Production Line Research Articles

Assessing the spread of sulfachloropyridazine in poultry environment and its impact on Escherichia coli resistance

Sulfachloropyridazine (SCP) is an antimicrobial (AM) commonly used in the poultry industry. This drug is excreted as the original compound, which may accumulate in litter. This work was done to assess whether SCP residues from droppings of broiler chickens that were treated with therapeutic doses of this drug spread into the production environment and to determine if these events were associated with the selection of resistant bacteria. To this end, broiler chickens were raised under controlled conditions, and their droppings and litter were processed to detect and identify SCP residues using an HPLC-MS/MS technique. This study selected Escherichia coli as an indicator bacterium for AM resistance. Its phenotypic resistance was determined using the Kirby-Bauer disk diffusion method, and its genotypic resistance was determined by performing a conventional PCR test. Our results showed that SCP residues did spread from the treated group to untreated sentinel groups because SCP residues in the litter reached levels up to 43.05 µg·kg−1 in a group placed immediately adjoining to the treated group, while another group placed 30 cm away showed a concentration of 29.79 µg·kg−1. Meanwhile, only trace concentrations were detected in droppings collected from sentinel groups. Of 239 strains of E. coli isolated from droppings, 12.13 % were resistant to sulfonamides, whereas 23.91 % of 92 E. coli isolated from broiler litter were resistant. The most prevalent resistance gene was the sul2 gene, both in droppings and litter, followed by the sul1 gene. The SCP concentrations were associated with the probability of E. coli being resistant to sulfonamides (p-value = 0.01). A Multiple Correspondence Analysis (MCA) also showed that phenotypic and genotypic resistances were associated and that both genes sul1 and sul2 would determine phenotypic resistance to sulfonamides in E. coli. The results presented in this study show that inedible by-products of the poultry industry are potentially a source of drug resistance that can spread from the animal production line to the environment, so awareness of the correct use of antimicrobials is essential to combat antimicrobial resistance.

Black powder and mineral deposits in the LPG production and processing line: Analytical study and formation mechanisms

The accumulation of mineral deposits such as black powder causes severe impacts throughout the gas production and transport process. The phenomenon takes place from gas wells to LNG/LPG treatment plants and terminals. The complexity lies in understanding the mechanisms by which these materials are formed in random quantities, as well as their varied nature. The aim of this study is to monitor black powder and other mineral deposits formation in a chain of gas production and processing situated in the south of Algeria and unravel their root causes. To that extent, an analytical study was carried out on four samples of both mineral deposits and black powder taken from an LPG processing and production unit. The adopted analytical approach included an exploration of the textural and structural properties. It also involves a composition characterizations related to the content of metallic elements, moisture, organic and mineral matter. The particle distribution is also determined. The analytical study was conducted using gravimetric determination, XRD, XRF, SEM/EDX and Raman spectroscopy. Particle distribution was given by Laser granulometry technique. The results of the DRX/FRX and EDX characterization primarily revealed that the samples contain a variety of mineral components such as iron oxides; magnetite Fe3O4 and hematite Fe2O3, iron oxy-hydroxide FeO(OH), iron sulphide FeS2, calcite CaCO3, SiO2 and halite NaCl. The presence distribution of these components depends on the location of the sample in gas line. Granulometry study shows that black powder contamination can be spread over a wide spectrum of particle sizes ranging from 1.51 µm to 678.50 µm. This study has allowed to elucidate the nature and distribution of particles deposited at different positions in the LPG production chain and to propose probable mechanisms for their formation.

Methodology to Generate Strategic Manufacturing Indicators to Support Decision-Making – Case Study of a Food Company

This study proposes a methodology that selects key performance indicators and measures their influence on the strategic manufacturing objectives of a food company. The proposed hybrid methodology used the focus model centered on a value function to select criteria and classify key performance indicators and the multi-criteria model DEMATEL to identify the level of influence of key performance indicators on strategic manufacturing objectives. This new method constructs a value function with input from all production line members. This allows the degree of direct and indirect influence of selected key performance indicators to be measured to achieve the organization’s strategic objectives. Finally, seven key performance indicators were selected, the most influential being the level of automation, performance, set-up times, and resource use.

