Industrial Production Processes Research Articles

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.

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 to 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.

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.

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.

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.

An experimentally validated model for a multivariate drying industrial process

Modeling of industrial production processes is becoming increasingly challenging due to their large number of variables. These variables often are highly correlated and introduce nonlinear and complex features. In this paper, we are interested to model an industrial dicalcium phosphate (DCP) drying process. Attempting to solve this issue is motivated by the need to improve several production conditions, such as minimizing the consumption of natural gas and reducing the pollution rate. In fact, applying an advanced control approach requires a dynamic model for the monitored plant. This work proposes a multivariable mathematical model for the DCP dryer within the Tunisian Chemical Group factory. A steady-state model has been reproduced using Aspen Plus software tool to implement the different functionalities of the system as well as involved reactions. Indeed, since the main operation in the drying process is the combustion reaction of the liquified petroleum gas (LPG) in the furnace that produce the necessary heat to reach a target value of temperature at the dryer outlet, we focus on determining a dynamic model for the furnace. To do so, we have proposed two approaches. The first is based on the Aspen dynamic tool. The second is based on the left matrix fraction description (LMFD) identification approach. The obtained results have been successfully validated using real measurements.

Integration of Industry 4.0 technologies in industrial manufacturing processes

The integration of Industry 4.0 technologies into industrial production processes represents a significant evolution. This article explores the impact and benefits of this integration, showing how emerging technologies are revolutionizing traditional production methods. The methodology addresses the fundamental concepts of Industry 4.0, such as device interconnection, real-time data analysis, and intelligent automation, and examines specific technologies incorporated into manufacturing processes, such as the Internet of Things (IoT), Additive Manufacturing, Artificial Intelligence (AI), and Augmented Reality (AR). Case studies and practical examples illustrate the implementation of these technologies in various sectors, from precision parts manufacturing to mass production. Finally, the challenges and opportunities are discussed, as well as the future implications for the manufacturing industry. This article offers a view of the role of Industry 4.0 in industrial production processes, highlighting its importance for competitiveness and innovation in the digital world.

Synergy of alloy and ligand for CO2 hydrogenation to formic acid on PdAu/HPC-AP

Formic acid (FA) is of great significance given its extensive applications in various industrial fields. At present, methanol carbonylation is the main industrial production process of FA. However, this process uses two kinds of raw materials from fossil fuels and emits a large amount of CO2. Alternatively, converting CO2 into FA can not only achieve the production of value-added chemicals but also effectively address environmental issues caused by CO2 emission. To improve the low reactivity of CO2, homogeneous and heterogeneous catalytic systems have been widely evaluated, among which heterogeneous catalytic systems are attracting ever-increasing attention owing to their great industrialization prospects. However, some of these heterogeneous catalysts suffer from low catalytic performance. Herein, PdAu alloy nanoparticles anchored on amine-functionalized hierarchical porous carbon (PdAu/HPC-AP) were constructed to drive efficient converting of CO2 into FA. Characterization and testing results demonstrated that the introduced Au atoms caused lattice distortion and charge redistribution on the surface of the bimetallic catalyst, facilitating the adsorption and activation of reaction molecules. Furthermore, the modification of HPC with an appropriate amount of amine ligands was found important to the chemical adsorption of CO2 and the dispersion of PdAu NPs. The Pd60Au40/HPC-AP catalyst with the optimum synergy of alloy and ligand demonstrated excellent activity for the target reaction with a TOF value of 486 h–1 at 100 °C. Moreover, it can even catalyze the reaction at room temperature with a TOF value of 13 h–1.

The relevance of introducing a requirements management system in the production process of the aircraft engine construction industry

It is necessary to have access to documents containing product requirements during the design process. Search and processing of this data slows down the product design significantly. The reduction of time losses calls for the availability of a proper software tool. In this work, programs for the creation of requirements management systems are considered. The proposed concept of the requirements management system will make it possible to increase the efficiency of the product design process. The identified potential of the software allows us to talk about the possibility of introducing this program into the production process of enterprises in the aircraft engine industry.

