Industrial Production Processes Research Articles

Intelligent Robotic Arm-Assisted Electrical Furnace for Efficient Metal Casting

Abstract: This project proposes the development of an intelligent robotic arm-assisted electrical furnace system for efficient and precise metal casting. Electric furnace is used for heating purpose in various industrial production processes. Electric furnaces are used where more accurate temperature control is required. By combining the strengths of robotic automation with the clean and controllable environment of an electric furnace, the system aims to achieve Enhanced quality and reduced waste, Improved safety and reduced hazards, increased efficiency and productivity, Greater flexibility, and versatility, Reduced environmental impact. The project will explore the integration of sensors enhancing efficiency and quality control. The integration of these technologies aims to enhance productivity, precision, and safety while reducing operational costs. The robotic arm is designed to perform loading and unloading tasks, handling raw materials and moulds with exceptional accuracy and repeatability. The robotic arm can be attached to the electrical furnace. With the help of sensors, robots can manage to avoid obstacles on their paths and achieve their goals with or without prior planning, depending on the complexity of their implemented program.

Improving Ammonia Emission Model of Urea Fertilizer Fluidized Bed Granulation System Using Particle Swarm Optimization for Sustainable Fertilizer Manufacturing Practice

Granulation is an important class of production processes in food, chemical and pharmaceutical manufacturing industries. In urea fertilizer manufacturing, fluidized beds are often used for the granulation system. However, the granulation processes release ammonia to the environment. Ammonia gas can contribute to eutrophication, which is an oversupply of nitrogen and acidification to the ecosystems. Eutrophication may cause major disruptions of aquatic ecosystems. It is estimated that global ammonia emissions from urea fertilizer processes are approximately at 10 to 12 Tg N/year, which represents 23% of overall ammonia released globally. Therefore, accurate modeling of the ammonia emission by the urea fertilizer fluidized bed granulation system is important. It allows for the system to be operated efficiently and within sustainable condition. This research attempts to optimize the model of the system using the particle swarm optimization (PSO) algorithm. The model takes pressure (Mpa), binder feed rate (rpm) and inlet temperature (°C) as the manipulated variables. The PSO searches for the model’s optimal coefficients. The accuracy of the model is measured using mean square error (MSE) between the model’s simulated value and the actual data of ammonia released which is collected from an experiment. The proposed method reduces the MSE to 0.09727, indicating that the model can accurately simulate the actual system.

Research on the impact of innovation investment on operating efficiency of listed forest product processing companies

PurposeThe forest products processing industry is a key component of the forestry economy, and the level of companies’ operating efficiency directly affects its profitability and market competitiveness.Design/methodology/approachIn order to deeply study the operation status of forest product processing industry, this paper takes the panel data of 70 listed forest product processing companies from 2015 to 2022 as the basis, and adopts BBC, CCR and DEA-Malmquist models to measure the operating efficiency of these companies. Meanwhile, the Tobit model is applied to deeply explore the impact of innovation input on operating efficiency.FindingsThe results of the paper show that: (1) the overall operating efficiency of listed forest product processing companies performs well, and the improvement of technology level promotes the growth of total factor productivity; (2) innovation input plays a significant positive role in listed forest product processing companies, which positively affects the operating efficiency.Practical implicationsA scientific and reasonable evaluation of the operating efficiency of listed forest product companies is of great practical significance to the development of the forestry industry The study of forest product processing industry is of key significance to the social economy.Originality/valueThis paper explores the improvement of production and operation efficiency of forest products processing enterprises for the purpose of in-depth analysis of the current situation of China's forest products processing enterprises, which is conducive to improving the innovation and operation efficiency of China's forest products processing enterprises, and realizing the high-quality development of China's forest products processing industry.