Intelligente Anlaufsteuerung für die Batteriezellenproduktion

Abstract The ramp-up of production lines as a result of changing product variants or production processes puts battery cell manufacturers under enormous quality and cost pressure due to high reject rates. A control system based on artificial intelligence methods can help to speed up the start-up processes. The InTeAn research project developed a procedure for developing a process control system to reduce ramp-up times and costs in battery cell production.

A Computer Vision Model for Seaweed Foreign Object Detection Using Deep Learning

Seaweed foreign object detection has become crucial for food consumption and industrial use. This process not only can prevent potential health issues, but also maintain the overall marketability of seaweed production in the food industry. Traditional methods of inspecting seaweed foreign objects heavily rely on human judgment, which deals with large volumes with diverse impurities and can be inconsistent and inefficient. An automation system for real-time seaweed foreign object detection in the inspection process should be adopted. However, automated seaweed foreign object detection has several challenges due to its dependency on visual input inspection, such as an uneven surface and undistinguishable impurities. In fact, limited access to advanced technologies and high-cost equipment would also influence visual input acquisition, thereby hindering the advancement of seaweed foreign object detection in this field. Therefore, we introduce a computer vision model utilizing a deep learning-based algorithm to detect seaweed impurities and classify the samples into ‘clean’ and ‘unclean’ categories. In this study, we managed to identify six types of seaweed impurities including sand sticks, shells, discolored seaweed, grass, worm shells, and mixed impurities. We collected 1204 images and our model’s performance was thoroughly evaluated based on comparisons with three pre-trained models, i.e., Yolov8, ResNet, and MobileNet. Our experiment shows that Yolov8 outperforms the other two models with an accuracy of 98.86%. This study also included the development of an Android application to validate the deep learning engine to ensure its optimal performance. Based on our experiments, the mobile application managed to classify 50 pieces of seaweed samples within 0.2 s each, showcasing its potential use in large-scale production lines and factories. This research demonstrates the impact of Artificial Intelligence on food safety by offering a scalable and efficient solution that can be deployed in other food production processes facing similar challenges. Our approach paves the way for broader industry adoption and advancements in automated foreign object detection systems by optimizing detection accuracy and speed.

Deep reinforcement learning optimization scheduling algorithm for continuous production line

In order to improve the scheduling effect of continuous production lines and enhance the processing efficiency of production lines, a deep reinforcement learning optimization scheduling algorithm for continuous production lines is proposed. The Monte Carlo algorithm and Bayesian evaluation method are combined to reduce the data complexity of the continuous production line pipeline problem; the deep neural network model is used to optimize the pipeline scheduling parameters, evaluate and encode them; the iterative greedy algorithm is combined with the deep reinforcement learning method to implement the model solution for the scheduling data problem and realize the scheduling of continuous production lines. The experimental results show that the optimal comprehensive evaluation results of the scheduling results of the proposed algorithm are all higher than 0.9531, the process delay is optimized to less than 5 min, the collection speed is fast, and the processing efficiency of the production line is improved.

Manipulator with Integrated Flexible Tactile Sensing Arrays for Kiwifruit Ripeness and Size Classification.

Fruit grading for ripeness and size is an essential process in the supply chain. Incorrect grading can easily lead to spoiled and degraded fruits entering the market, reducing consumers' confidence in purchasing. At the same time, it is easy to cause the fruit supply chain to reduce profits, unreasonable resource allocation, and related practitioners' income. The current mainstream machine vision grading and manual grading in the production line have dilemmas such as susceptibility to environmental interference, inconsistent grading standards, high cost, and labor shortage. To overcome these problems, this study proposes an integrated flexible tactile sensing array (3 × 4) manipulator for efficient, stable, low-cost, and accurate ripeness and size grading of kiwifruit. The flexible sensing manipulator grasps the kiwifruit, detects the hardness of the kiwifruit by relying on tactile sensing, and determines the ripeness level based on the hardness. The size of the kiwifruit is also differentiated according to whether there is a significant change in the resistance of the topmost sensing unit of the flexible pressure sensor array. The 0, 1, 2, 3, 4, and 5 anomalies that may occur in actual production were tested and combined with machine learning KNN, SVM, and RF algorithms for data modeling and grading. The results show that the lowest accuracy of 0, 1, 2, 3, 4, and 5 possible outliers is 86.67% (KNN), 95.83% (SVM), and 92.5% (RF), respectively. KNN has the lowest classification effect, and SVM has the best. This study overcomes the drawbacks of inefficient destructive detection and unstable manual detection and makes up for the vulnerability of single machine vision to interference from environmental factors. This study can alleviate the challenges caused by fruit wastage and promote the sustainable production and consumption of the fruit industry chain.

