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.

Structural Insights of a cis-Epoxysuccinate Hydrolase Facilitate the Development of Robust Biocatalysts for the Production of l-(+)-Tartrate.

l-(+)-Tartaric acid plays important roles in various industries, including pharmaceuticals, foods, and chemicals. cis-Epoxysuccinate hydrolases (CESHs) are crucial for converting cis-epoxysuccinate to l-(+)-tartrate in the industrial production process. There is, however, a lack of detailed structural and mechanistic information on CESHs, limiting the discovery and engineering of these industrially relevant enzymes. In this study, we report the crystal structures of RoCESH and KoCESH-l-(+)-tartrate complex. These structures reveal the key amino acids of the active pocket and the catalytic triad residues and elucidate a dynamic catalytic process involving conformational changes of the active site. Leveraging the structural insights, we identified a robust BmCESH (550 ± 20 U·mg-1) with sustained catalytic activity even at a 3 M substrate concentration. After six batches of transformation, immobilized cells with overexpressed BmCESH maintained 69% of their initial activity, affording an overall productivity of 200 g/L/h. These results provide valuable insights into the development of high-efficiency CESHs and the optimization of biotransformation processes for industrial uses.

Challenges, progress, and future perspectives for cyanobacterial polyhydroxyalkanoate production

AbstractPolyhydroxyalkanoates (PHA) are a promising bio-based alternative to traditional plastics derived from petroleum. Cyanobacteria are photosynthetic organisms that produce PHA from CO2 and sunlight, which can potentially reduce production costs and environmental footprint in comparison to heterotrophic bacteria cultures because (1) they utilize inorganic carbon sources for growth and (2) they do not require intensive aeration for oxygenation. Moreover, supplementing precursors such as propionate, acetate, valerate, etc., can be used to obtain various copolymers with plastic customizable properties in comparison to the classical homopolymers, such as polyhydroxybutyrate, PHB. This critical review covers the latest advances in PHA production, including recent discoveries in the metabolism interplay between PHA and glycogen production, and new insights into cultivation strategies that enhance PHA accumulation, and purification processes. This review also addresses the challenges and suggests potential solutions for a viable industrial PHAs production process.

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.

Study on the gelling properties of egg white/surfactant system by different heating intensities

The aim of this study was to elucidate the different effects and difference mechanism of gelling properties among egg white (EW) treated with different heating intensities and the composite addition of rhamnolipid and soybean lecithin. Particle size analyzer, potentiometric analyzer, surface hydrophobicity method, and Fourier transform infrared spectroscopy techniques were used to determine the physicochemical properties and molecular structure, respectively. Low-field nuclear magnetic resonance, magnetic resonance imaging, texture profile analysis, and scanning electron microscopy techniques were used to analyze the gelling properties and gel structure, respectively. And we illuminate the different mechanisms in the gelling properties of the EW with various treatments and key internal factors that play important roles in improving gelling properties by establishing the link between the gelling properties and relevant characteristics by mixed effects model and visual network analysis. The results indicate raising the content of rhamnolipid decreased the migration of immobilized water in the EW gel and the free water content. At the heating intensities of 55 °C/3.5, 65 °C/2.5, and 67 °C/1.5 min, with an increase in rhamnolipid, the gel's cohesiveness, gumminess, and chewiness gradually increased. The mixed effects model indicated that heating intensities and composite ratios have a 2-way interaction on zeta potential, the relaxation time of bound water (T21), the content of bound water (P21), the content of immobilized water (P22), and fractal dimension (df) attributes (P < 0.05). The visual network analysis showed that the protein solubility, the relaxation time of immobilized water (T22), surface hydrophobicity, zeta potential, average particle size (d43) and the relaxation time of free water (T23) are critical contributors to the different gelling properties of EW subjected to various treatments and the improvement of gelling properties. This study will provide theoretical guidance for the development of egg white products and the expansion of egg white's application scope in the egg product processing industry.

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 &amp; 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

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.

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.

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.

What will be the hydrogen and power demands of the process industry in a climate-neutral Germany?

The defossilization of industry has far-reaching implications regarding the future demand for hydrogen and other forms of energy. This paper presents and applies a fundamental bottom-up model that relies on techno-economic data of industrial production processes. Its aim is to identify, across a range of scenarios, the most cost-effective low-carbon options considering a variety of production systems. Subsequently, it derives the hydrogen and electricity demand that would result from the implementation of these least-cost low-carbon options in process industries in Germany. Aligning with the German government's target year for achieving climate neutrality, this study's reference year is 2045. The primary contribution lies in analyzing which hydrogen-based and direct electrification solutions would be cost-effective for a range of energy price levels under climate-neutral industrial production and what the resulting hydrogen and electricity demand would be. To this end, the methodology of this paper comprises the following steps: selection of the relevant industries (I), definition of conventional reference production systems and their low-carbon options (II), investigation and processing of the techno-economic data of the standardized production systems (III), establishment of a scenario framework (IV), determination of the least-cost low-carbon solution of a conventional reference production system under the scenario assumptions made (V), and estimation of the resulting hydrogen and electricity demand (VI). According to the results, the expected industrial hydrogen consumption in 2045 ranges from 265 TWh for higher hydrogen prices in or above the range of onshore wind-based green hydrogen supply costs, to up to 473 TWh for very low hydrogen prices corresponding to typical blue hydrogen production costs. Meanwhile, the direct electricity consumption of the process industries in the results ranges from 147 TWh for these rather low hydrogen prices to 338 TWh for the higher hydrogen prices in the region of or above the hydrogen supply costs from the electrolysis of energy from an onshore wind farm. Most of the break-even hydrogen prices that are relevant to the choice of low-carbon options are in the range of the benchmark purchase costs for blue hydrogen and green hydrogen produced from offshore wind power, which span between €40 per MWh and €97 per MWh.

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.

An improved MOEA/D for multi-objective flexible job shop scheduling by considering efficiency and cost

To achieve cost reduction and efficiency improvement in the production process of the aerospace industry, this study establishes a mathematical model for the multi-objective flexible job shop scheduling problem (MOFJSP). The model comprehensively considers four optimisation objectives: completion time, tool number, machine load, and machine energy consumption. For solving the MOFJSP, an improved MOEA/D (IMOEA/D) algorithm is proposed. To improve the quality of solutions, the algorithm introduces another global update strategy in addition to the original substitution operation. The population is initialised using four allocation rules, and the neighbourhood is dynamically updated based on the degree of population evolution. The simulation results on a single instance indicate that the simulation time of IMOEA/D has decreased by 10.5% and machine energy consumption has reduced by 9.5%. Moreover, when compared to various classic multi-objective algorithms on 25 benchmark instances, the IMOEA/D algorithm achieved optimal results in 19 instances for inverted generational distance comparisons, 15 instances for hypervolume comparisons and nearly all instances for set coverage comparisons. The results reveal that the proposed IMOEA/D is effective and competitive in solving MOFJSP.