Conveyor Belt Research Articles

Detection of walnut internal quality via improved YOLOv5 and x‐ray imaging

AbstractThis study proposes a walnut target detection algorithm based on improved YOLOv5 and x‐ray imaging to meet the demand for internal quality detection and removal in the Xinjiang walnut industry. By replacing the C3 module in the backbone layer with the C2f module and the couple‐head in the head layer with the decouple‐head, the algorithm reduces computational complexity, enhances robustness and generalizability, and retains more spatial information, thereby improving the performance of multicategory small target detection. In addition, this paper replaces the original CIOU loss function with the EIOU loss function to improve the convergence speed of the algorithm's accuracy and boundary aspect ratio. Compared with the original model, the improved model, improved YOLOv5, maintains the same average precision for normal walnuts while increasing the average precision for shriveled walnuts and empty‐shell walnuts by 8.2% and 0.4%, respectively. Compared with other mainstream models, such as VGG16, ResNet50, YOLOv5s, YOLOv7, YOLOv8s, YOLOv9s, and YOLOv10s, this model achieves the highest detection accuracy and good detection performance, with a single‐image detection time of 11.9 ms, meeting the requirements for real‐time detection. This work lays a foundation for automatic robot detection of the internal quality and removal of walnuts, showing practical application potential.Practical applicationsXinjiang stands as a prominent producer of walnuts; however, due to the decentralized nature of its cultivation and management processes, a notable prevalence of internal empty shells and shrinkage is observed, which substantially diminishes their commercial value. The integration of YOLOv5 with x‐ray imaging technology promises to enhance the precision of internal quality assessments of walnuts. The improved YOLOv5 model, as delineated in this study, exhibits the highest detection accuracy when benchmarked against a multitude of other models. It achieves a detection latency of merely 11.9 ms per image, thereby satisfying the stringent demands for real‐time detection applications. This model is designed for integration into x‐ray systems with an adjunctive sorting mechanism, which facilitates the inspection and exclusion of defective walnuts on conveyor belts.

A robotic fish processing line enhanced by machine learning

This paper presents the design of a comprehensive automatic fish processing line utilizing machine learning algorithms. The processing line encompasses several essential steps, including fish identification by type, fish sorting by size, fish orientation based on shape, and fish cutting at the optimal chopping points. The primary objective of this design is not just automation but also maximizing economic benefits by preserving the maximum amount of fish meat during the cutting process, achieved through the application of machine learning algorithms. To accomplish these goals, we employ a combination of transfer learning and convolutional neural networks to establish criteria for actions across all stages of automatic fish processing. At the heart of the processing station is a conveyor belt equipped with numerous sensors and lenses. Positioned along this conveyor belt are two robotic arms, responsible for precise positioning and cutting operations, all guided by the machine learning algorithms. To provide a visual representation of these design concepts, we have created a 3D SolidWorks model.

Implementing a Vision-Based ROS Package for Reliable Part Localization and Displacement from Conveyor Belts

The use of computer vision in the industry has become fundamental, playing an essential role in areas such as quality control and inspection, object recognition/tracking, and automation. Despite this constant growth, robotic cell systems employing computer vision encounter significant challenges, such as a lack of flexibility to adapt to different tasks or types of objects, necessitating extensive adjustments each time a change is required. This highlights the importance of developing a system that can be easily reused and reconfigured to address these challenges. This paper introduces a versatile and adaptable framework that exploits Computer Vision and the Robot Operating System (ROS) to facilitate pick-and-place operations within robotic cells, offering a comprehensive solution for handling and sorting random-flow objects on conveyor belts. Designed to be easily configured and reconfigured, it accommodates ROS-compatible robotic arms and 3D vision systems, ensuring adaptability to different technological requirements and reducing deployment costs. Experimental results demonstrate the framework’s high precision and accuracy in manipulating and sorting tested objects. Thus, this framework enhances the efficiency and flexibility of industrial robotic systems, making object manipulation more adaptable for unpredictable manufacturing environments.