Boosting Algorithms for the Accident Severity Classification

<div><i>Background:</i> Road accident severity estimation is a critical aspect of road safety analysis and traffic management. Accurate severity estimation contributes to the formulation of effective road safety policies. Knowledge of the potential consequences of certain behaviors or conditions can contribute to safer driving practices. Identifying patterns of high-severity accidents allows for targeted improvements in terms of overall road safety. <i>Objective:</i> This study focuses on analyzing road accidents by utilizing real data, i.e., US road accidents open database called “CRSS.” It employs advanced machine learning models such as boosting algorithms such as LGBM, XGBoost, and CatBoost to predict accident severity classification based on various parameters. The study also aims to contribute to road safety by providing predictive insights for stakeholders, functional safety engineering community, and policymakers using KABCO classification systems. The article includes sections covering theoretical methodology, data analysis, model development, evaluation, performance metrics, and implications for improving road safety measures by comparing the performance of different boosting algorithms on the CRSS dataset. This study aims to identify the most effective machine learning algorithm to integrate into our product line in the near future, enabling accurate prediction of both accident severity and occurrence. <i>Results and Conclusions:</i> This study addresses challenges in evaluating performance metrics for different severity classes within unbalanced datasets, emphasizing the impact of dominant classes like Class O (O = no apparent injury) on overall accuracy. The investigation reveals the limitations and conservatism associated with imbalanced data in boosting models, hinting at a potential ceiling in their performance around 80%. Comparative analysis of algorithms, including CatBoost, XGBoost, and LGBM, demonstrates comparable performance even in the case of applying KNN algorithm for pre-processing, based on various metrics, especially accuracy, <i>F</i><sub>1</sub>-score, ROC-AUC, and PR-AUC for all severity classes. XGBoost with KNN algorithm did not show any significant performance improvement compared to the XGBoost without KNN algorithm. The study includes performance metrics, such as <i>F</i><sub>1</sub>-score, CM upper triangle, ROC-AUC, and PR-AUC applied to an accident analysis case study. Future work directions involve extending the application of CatBoost, XGBoost, and other algorithms to diverse datasets, exploring the capabilities of deep neural networks, refining dataset preparation for accuracy improvement, and creating unified tools for hazard analysis and risk assessment.</div>

Heterogeneous Graph Neural Network for Modeling Intelligent Manufacturing Systems

Abstract Currently, manufacturing systems have become more and more complex, often involving multiple machines, systems and processes to produce workpieces. In order to facilitate comprehensive analysis and control of manufacturing processes, the integration of connections between machine level, system level, and process level in manufacturing process modeling is needed. However, traditional graph deep learning models are unable to take into account the heterogeneity of different machines in the system when modeling manufacturing systems. To address this problem, this paper proposes a new approach: modeling manufacturing systems using the Heterogeneous Graph SAmple and aggreGatE algorithm (HGNN) based on cutting-edge Bayesian neural networks and graph deep learning. This method considers the connection between different manufacturing system levels, treats machine operations as nodes, and connects different nodes through material flow and operational similarity. The effectiveness of the method is demonstrated through its application to model an aero-engine blade production line. Extensive numerical experiments show that the proposed graph modeling method is effective in expressing the heterogeneity of different machines and multi-level manufacturing processes. By integrating machine heterogeneity into the modeling of a manufacturing system, it not only facilitates comprehensive analysis and control of the manufacturing process, but also lays the foundation for cost savings and productivity improvements in the manufacturing system.

Case Analysis About Demand Changes of New Energy Vehicles and Its Stock Returns

The industry for new energy vehicles (NEVs) has experienced giant growth which draws interest from investors, decision-makers, and scholars. This research examines the relationship between changes in market demand and the stock returns of NEV firms. The method we use is to take into account a number of variables including international relations, governmental regulations, and developments in related industries. This article investigates the cases of Tesla Inc. and NIO Inc. on the performance of NEV stocks by the following ideas: the impact of foreign relations, government policies, and shifts in associated industries. For investors in the NEV industry, the results highlight how crucial it is to understand market dynamics as well as possible dangers and possibilities. Also, the article suggests topics for further investigation, such as the effects of NIO's global initiatives like growing product lines and Tesla's growing product range. Stakeholders hoping to properly manage the changing NEV ecosystem will find great insights from this thorough investigation.