Using the Lean and Green Six Sigma Method at PT. XYZ, this Painting Process aims to Reduce Tosou Butsu Alloy Wheel Defects

Aluminium wheels are a significant component of the automotive industry, but opinions about their quality are primarily based on how they look. As a result, finishing is crucial to the production process in the wheel industry. Efforts must be made to reduce the frequency of Tosou Butsu flaws in the painting process, which most frequently occur in the paint section, even though there are still a lot of errors in the wheel production process. Therefore, to lessen the chance of Tosou Butsu faults developing during the wheel finishing process, the Lean & Green Six Sigma technique is applied. Measure, Analyze, Improve, Control, and Define are the five stages of the Six Sigma methodology (DMAIC). Conversely, the Six Sigma level has grown from 3,417?, which was the level before the Sigma was repaired, to 3,750? in Sigma level 3 conditions, or level 4 conditions, with the potential for Tosou Butsu defects to occur at 12104 for a million production processes. Six Sigma is then applied to In this investigation, the painting procedure was able to lower the percentage of Tosou Butsu faults from 0.0276 to 0.0121. In order to optimize efficiency and focus on system mechanisms that align with standard operating procedures and human resource development, the Six Sigma process needs to be applied regularly and refined until the Sigma level reaches 6?.

Preliminary Purification and Partial Characterization of a Functional Bacteriocin of Lacticaseibacillus paracasei Zhang and Mining for its Gene Cluster.

Bacteriocins produced by lactic acid bacteria (LAB) have good potential for use as food biopreservatives. Lacticaseibacillus paracasei Zhang (L. paracasei Zhang) is both a food use and a probiotic bacterium. This study aimed to purify and preliminary characterize the active antibacterial metabolite of L. paracasei Zhang. The cell-free supernatant of L. paracasei Zhang was collected and purified by ultrafiltration and gel filtration chromatography. The 1-3kDa active fraction could inhibit the growth of Staphylococcus aureus but not Escherichia coli. Further antibacterial activity assays revealed its capacity to suppress various foodborne and human opportunistic pathogens (including Staphylococcus aureus, Pseudomonas fluorescens, Pseudomonas aeruginosa, Listeria monocytogenes, and Bacillus cereus), but not fungi. The antibacterial activity showed good tolerance to heat (40 to 100°C), acid-base (pH 2-3 and pH 6-10), and digestions by a number of industrial and animal/human enzymes (such as trypsin, pepsin, α-amylase, and protease K, except papain); these desired properties make it a suitable biopreservative to be used in harsh and complex industrial production processes. The high papain sensitivity suggested a proteinaceous/peptide nature of the bioactivity. Moreover, our genomic data mining for bacteriocin through BAGEL4 revealed an area of interest encoding a complete set of putative genes required for bacteriocin production. In conclusion, our study showed that L. paracasei Zhang can produce extracellular functional antibacterial metabolite, likely a class II bacteriocin. Our preliminary extraction and characterization of the active metabolite demonstrated that it has good potential to be used as a biopreservative or an agent for suppressing gastrointestinal infections.

A Novel Fault Diagnosis Framework for Industrial Production Processes Based on Causal Network Inference

A Novel Fault Diagnosis Framework for Industrial Production Processes Based on Causal Network Inference

Agri-food supply chain optimization through a decentralized production process in the olive oil industry

A tactical/operational model is proposed to assist decision-making in harvest planning and olive oil production planning of the different producers in the rural sector, considering allocation and transportation decisions of the kilograms of olives to the production plant with temporary relocation decisions of the mobile olive oil mills through decentralized production activities in the agri-food supply chain. A Mixed Integer Linear Programming model is presented that maximizes the profit of small farmers in the production process of single-varietal extra virgin olive oil. The decisions are based on the geographical dispersion of the different olive growers and the oil concentration of the olive varieties in a certain period, which depends on the climatic conditions of each sector where the olives are cultivated. The mathematical programming model is applied in Coquimbo, Chile, using actual data collected from previous harvest seasons. The computational results of the optimization model indicate that transportation decisions impact olive harvesting and oil production decisions. It is concluded that cooperatives of small producers in the rural sector increase overall profits and reduce transportation costs and the number of kilometers traveled by at least 41% through a decentralized production process with mobile production plants.

Dynamic fault detection and diagnosis of industrial alkaline water electrolyzer process with variational Bayesian dictionary learning

Alkaline Water Electrolysis (AWE) is one of the simplest green hydrogen production method using renewable energy. AWE system typically yields process variables that are serially correlated and contaminated by measurement uncertainty. A novel robust dynamic variational Bayesian dictionary learning (RDVDL) monitoring approach is proposed to improve the reliability and safety of AWE operation. RDVDL employs a sparse Bayesian dictionary learning to preserve the dynamic mechanism information of AWE process which allows the easy interpretation of fault detection results. To improve the robustness to measurement uncertainty, a low-rank vector autoregressive (VAR) method is derived to reliably extract the serial correlation from process variables. The effectiveness of the proposed approach is demonstrated with an industrial hydrogen production process, and RDVDL can efficiently detect and diagnose critical AWE faults.

