, a porous coordination polymer with inherent positive charged sites has been successfully constructed by 1D chains through non-conventional CH···O hydrogen bonds. The flexible framework shows adsorption ability for C2H2 while negligible adsorption of C2H4; and a much earlier steep increase for C3H4 isotherm compared to C3H6 isotherm in the low-pressure region. Ideal adsorbed solution theory further illustrated that the framework has the ability to separate C2H2/C2H4 and C3H4/C3H6 mixtures.

Barometric pressure monitoring and multiple applications based on pulsed airflow-driven silver nanoparticles/copper foam triboelectric nanogenerators

Abundant high-pressure exhaust gas is generated during industrial production processes. The direct release of this exhaust gas not only leads to energy dissipation but also increases the accident risk. Therefore, it is essential to utilize and monitor high-pressure exhaust gas. This study designed a triboelectric nanogenerator (TENG) driven by high-pressure pulse airflow to realize energy harvesting and pressure monitoring at the gas–solid interface. The silver nanoparticles (Ag NPs)/ copper foam (CF)-based TENG (SC-TENG) were fabricated and exhibited excellent output characteristics. Compared to the copper-based TENG, the short-circuit current and output power were increased by 68.72 % and 63.34 %, respectively, which could directly light up 111 light-emitting diodes (LEDs). Furthermore, the effects of pipeline mateal, valve material, preparation environment (AgNO3 concentration), and air pressure were investigated to optimize the electric output of SC-TENG. The SC-TENG exhibited excellent pressure sensing capabilities and could be applied for rapid and sensitive long-term monitoring. Based on this, a wireless high-pressure warning system was designed to realize signal transmission and safety pre-warning. Lastly, a novel information encryption method based on pulse airflow was developed, and its potential applications in various fields were discussed. This work not only proposes a utilization and solution strategy for high-pressure exhaust gas issues to realize the principle of “turning waste into treasure”, but also provides valuable insights into the extended applications of TENG technology.

Ultrasound-assisted enhanced leaching of lithium and fluoride compounds from waste cathode carbon: Comprehensive recovery and leaching mechanism

Due to the rapid development of the aluminum industry, waste cathode carbon, as a hazardous solid waste, is inevitably generated during the production process of the aluminum electrolysis industry. Improper treatment of waste cathode carbon will inevitably result in significant environmental damage, affecting both flora and fauna. Therefore, ultrasound-assisted leaching was employed to extract the waste cathode carbon, using a sodium hydroxide solution as the leaching medium to recover valuable metals and electrolytes, thereby mitigating environmental risks and reclaiming resources. Optimal conditions for ultrasonic-assisted alkali leaching were determined through a single-factor experiment: a leaching temperature of 70 °C, a liquid-solid ratio of 7:1, a leaching time of 50 min, an alkali concentration of 100 g/L, and ultrasonic power of 250 W. Under these conditions, the fluoride leaching efficiency reached 89.17 %, and the lithium ion leaching efficiency was 86.33 %. Comparative analyses between traditional leaching methods and the optimized experiments revealed that the advantages of ultrasonic action and cyclic leaching contributed to an increase of 5.28 % in the fluoride leaching efficiency and 11 % in the lithium ion leaching efficiency. Furthermore, ultrasonic-assisted leaching significantly reduces the leaching time to 50 min while achieving a higher rate of impurity removal.

Exploring chemical markers and identifying phenolic markers using a metabolomics strategy and chemometrics to study the different origins of defatted Coix seed

Coix seed, a prevalent medicinal and food-homologous plant, is extensively consumed in Asia. It has various pharmacological properties, such as anti-inflammatory and anticancer effects. Coix seed oil, as its main component, is widely produced. However, during the industrial production process of Coix seed oil, substantial byproducts are produced, namely, defatted Coix seeds, which are also worth researching. Currently, it remains unclear whether there will be differences in defatted Coix seeds obtained from different geographical locations, with previous studies reporting that phenolic compounds in defatted Coix seeds have a significant utilization value. In this study, firstly, the TPC and TFC of samples collected in three temperature zones were detected. Subsequently, UPLC-Q-TOF/MS was used to analyze the samples, and a metabolomics data processing strategy and chemometric analysis method were established. We have confirmed the presence of flavonoids and phenolic compounds in 30 batches of Coix seed from different temperature zones in China, and concluded that the overall quality of Coix seed from different batches is relatively stable. With the established strategy, 12 characteristic chemical markers were identified, and 5 valuable phenolic chemical markers were selected for distinguishing the origin of Coix seed and evaluating the quality of defatted Coix seed. Among them, proanthocyanidin A2 has the highest content in defatted Coix seed in subtropical regions, while the content of caffeic acid, naringin, rutin, and chlorogenic acid decreases from north to south. The strategy proposed in this study may provide some basis for the quality control and rational use of defatted Coix seeds.