Non-ischemic cerebral enhancing (NICE) lesions after flow diversion for intracranial aneurysms: a multicenter study

BackgroundNon-ischemic cerebral enhancing (NICE) lesions have been reported as a rare complication of various neuroendovascular procedures, but information on their incidence after flow diversion is scant. It is unclear if...

A line scanning monitoring method for conveyor belt deviation using point cloud

Abstract A precise conveyor belt deviation monitoring method using line array point cloud data is proposed and demonstrated, which can ensure the healthy running of the conveyor system. The point cloud data characterizing the surface of the conveyor belt is collected in a line scanning way. Then, using a unique soft extraction method that weighted fusing three key features (cross-sectional variation, belt’s horizontal width, and previous frame) to process this data, the edge information of the conveyor belt can be accurately and robustly identified in real-time. Furthermore, the point cloud processing mode enables a belt-segmented deviation analysis method based on a standard sequence query. This can accurately determine the offset value and deviation trend of the conveyor belt, thereby achieving early warning of deviation faults. Experimental results show that the belt edge identification precision can reach 0.3 mm, and an early warning can be provided at least 57 m before the occurrence of a belt deviation fault. This belt deviation monitoring method can be widely applied in various working environments, especially in harsh conditions like mines and ports. It also has potential applications in automated production lines within Industry 4.0.

A new method for growing epitaxial films on foreign substrates − Bent epitaxy

In recent years, to mitigate the effects of climate change, countries’ transition to the green economy has accelerated, and thereby, the demand of semiconductor industry for wide-gap semiconductors such as SiC and GaN has increased dramatically. These semiconductors are expensive. To reduce the price of the final product, industrial production of thin epitaxial films of sought-after semiconductors on accessible and cheap substrates, such as Si and sapphire, is being developed. When growing epitaxial films on foreign substrates, the resulting heterostructure bends, becomes concave or convex, due to a mismatch between the coefficients of thermal expansion (CTE) of the substrate and the epitaxial film. As a result, the quality of the epitaxial film deteriorates and further processing of the resulting heterostructures in production lines becomes difficult or even impossible. The essence of the proposed new method is that the epitaxial film is grown not on a flat, but on a pre-curved substrate with the expectation that after cooling the fabricated heterostructure to room temperature, the heterostructure will take a flat shape due to the difference in the CTE of the epitaxial film and the substrate. A round bimetallic plate, located under the substrate, bends the substrate in the required direction to the desired value at the growth temperature, and gradually reduces the bend of the fabricated heterostructure during cooling, in proportion to the thermal contraction of the epitaxial film. This allows obtaining qualitative and flat epitaxial films, regardless of the material of the substrate or epitaxial film and the diameter of the substrate or thickness of the epitaxial film, without any buffer layers or carrying out any additional operations and costs.

Research on the Positioning and Development Strategy of Archaeopteryx Brand in Chinese Market

As people focus more and more on health, outdoor activities are becoming more and more popular, and Arcteryx, as a leading brand in outdoor activities, has received a lot of attention recently, this paper adopts the literature review and case law method around Arcteryxs itself, and researches on Arcteryxs market strategy, technological innovation, market adaption, market competitiveness, etc., one by one, and this thesis has certain reference value for the positioning and strategy of Arcteryx as well as other outdoor brands in the Chinese market, as well as in brand marketing, product innovation, consumer relationship management, and digital application. This thesis has certain reference value for Arcteryx and other outdoor brands positioning and strategy in the Chinese market, as well as in brand marketing, product innovation, consumer relationship management and digital application. It also points out that the brand recognition and product line concentration in Arcteryxs high-end market are relatively low, and the future should further explore the regional market performance, changes in consumer behavior and the application of virtual technology.