Directional hydrothermal hydrogenation of palmitic acid to alcohol over carbon-encapsulated highly dispersed Co catalyst

The directed conversion of fatty acids into fatty alcohols in water medium has recent received increasing attention. Here, a carbon-encapsulated catalyst (Co@C) is designed and used for the hydrothermal hydrogenation of palmitic acid (a model compound of microalgae-based bio-oils) to produce 1-hexadecanol, exhibiting ultra-high catalytic activity. The yield of 1-hexadecanol is as high as 95.1 % at 200 °C within 60 min. The characterization results indicate that a large number of highly dispersed CoO nanoparticles (CoO NPs) provide sufficient hydrogenation active centers for the Co@C catalyst. More importantly, under the protection of the carbon layer, the Co@C catalyst exhibits high hydrothermal stability, far superior to the conventional supported Co/AC catalyst. The catalyst uses inexpensive glucose and cobalt nitrate as raw materials, with a simple synthesis process, low cost, and easy large-scale industrial production. This work provides a new strategy for obtaining catalysts with hydrothermal stability and activity for production of alcohols.

A Review of Emerging Technologies for the Extraction of Bioactive Compounds from Berries (Phalsa Berries)

Berries have been gaining in popularity among consumers and producers due to their natural bioactive compounds that have beneficial effects on human health. This review aimed to identify effective techniques for the extraction of bioactive compounds from berries, consolidate the findings of recent studies using various extraction technologies, and provide a global perspective on the research trends in this field. These extraction techniques include pulsed electric field, ultrasound-assisted extraction, pressurized liquid extraction, microwave-assisted extraction, and supercritical CO2 extraction. The solid waste generated during the industrial berry juice production process is assumed to be a less expensive source of raw materials for the natural extraction of bioactive compounds. The main aim of modern techniques is to produce more of the desired compound and find a method to extract bioactive compounds from berries without the use of hazardous solvents. These include flavonoids, phenols, anthocyanins, and antioxidants. Regarding the characterization of the bioactive compounds that are isolated from berries, aspects such as scanning electron microscopy, X-ray powder diffraction, Fourier transform infrared spectroscopy, and nuclear magnetic resonance were reviewed.

Gas–Liquid Two-Phase Flow Measurement Based on Optical Flow Method with Machine Learning Optimization Model

Gas–Liquid two-phase flows are a common flow in industrial production processes. Since these flows inherently consist of discrete phases, it is challenging to accurately measure the flow parameters. In this context, a novel approach is proposed that combines the pyramidal Lucas-Kanade (L–K) optical flow method with the Split Comparison (SC) model measurement method. In the proposed approach, videos of gas–liquid two-phase flows are captured using a camera, and optical flow data are acquired from the flow videos using the pyramid L–K optical flow detection method. To address the issue of data clutter in optical flow extraction, a dynamic median value screening method is introduced to optimize the corner point for optical flow calculations. Machine learning algorithms are employed for the prediction model, yielding high flow prediction accuracy in experimental tests. Results demonstrate that the gradient boosted regression (GBR) model is the most effective among the five preset models, and the optimized SC model significantly improves measurement accuracy compared to the GBR model, achieving an R2 value of 0.97, RMSE of 0.74 m3/h, MAE of 0.52 m3/h, and MAPE of 8.0%. This method offers a new approach for monitoring flows in industrial production processes such as oil and gas.

Quantitative shear stress imaging in high throughput in-line Si wafer inspection

Defect inspection methods are basic steps in electronic chip manufacturing and power device production processes in the semiconductor industry. Based on stress induced photoelastic anisotropy, in-line polarized stress imaging (PSI) and quantitative determination of the local shear stress component are possible in single crystalline silicon wafers. Hereafter, such polarization stress images are compared and validated quantitatively with results of finite element simulations based on different 3D elastic models. Uniquely, high resolution at high throughputs is achieved for 300 mm wafer size in the present quantitative stress imaging study.