Optimized structure design for binary particle mixing in rotating drums using a combined DEM and gaussian process-based model

Particle mixing is a crucial operation in various industrial production processes. However, phenomena like segregation or local accumulation can arise, especially when particles differ in properties like radius and density. Numerical simulation of particles using Discrete Element Method (DEM) allows for the manipulation of control variables in batches, generating a large amount of data and facilitating quantitative research. In this study, the mixing behaviors of binary particles in rotating drums are systematically investigated. The DEM model is first validated with experimental data and then rotating drums with varying obstacles, rotation speeds, particle radii, and densities are simulated. Moreover, a Gaussian process-based optimization is conducted by correlating Lacey mixing index (MI) and parameterized shape of obstacle to find the optimized mixing condition. Experimental validations are further performed on the optimized condition to verify the design. It is shown that this integrated approach holds significant potential for enhancing the efficiency, effectiveness of industrial mixing processes and the consideration of energy consumption when balancing the mixing efficiency and optimal rotating speed.

A modeling framework to assess fenceline monitoring and self-reported upset emissions of benzene from multiple oil refineries in Texas

Benzene as one type of hazardous air pollutants (HAPs) is produced by industrial production processes and/or emitted during upset events caused by man-made or natural accidents. Although upset emissions of benzene can be a significant contributor to the total emission, it is still challenging to quantify. This study first develops a fast modeling framework using obstacle-resolving computational fluid dynamics modeling to compare the modeled within-facility-scale passive pollutant dispersion with the observed levels based on self-reported emissions for fourteen facilities in Texas, United States. Results of numerical simulations demonstrate that neglecting the obstacle effect can underpredict (overpredict) the near-(far-)field concentrations for a low source. For a source located above obstacles, underprediction occurs at all distances. The diagnostic framework is applied to 107 self-reported upset emission events for fourteen petroleum refineries in Texas from year 2019–2022. Considering different metrics across all events, it can be concluded that the modeled concentrations based on self-reported emissions likely underpredict the observed concentration increments. Depending on the possible source height, the median factor of underprediction ranges from 3 to 95 based on the average-plume metric. The agreement between model and observation is better for events characterized by high emission amounts and rates, which also correspond to high observed concentration increments. Overall, the research highlights the importance of considering obstacles and demonstrates the potential application of the current approach as an efficient diagnostic method for self-reported upset emissions using fenceline observations of HAPs.

Development of a Value Stream Map to Optimize the Production Process in a Luxury Metal Piece Manufacturing Company

The current market is highly competitive, and customers are increasingly demanding. In this context, organizations need to adopt tools that enhance process efficiency to ensure competitiveness. This report aims to implement Lean tools, specifically Value Stream Mapping (VSM) and complementary tools, to optimize the production process in the metal treatment industry. A case study was conducted, beginning with a brief sector and process recognition, followed by an analysis of production stages using VSM. Value-added activities, non-value-added activities, and waste were identified. The current VSM revealed a Lead Time (LT) of approximately 336 h (14 days), value-added activities (VA) of 21 h, and a process cycle efficiency (PCE) of 6.29%. Improvement actions were proposed to reduce waste and increase competitiveness. After implementation, the LT decreased to approximately 318 h (13 days), VA increased to 23 h, and process efficiency improved to 7.15%. Despite the limitations of VSM in discontinuous flows, its use increased process efficiency, demonstrating its applicability in complex industrial contexts.

A sulfonated hyperbranched bio-based waterborne polyurethane sizing coating for the enhancement of mechanical properties and surface wettability of carbon fibres

Wet-weaving and cluster braiding in industrial production processes generate irreversible defects and grooves in carbon fibre (CF) filaments, which reduce the service life and mechanical properties of the fibres. Additionally, the fibre surface covered with numerous inactive carbon-containing groups and presents chemical inertness, which limits the prospects of CF applications. Hence, the hyperbranched bio-based waterborne polyurethanes (SWPU) possessing the synergism of sulfamate and carboxylate complex hydrophilic units were adopted as sizing coatings to improve the flexural and tensile strengths of CF, as well as fibre surface roughness and wettability. The SWPU is designed on dihydroxymethylbutyric acid and sulfamates (A95) as per- and post-chain extenders, respectively, trimethylolpropane (TMP) as an intramolecular cross-linker, and plant-extracts castor oil (CO) replacing petroleum-based petrochemicals as the soft-segments. The construction of CO-TMP hyperbranched cross-linking networks and the strengthened hydrogen-bonding effects of the polar sulfamates positively affect the intermolecular interactions and configuration stability of SWPU sizing coatings. SWPU revealed favourable thermo-mechanical properties achieving a T5% decomposition temperature and toughness of 297.83 °C and 35.24 MJ/m3. The coating effects of the bio-based sizing agents not only repaired defects and improved surface roughness on CF, but the polar groups in SWPU comprising SO3Na, carbamate and polyurea promoted the chemotactic activity and wettability of the fibres. The surface energy and tensile strength of the SWPU sized CF reached 43.75 mN/m and 5.68 GPa, respectively, which were 45.4 % and 30.7 % higher compared to original CF. The research contributes to the development and industrial production of high-performance, eco-friendly bio-based sizing coatings.