The always changing data problem of using AI in manufacturing : Using synthetic data from the digital twin to feed AI models

Production is becoming increasingly flexible, which also requires the flexibility of the support system for the production. And the key here is the speed of decisions, in which the support of modern artificial intelligence systems can be crucial. Flexible production is based on a well-planned control of production, which increasingly uses some artificial intelligence component. Artificial intelligence can already be useful in the early stages of planning the production line, and of course it can also control the daily operation of the production line after the installation of the production place or line. The biggest problem is supplying the neural network that controls the artificial intelligence with training data. The production lines typically change every 2-4 months, new products appear, the layout changes, and the main process data also changes due to the development of the processes. This results in the training data becoming outdated or obsolete very quickly and thus cannot be used to train models anymore. High-quality learning data can be produced by digital twin models of production lines. Such synthetic data has several advantages over data collected from production. In this article, we investigate how useful this synthetic data is during the life cycle of the production line.

Recovery of mining and agri-food wastes in fired materials: a case study of the Moroccan industry.

The linear economy follows the "take-make-dispose" model and generates huge amounts of waste without consideration for recycling or reuse. This model which deals with raw materials puts pressure on natural resources and creates a serious environmental impact. In a circular economy, the "reduce-reuse-recycle" model is applied to recycle waste into resources and reduce the impact on the environment and society. This work aims to highlight the significance of implementing a circular economy approach in the construction sector by merging two different production lines, notably mining activity and agri-food industry. The investigation presents sustainable management of coal mine waste (CMW) and olive pomace (OP) in the production of eco-friendly fired materials and introduces an innovative approach for manufacturing lightweight fired bricks. Microstructural, physical, mechanical, and thermal properties were determined to evaluate the technological quality of fired materials at 900°C. As a pore-forming agent, adding 10 wt% OP yielded specimens with a bulk density of 1552kg/m3, water absorption of 19.80%, apparent porosity of 29.61%, loss on ignition of 26.98%, and compressive strength of 7.08MPa, satisfying standards for clay masonry units. Simultaneously, it enhances the thermal insulation by reducing thermal conductivity by 18% compared to the control sample with CMW. In this regard, the transition to a greener construction sector necessitates the immediate implementation of a circular economy approach to developing eco-friendly building materials by recovering large amounts of industrial waste, limiting the overuse of natural resources (e.g., clays), and improving the engineering properties of the final product.

LINI HULU PRODUKSI SAWIT INDONESIA: PERSPEKTIF RANTAI PASOK

Smallholder oil palm plantations are still facing the challenge of solidity in increasing their collective action to influence the supply chain of the national and global palm oil industry. This study aims to analyze the upstream system of smallholder palm oil production in building the sustainability of the supply chain of the national palm oil industry from a supply chain perspective. The data collection method combines an empirical study approach and literacy. An empirical study was carried out to search for institutional portraits of farmers. Furthermore, the search for commodity flows and palm oil agro-industry actors was carried out using a supply chain scheme framework. Based on the discussion, it is concluded that the disclosure of the flow of information related to the volume, price, and supply value differs according to the stakeholders involved. The supply chain of upstream palm oil production starts from the production line of oil palm plantations to the fabrication of CPO/PKO and the fabrication of their derivatives. Oil palm farmers are the weakest parties in accessing prices, volumes, and purchasing capacity information. Independent and plasma oil palm plantation smallholders are the most vulnerable to problems of uncertainty, transparency, transaction costs, and institutions, as well as the determination of an undisclosed price share between the cooperative as an intermediary and the core company. The downstream supply chain of palm oil agroindustry (olein, biodiesel, and chemical industry) is dominated by stakeholders affiliating with national private plantation companies within the framework of vertical supply synergies. CPO/ PKO palm derivative processed agroindustry, which has no vertical affiliation with national plantation companies on the upstream side, was the weakest actor in dealing with uncertainty in the supply of raw materials.