The Role of AI in Co-design and Production Process in the Design Industry: A Prompt Investigation in the Creative Industry

The Role of AI in Co-design and Production Process in the Design Industry: A Prompt Investigation in the Creative Industry

Selection of waste receiving companies for sustainable industrial symbiosis network: an application a case in Ankara for foundry industry waste

AbstractIn the past twenty years, besides the technological and innovative efforts for production processes, there have been activities for the recovery and quality improvement of waste based on the development of sustainable solutions. These activities bring us closer to zero waste by protecting natural resources and reducing the environmental impact of production facilities. One of the essential approaches in this process is the industrial symbiosis (IS) approach. Efforts to implement the industrial symbiosis approach in an industrial park constitute the motivation of the study. The study aims to identify alternative enterprises that can evaluate the wastes generated in the main production process of the foundry industry with industrial symbiosis and rank them to establish a symbiotic relationship. First, the Pythagorean Fuzzy Analytical Hierarchy Process (PF-AHP) model has been developed to evaluate organizational factors, waste-specific factors, and economic and environmental sustainability criteria for establishing an IS network among alternative businesses. Then, alternative businesses were ranked to establish a symbiosis network with the PF-TOPSIS method. The proposed method contributes to the solution of a real-life problem that may arise when there are multiple alternatives to cooperate in the field of industrial symbiosis. By contributing this framework to the literature, the robustness of the results has been demonstrated through sensitivity analysis and comparative analysis. According to the results obtained, it was seen that the importance levels of economic factors came to the fore. Furthermore, among the alternatives, enterprises with high waste exchange potential, such as cement, took first place in the ranking.

Advanced Technologies in Food Processing—Development Perspective

Research into innovative techniques in food technology is developing dynamically. This is indicated by the significant increase in the number of scientific studies in this field. The aim of this work was to provide a comprehensive, in-depth analysis of the available scientific evidence on new techniques used in food that not only increase efficiency but also enable the creation of products with desirable sensory and nutritional characteristics. Research on techniques including cold plasma, high-pressure processing, ultrasound, pulsed electric fields, sous vide, and microwave heating aims to provide innovative methods of food processing, in the context of meeting growing consumer expectations and optimizing production processes in the food industry. Compared to traditional food processing methods, innovative techniques can provide more efficient solutions in the processing of products. Research on alternative non-thermal methods in food technology suggests their possible benefits, including enhancing sensory and nutritional quality, minimizing environmental impact, and increasing production efficiency, which are a significant challenge in the modern food industry. Despite the many benefits, it is worthwhile to continue research to further improve modern food technologies.

Uncovering crystal growth and regeneration mechanism to control crystal morphology: The case study of aceclofenac

The study of crystal growth and regeneration mechanisms is of great significance for controlling the morphology of crystals. Through the investigation of the recovery and regrowth processes of crystals, we have revealed the regeneration mechanism of aceclofenac (ACF) crystals and the influence of hydroxypropyl methylcellulose (HPMC) on the regeneration of ACF crystals. Firstly, ACF crystal regeneration experiments were conducted in acetone (ACT) and methyl acetate (MA), and it was found that the broken crystals could be successfully regenerated and restored to their original morphology, the regenerated ACF crystals in MA exhibited a larger aspect ratio. Secondly, the crystals obtained from the two solvents were placed in different solvents for regrowth, and it was shown that solvent was a key factor influencing the aspect ratio of ACF crystals. Additionally, HPMC was used to regulate the crystal morphology, it was found that the best effect was achieved when the mass fraction of HPMC was 0.5%, resulting in a regenerated crystal with an aspect ratio of only 2.19. Molecular simulation results indicated that the interaction between the radial (110) crystal facet and the solvent was weaker than that with the axial (1 0 -1) crystal facet, leading to a higher radial growth rate and a larger aspect ratio of the crystal. However, after adding HPMC, the interaction between HPMC and the radial (1 1 -1) and (110) crystal facets became stronger. HPMC selectively adsorbed on these two crystal facets, ultimately resulting in crystals with a smaller aspect ratio. The research findings of this study will provide theoretical basis for solving the problem of difficult morphology control due to crystal fragmentation in the pharmaceutical industry production process.