Modeling the stability of air flows in inclined workings in case of fire

Purpose. Development of a mathematical model and study of the processes occurring during conveyor belt fires in inclined mines to determine the mechanism of air flow distribution under appropriate conditions. Methods. Practical measurements of the distribution of air flows in an inclined conveyor operated at the mining and processing plant of PJSC ArcelorMittal Kryvyi Rih were carried out. Based on the obtained data, a mathematical model was crea-ted, which was then used to perform Computational Fluid Dynamics (CFD) modeling of gas flows in the mine under normal conditions and in the event of a fire with a thermal capacity of 9 MW. Findings. The study reveals the peculiarities of gas flows during the combustion of a conveyor belt in inclined workings, as well as the influence of turbulence on the flow, changes in the density of gases during heating, and the impact of gravity on the distribution of gases in the cross-section and along the length of the workings. A fire centre with a heat output of 9 MW has a significant impact on the distribution of air flows. The phenomenon of thermal expansion results in a 59.2% increase in the volume of gases behind the fire. The thermal expansion of gases and their low density have a significant impact on the formation of gas flows in inclined workings. As the jet moves away from the centre of the fire, the velocity of the jet gradually increases, reaching a value of 12.4 m/s. This results in a notable alteration in the distribution of total pressure across adjacent workings, accompanied by an increase in flow turbulence. Consequently, the mass of air exiting the right branch is observed to have a five-fold increase in comparison to the pre-fire state. The overturning of the jet from the left branch of the workings gives rise to the exclusive distribution of combustion products along the right branch. Originality. The study helps to understand the mechanism of jet overturning and the peculiarities of the distribution of combustion products in case of fire in inclined conveyor workings. Practical implications. The results of the research allow us to predict the probability of fire, identify the most dangerous areas for fire, and plan the most rational actions for firefighting in specific unique conditions.

Stair Climbing Electric Wheelchair

From 18 century many types of wheelchairs had been designed, by developing its functionality. Our project focuses on addressing the mobility challenges faced by individuals with disabilities, particularly those who encounter difficulties navigating staircases. The project introduces a novel solution in the form of an electric stair- climbing wheelchair designed to provide enhanced accessibility in both indoor and outdoor environments. The wheelchair incorporates innovative technologies, including electric motors and intelligent control systems, to enable seamless and safe navigation on various types of stairs. The important parts of this product are conveyor belt, frame and driving mechanism climbing wheelchair for regular use by old disabled people. The design of frame will be done by considering various loads, stresses at various positions. The main factor of wheelchair are the angle of stair and center of gravity of whole system. The design process involves considerations for user comfort, safety, and ease of operation, with a particular emphasis on accommodating diverse user needs. Through a combination of both electrical and mechanical engineering principles and modern electronics, the wheelchair is equipped to ascend and descend stairs smoothly, offering a versatile solution for individuals with mobility impairments. We also plan to explore user feedback and ergonomic evaluations to refine the wheelchair's design and functionality. Preliminary testing would be done to demonstrates the feasibility and effectiveness of the proposed solution in real-world scenarios. The findings of this study contribute to the ongoing efforts to improve the quality of life for individuals with disabilities by expanding their mobility options and promoting inclusivity in various environments.

What can we learn from the experiment of electrostatic conveyor belt for excitons?

Motivated by the experiment of electrostatic conveyor belt for indirect excitons (Winbowet al2011Phys. Rev. Lett.106196806), we studied the exciton patterns for understanding the exciton dynamics. By analyzing the exciton diffusion, we found that the patterns mainly came from the photoluminescence of two kinds of excitons. The patterns near the laser spot came from the hot excitons which can be regarded as the classical particles. However, the patterns far from the laser spot come from the cooled or coherent excitons. Considering the finite lifetime of Bosonic excitons and of the interactions between them, we built a time-dependent nonlinear Schrödinger equation including the non-Hermitian dissipation to describe the coherent exciton dynamics. The real-time and imaginary-time evolutions were used alternately to solve the Schrödinger equation to simulate the exciton diffusion accompanied by the exciton cooling in the moving lattices. By calculating the escape probability, we obtained the transport distances of the coherent excitons in the conveyor, consistent with the experimental data. The cooling speed of excitons was found to be important in coherent exciton transport. Moreover, the plateau in the average transport distance cannot be explained by the dynamical localization-delocalization transition induced by the disorders.