Теоретические исследования процесса смешивания кормов центробежным смесителем

The article presents theoretical studies aimed at understanding the physical processes occurring in the production of compound feeds using a centrifugal cascade mixer, which open up new horizons in optimizing feed production technology. Special attention in these studies is paid to the analysis of the mixing process, which, as it turned out, is closely interrelated with the principles of operation of the dispenser. As a result of the analysis, it was found that the dispenser and mixer should be considered as a single system, regardless of the mixing mechanism used in the production process. This made it possible to develop a cascade mixer of feed materials of centrifugal type and continuous action, which is an innovative solution in the field of feed production. This mixer provides a homogeneous mixture of loose feed components and can be integrated into production lines for the preparation of both feed and combined mineral briquettes. The key advantage of the proposed mixer is the use of a centrifugal force field, which, unlike the gravity field, provides more efficient mixing. In this case, the process of preparing compound feeds can be represented as a sequence of three key stages: the metered supply of components, the introduction of one component into the composition of another, transportation (removal) of the finished product from the working area of the mixer. The research allowed us to obtain analytical dependences of the components of the centrifugal force on the angular velocity of the rotor and its radius. This allows you to optimize the operating parameters of the mixer to achieve maximum mixing efficiency. During the research, a mathematical model of the spatial motion of a particle in a mixer was developed. The model is presented in the form of Lagrange equations of the second kind, the generalized coordinates of which are the displacement of the particle along the cone and the polar angle of the particle position. This makes it possible to solve the optimization problems of centrifugal mixers according to a given objective function. Thanks to the conducted research, it was possible to develop an innovative mixer that provides a homogeneous mixture of components of loose compound feeds. The use of centrifugal forces in the mixer made it possible to increase the mixing efficiency and ensure that the final product meets the requirements of the formulation. The research results are of great practical importance and can be used to improve feed production technologies. Keywords: COMPOUND FEED, FEED MIXTURE, MIXER, DISPENSER, CENTRIFUGAL MIXER, PARTICLE MOTION IN THE MIXER

Hardware Design Strategies Oriented to Edge Computing and AgriFood Electronics for Image Processing using Cellular Automata

There has been a growing interest from academia and industry in developing circuits and systems for edge computing and quality control tasks in food production lines, where image-processing is frequently required. This paper outlines the required considerations for designing a fruit classification system based on image-processing using Cellular Automata (CA) models and integrating it into reconfigurable hardware (HW) such as Field Programmable Gate Arrays (FPGAs). Parallel processing in CA requires numerous processing elements to be implemented and mapping CA models to HW generally comes with limitations. Homogeneous CA arrays are easier to design and implement in HW but can be resource-demanding. To fill this gap, this study explores different alternatives for the HW implementation of CA models, particularly trading computational-parallelism for a more optimized use of the available HW resources. We conducted experimental tests of the designed HW system using the Digilent Nexys development board, and the operation was validated against software-based benchmarks for image-processing, particularly concerning edge-detection. The presented study provides a broader range of design solutions for the HW implementation of two-dimensional CA models and a better understanding of their advantages and disadvantages. The results show that solutions focusing on instruction-parallelism add some complexity to the conception and require more design effort, compared to homogeneous CA models composed of identical cells. However, the instruction-parallel design solutions can significantly improve the HW resource utilization, especially when implementing computationally intensive CA rules in FPGAs.

Production and Characterization of Duckweed Bio-Oil via Pyrolysis: A Renewable Alternative to Fossil Fuels

Duckweed bio-oil has gained significant interest due to its exceptional benefits in raw material utilization and eco-friendliness. Research on microalgae cultivation and pyrolysis production lines is vital to enhancing the efficiency of duckweed biomass consumption for renewable energy. In this work, the pyrolysis process was applied to create bio-oil from duckweed. A fixed-bed reactor was connected to condensers, with heat delivered via a gas burner, and a thermocouple used to measure the reactor’s temperature. A stopwatch was used to track the elapsed time until the last drop of product was visible from the system. The results show that both duckweed oils are heavy oils, with an API gravity of 8.73, densities of 1.079 g/cm³ and 1.006 g/cm³, high viscosities of 8.24 mm²/s and 9.32 mm²/s, respectively, a specific gravity of 1.01, a high flash point of 96 °C, and a pour point of 16 °C. The research confirms that duckweed biomass can be pyrolyzed to produce bio-oil. GC/MS analysis was conducted on the generated bio-oil, revealing the presence of multiple polyaromatic hydrocarbons. Consequently, carbon chain elements (C8-C28) are present in duckweed bio-oils. According to the findings, duckweed-derived bio-oil shows great promise as a fossil fuel alternative. This study suggests that, since microalgae have the potential to be a viable replacement for fossil fuels, efforts should be made to scale up microalgae production from the laboratory to industrial levels.