The impact of pressure-shift freezing on the three-dimensional network structure and properties of myofibrillar proteins in surimi gel derived from freshwater fish

Freezing treatment can prolong the shelf life of surimi products, but it will lead to deterioration of surimi gel quality. This study compared the effects of three different processing methods — non-freezing processing (control), conventional air freezing (CAF), and pressure-shift freezing (PSF) — on the three-dimensional (3D) network structure of grass carp surimi gel. After PSF treatment, the pores in the 3D gel network structure were denser compared to the control group which facilitates interaction of water and network structure. Furthermore, the ultraviolet and fluorescence spectral analysis indicated that PSF treatment mitigates the decline in stability of the tertiary structure of myofibrillar protein (MP) during freezing. The microstructure of MP showed that PSF treatment promotes the cross-linking of MP to form protein aggregates and then promotes the gelation of MP. Industrial relevancePSF can increase the commodity value of low-value freshwater fish products and has potential in the development of new products of surimi gels. This study reported a novel idea and theoretical basis for the application of PSF technology in the frozen food and aquatic product processing industry.

Changes in properties of myofibrillar protein and myofibrillar protein gel from freshwater fish after low-temperature and high-pressure collaborative treatment

This study investigated the effects of low-temperature and high-pressure collaborative and conventional air freezing (CAF) on myofibrillar protein (MP) characteristics from grass carp. Turbidity, solubility, and particle size analysis showed that the pressure shift freezing (PSF) treatment could inhibit the formation of large MP aggregates to a certain extent during freezing. Chemical interaction studies unveiled that PSF treatment not only mitigates damage to the inherent structure of MP during freezing but also enhances its crosslinking ability. Microstructure analysis revealed a densely woven fibrous protein network structure within the MP gel in the PSF group. MP gel with fiber shape network structure in the PSF group exhibited stronger water-holding capacity and mechanical properties compared to the CAF group having an incomplete protein network structure. This suggests that the combined application of low-temperature and high-pressure treatment holds promising potential for utilization in the surimi gel processing industry and aquatic products processing.Industrial relevance: The quality and safety of food are closely related to the commodity value of food. Combined low-temperature and high-pressure treatment can enhance the commodity value of low-value freshwater fish products and has potential in surimi gel new product development. This study provides theoretical guidance for high-pressure and low-temperature collaborative processing and industrial production of related aquatic products.

Boosting Electrocatalytic Ethylene Epoxidation by Single Atom Modulation.

The electrochemical synthesis of ethylene oxide (EO) using ethylene and water under ambient conditions presents a low-carbon alternative to existing industrial production process. Yet, the electrocatalytic ethylene epoxidation route is currently hindered by largely insufficient activity, EO selectivity, and long-term stability. Here we report a single atom Ru-doped hollandite structure KIr4O8 (KIrRuO) nanowire catalyst for efficient EO production via a chloride-mediated ethylene epoxidation process. The KIrRuO catalyst exhibits an EO partial current density up to 0.7 A cm-2 and an EO yield as high as 92.0 %. The impressive electrocatalytic performance towards ethylene epoxidation is ascribed to the modulation of electronic structures of adjacent Ir sites by single Ru atoms, which stabilizes the *CH2CH2OH intermediate and facilitates the formation of active Cl2 species during the generation of 2-chloroethanol, the precursor of EO. This work provides a single atom modulation strategy for improving the reactivity of adjacent metal sites in heterogeneous electrocatalysts.

Boosting Electrocatalytic Ethylene Epoxidation by Single Atom Modulation

AbstractThe electrochemical synthesis of ethylene oxide (EO) using ethylene and water under ambient conditions presents a low‐carbon alternative to existing industrial production process. Yet, the electrocatalytic ethylene epoxidation route is currently hindered by largely insufficient activity, EO selectivity, and long‐term stability. Here we report a single atom Ru‐doped hollandite structure KIr4O8 (KIrRuO) nanowire catalyst for efficient EO production via a chloride‐mediated ethylene epoxidation process. The KIrRuO catalyst exhibits an EO partial current density up to 0.7 A cm−2 and an EO yield as high as 92.0 %. The impressive electrocatalytic performance towards ethylene epoxidation is ascribed to the modulation of electronic structures of adjacent Ir sites by single Ru atoms, which stabilizes the *CH2CH2OH intermediate and facilitates the formation of active Cl2 species during the generation of 2‐chloroethanol, the precursor of EO. This work provides a single atom modulation strategy for improving the reactivity of adjacent metal sites in heterogeneous electrocatalysts.