Non-contact measurement of conveyor belt speed based on fast point cloud registration of feature block

Abstract Non-contact, real-time measurement of conveyor belt speed is critical for energy-saving speed regulation and efficient development of coal mine conveyor systems. Existing speed measurement technologies for conveyor systems are often limited by the slippage and wear in contact measurement and complex environmental disturbance. This study introduces three-dimensional point cloud technology into coal flow information detection and innovatively presents a non-contact measurement method of conveyor belt speed based on fast point cloud registration of feature blocks. In the proposed method, a three-dimensional camera is used to capture point cloud data of the dynamically running conveyor belt, and the raw point cloud is preprocessed and segmented into blocks. Then, the C-MANV (curvature and mean angle of normal vectors) features of block point clouds are constructed based on the point cloud neighborhood curvature and the mean angle of neighborhood normal vectors. Finally, an improved block point cloud registration method based on C-MANV features is adopted to achieve the accurate measurement of the dynamic running speed of conveyor belt. Experimental results demonstrate that the proposed method achieves an average relative error of less than 1.8% in high-speed conveyor belt operation with an average processing time of less than 35 ms, which fulfils the accuracy and real-time requirements for conveyor belt speed detection in coal mines. This study provides an effective technical solution for the speed monitoring of coal mine conveyor systems.

A membrane-associated conveyor belt controls the rotational direction of the bacterial type 9 secretion system.

Many bacteria utilize the type 9 secretion system (T9SS) for gliding motility, surface colonization, and pathogenesis. This dual-function motor supports both gliding motility and protein secretion, where rotation of the T9SS plays a central role. Fueled by the energy of the stored proton motive force and transmitted through the torque of membrane-anchored stator units, the rotary T9SS propels an adhesin-coated conveyor belt along the bacterial outer membrane like a molecular snowmobile, thereby enabling gliding motion. However, the mechanisms controlling the rotational direction and gliding motility of T9SS remain elusive. Shedding light on this mechanism, we find that in the gliding bacterium Flavobacterium johnsoniae , deletion of the C-terminus of a conveyor belt protein controls, and in fact, reverses the rotational direction of T9SS from counterclockwise (CCW) to clockwise (CW). Largescale conformational changes at the interface of the T9SS ring with the C-terminus of the conveyer belt, as well as those of the ring protein themselves, in concert with a CW bias of the stators general rotation brings forth a 'tri-component gearset' model: the conveyor belt controls the conformation of the T9SS ring, and thereby its rotational direction. Consequently, the CW rotating stator either push the outer edge of the T9SS rings, causing its CCW rotation or press against the inner surface of the rings, resulting in CW rotation. This regulatory mechanism exemplifies how an outer membrane associated conveyor belt adjusts the rotational direction of its driver, the T9SS, thus providing adaptive sensory feedback to control the motility of a molecular snowmobile.

Development and integration of a continuous horizontal belt filter into drug production procedure