Study of Cocoa Powder Production Process and Packaging Technology in UGM Cocoa Teaching and Learning Industry

UGM Cocoa Teaching & Learning Industry (UGM CTLI) is a business unit based on cocoa processing as a learning and industrial pioneer and provides makloon services to produce cocoa products according to SNI standards. One of the processed cocoa products that has the highest product demand is cocoa powder because it has large market potential as a raw material for the food and beverage industry. The quality of cocoa powder can decrease during the processing process, storage process and distribution process, so it is necessary to study the production and packaging processes regarding their function in maintaining product quality from damage. This research was carried out on January 2 - January 31 2024. Data collection methods used included interviews, observation, documentation and literature study. The cocoa powder packaging used is in the form of a sewing bag consisting of kraft paper and HDPE plastic. Based on the results of previous research, the use of HDPE plastic is considered appropriate and able to protect the quality of cocoa powder better than other plastics.

CDR1, a DUF946 domain containing protein, positively regulates cadmium tolerance in Arabidopsis thaliana by maintaining the stability of OPT3 protein

Industrial and agricultural production processes lead to the accumulation of cadmium (Cd) in soil, resulting in crops absorb Cd from contaminated soil and then transfer it to human body through the food chain, posing a serious threat to human health. Thus, it is necessary to explore novel genes and mechanisms involved in regulating Cd tolerance and detoxification in plants. Here, we found that CDR1, a DUF946 domain containing protein, localizes to the plasma membrane and positively regulates Cd stress tolerance. The cdr1 mutants exhibited Cd sensitivity, accumulated excessive Cd in the seeds and roots, but decreased in leaves. However, CDR1-OE transgenic plants not only showed Cd tolerance but also significantly reduced Cd in seeds and roots. Additionally, both in vitro and in vivo assays demonstrated an interaction between CDR1 and OPT3. Cell free protein degradation and OPT3 protein level determination assays indicated that CDR1 could maintain the stability of OPT3 protein. Moreover, genetic phenotype analysis and Cd content determination showed that CDR1 regulates Cd stress tolerance and affect the distribution of Cd in plants by maintaining the stability of OPT3 protein. Our discoveries provide a key candidate gene for directional breeding to reduce Cd accumulation in edible seeds of crops.

Soft sensor modeling based on masked convolutional transformer block deep residual shrinkage network

BackgroundData-driven soft sensor technology is currently an important means for industrial data prediction, addressing the challenge of predicting key quality variables in industrial production processes. Convolutional neural networks (CNN) are widely applied in soft sensor modeling due to their excellent nonlinear modeling and feature extraction capabilities. However, CNN also faces several issues, such as poor robustness to interference and difficulties in extracting features from complex process data. MethodsThis paper introduces a novel CNN model called the Masked Convolutional Transformer Block Deep Residual Shrinkage Network (MCTB-DRSN). Firstly, the Masked Convolutional Transformer Block (MCTB) is utilized to extract features from different positions, thereby enabling the network model to focus more on important information. Secondly, the Global Response Normalization (GRN) layer is incorporated into the Deep Residual Shrinkage Network (DRSN) module, which enhances feature competition among channels. Significant FindingsThis method can provide effective monitoring for chemical production process. Compared with the traditional soft sensor method on the debutanizer column dataset, the results show that the prediction accuracy of this model is significantly improved.

The use of cheese whey powder in the cultivation of protein-rich filamentous fungal biomass for sustainable food production

Cheese whey is an industrial by-product that is generated in excess during the cheese production process in the dairy industry. Despite the potential utility of whey, it continues to pose environmental threats in the industry. This study comprehensively evaluates the utilization of two fermentation techniques (solid-state fermentation and submerged fermentation) for producing fungal biomass from cheese whey powder, employing Aspergillus oryzae, Rhizopus oryzae, and Neurospora intermedia for sustainable food production. It has been observed that submerged fermentation is more effective in increasing the protein content of whey powder compared to solid-state fermentation. The highest biomass yield was achieved with A. oryzae (5.29 g/L, 0.176 g biomass/g substrate), followed by N. intermedia (3.63 g/L, 0.121 g biomass/g substrate), and R. oryzae (1.9 g/L, 0.063 g biomass/g substrate). In the bubble column reactor, the protein content of the substrate (78.65 g/kg) increased by 165.54 and 176.69% with A. oryzae (208.85 g/kg) and N. intermedia (217.62 g/kg), respectively. This study has demonstrated that whey powder can be converted into protein-rich biomass through fungal bioconversion. The obtained biomass has the potential to be developed as an alternative food and feed source, contributing to waste management and sustainable food production.