Linearity Study of Color Measurement Methods in Digital Image Colorimetry

Color metrology is steadily developing in the interests of fundamental and applied science, improving industrial production processes. The author considers the problem of ensuring metrological traceability of color measurements under natural conditions.Digital image colorimetry methods demonstrate good linearity and precision in relatively small application ranges in studies of same type and mainly non-luminous objects. For complex scenes with a wide range of brightnesses, nonlinear functional color rendering models are used that are focused on visual perception, peak brightness of the monitor and reference viewing conditions, which complicates the processing of measurement results and reduces a confidence interval.The described method of measuring the chromaticity coordinates of an object by its digital image is based on tensor calculus and consists of multiple registration of an object and sets of reference samples with different exposure times, building calibration dependencies forming a multidimensional colorimetric scale in color space and determining chromaticity coordinates as points of intersection of color tensors with the plane of the color locus. In order to ensure accuracy and reliability of the measurement results in the software and hardware environments, a validation study of the performance indicators of this method was carried out, one of which is linearity. An experiment based on comparing the developed method with the reference measurement method showed the effectiveness of the developed wide-range measurement method with good linearity (correlation coefficient R=0.97… 0.99, residual relative standard deviation s0≈1–2%) when adjusting from the subjective factor and display device.The experimental materials may be useful to metrologists solving problems of determining the metrological traceability of the color of digital images in media. The article will be of interest to a wide range of readers with professional interests related to the colorimetry of digital images.

Machine learning models in the process of metal organic chemical vapor deposition epitaxial manufacturing of Gallium Arsenide

Metal–organic chemical vapor deposition (MOCVD) is a vapor-phase epitaxial growth technique commonly used in industry and academia to manufacture high-quality Gallium Arsenide (GaAs) compound semiconductor materials. In the actual industrial GaAs MOCVD epitaxial production process, a large amount of process parameters, sensor data, epitaxial wafer testing data, etc. are generated. These epitaxial-growth big data have large sample size and high noise, making data mining increasingly difficult. Nevertheless, this indicates that these data contain abundant valuable information. Therefore, this study proposes a machine learning–based GaAs MOCVD growth model. By analyzing the GaAs epitaxial growth data generated during MOCVD of GaAs, a machine learning model is established to predict the optimal production process for epitaxial growth. By mining the relationship between variables and developing a predictive model for assessing the performance of epitaxial materials, important guidance can be provided for process parameter tuning of GaAs epitaxial growth, considerably reducing debugging costs and further improving the intelligence level of GaAs MOCVD epitaxial growth.

A Modification Depth First Search (DFS) Algorithm for Troubleshoot Rotating Equipment Diagnosis

Rotating Equipment has a role in the industrial production process. There are times when the equipment that is being operated has trouble. Operators have difficulty dealing with problems of rotating equipment due to limited knowledge. To solve this problem we must have an expert with knowledge and experience. Based on this, the problem is building an expert system application to diagnose troubleshoot on rotating equipment which aims to transfer the knowledge that an expert has into the computer so that operator can find out what problems occur. This paper use Depht First Search (DFS) method, namely inward tracing techniques and Forward Chaining, namely the inference method that uses reasoning where to test a hypothesis starts from a fact. This system is equipped with an expert menu for knowledge management, so that experts can add, edit, and delete knowledge. The results showed that DFS and Forward Chaining are very suitable for diagnosing troubleshooting on Rotating Equipment. Based on the reasoning of the experts in their field and adjusted to the symptoms experienced by equipment so that the type of damage is found. It can also assist operators in diagnosing troubleshoot on Rotating Equipment so that operators can take preventive action to prevent further damage to the equipment.

Фінансові перешкоди впровадженню теплових насосів в централізованому теплопостачанні

The use of powerful industrial heat pumps (HP) is a fairly effective means of rational and environmentally friendly use of energy resources both in individual households and in district heating (DH), as well as in production processes in various industries. Powerful HPs are used in Ukraine much less than in developed European countries. In fact, these are isolated cases. The post-war reconstruction of destroyed cities with their district heating systems gives Ukraine a unique chance to use modern energy-efficient technologies, including HP. As a result, efficiency will improve and DH competitiveness will increase due to the low cost of thermal energy for consumers. Almost all developed countries of the world have government financial support for the HP implementation. The purpose of this study is to determine the level and form of government financial support for projects to implement HP in DHs of Ukraine. Financial modeling of the implementation of heat pump plants shown that they are more expediently to be used in the mode of generating thermal energy than in the mode of regulating the electric load of power systems. It is shown that the projects of heat pump plants, which use the heat of flue gases as a source of low-potential heat (LPH), are the most economically attractive and practically do not require government financial support. Projects of heat pump plants, which use air, ventilation emissions, waste water, soil and groundwater, sea, rivers, and waste heat of technological processes as LPH, are not financially attractive without government financial support, and some of them are even unprofitable. The most appropriate comprehensive financial support is tax incentives and interest compensation on the loan. Sensitivity analysis showed that projects for the implementation of heat pump plants may be the most sensitive to the amount of generated thermal energy, the price of natural gas and electricity. Keywords: heat pumps, low-potential heat sources, district heating, financial obstacles, tax incentives, grants.