In the pharmaceutical industry, filtration is traditionally carried out in batch mode. However, with the spread of continuous technologies, there is an increasing demand for robust continuous filtration strategies suitable for processing suspensions produced in continuous crystallizers. Accordingly, this study aimed to investigate a lab-scale horizontal conveyor belt filtration approach for pharmaceutical separation purposes for the first time. The newly developed continuous horizontal belt filter (CHBF) was tested under different systems (microcrystalline cellulose (MCC)/water, lactose/ethanol and acetylsalicylic acid (ASA)/water) and diverse conditions. Filtration was robust using a well-defined unimodal particle size distribution MCC in water system, where the residual moisture content varied within narrow limits of 45–52% independently from the process conditions. Besides, the residual moisture content highly depended on the applied solvent and particle size. It could be reduced to below 2% by processing the suspensions of either a volatile solvent (lactose in ethanol) or an aqueous slurry of a large particle size ASA. Finally, the CHBF was connected to a mixed suspension mixed product removal (MSMPR) or a plug flow crystallizer (PFC). The residual moisture content of the CHBF-filtered ASA product and operation characteristics (onset of steady-state) were evaluated in both continuous crystallizer-filter systems. The MSMPR-CHBF system operated with a longer startup period. The size of the in situ-produced crystals was of a similar order magnitude in both systems, resulting in a similar residual moisture content (around 20%). Overall, the tested continuous filter was robust, did not modify the crystal morphology in the examined experimental range, and could be effectively integrated with continuous crystallizers.

Tunneling Mechanisms of Quinones in Photosynthetic Reaction Center–Light Harvesting 1 Supercomplexes

In photosynthesis, light energy is absorbed and transferred to the reaction center, ultimately leading to the reduction of quinone molecules through the electron transfer chain. The oxidation and reduction of quinones generate an electrochemical potential difference used for adenosine triphosphate synthesis. The trafficking of quinone/quinol molecules between electron transport components has been a long‐standing question. Here, an atomic‐level investigation into the molecular mechanism of quinol dissociation in the photosynthetic reaction center–light‐harvesting complex 1 (RC–LH1) supercomplexes from Rhodopseudomonas palustris, using classical molecular dynamics (MD) simulations combined with self‐random acceleration MD‐MD simulations and umbrella sampling methods, is conducted. Results reveal a significant increase in the mobility of quinone molecules upon reduction within RC–LH1, which is accompanied by conformational modifications in the local protein environment. Quinol molecules have a tendency to escape from RC–LH1 in a tail‐first mode, exhibiting channel selectivity, with distinct preferred dissociation pathways in the closed and open LH1 rings. Furthermore, comparative analysis of free energy profiles indicates that alternations in the protein environment accelerate the dissociation of quinol molecules through the open LH1 ring. In particular, aromatic amino acids form π‐stacking interactions with the quinol headgroup, resembling the key components in a conveyor belt system. This study provides insights into the molecular mechanisms that govern quinone/quinol exchange in bacterial photosynthesis and lays the framework for tuning electron flow and energy conversion to improve metabolic performance.

Enhancing the efficiency of Brown 24 pigment production through continuous microwave heating in conveyor belt and rotary kiln systems: A design and optimization study

In energy intensive industries, the continuous production with microwave technology presents several challenges in achieving high energy efficiency and heating uniformity. In the ceramic pigments sector, the Brown 24 is the ideal candidate for this study due to its susceptibility to thermal runaways and its high temperature to reach total conversion, higher than 1000K. By another hand, most literature related to this field focus on static systems while ignoring the continuous ones which are required by the industry. A coupled model that integrates thermal, electromagnetic and chemical phenomena within an energy system was implemented in COMSOL Multiphysics. Additionally, a MATLAB controller was employed to dynamically adjust the cavity length through a moving plunger, which maximizes the electrical efficiency. The required power is also managed to guarantee a total chemical conversion of the material. The proposed optimization methodology reduces computational costs, and it is applicable to any continuous microwave system processing moving solid materials. In this work, two microwave configurations were optimized. The first one, based on a conveyor belt, achieved a global efficiency close to 70%. While the second one, based on a rotary kiln, achieved a global efficiency of 85% and a production rate of 4.66kg/h, significantly outperforming a previous study by factors of 1.57 and 2.06, respectively. These findings show the potential for substantial improvements in continuous microwave systems.

双行轮式自走型大白菜收获机设计与试验

Aiming at the problems of low harvesting level of Chinese cabbage, high cost of manual operation and lack of special harvesting machinery, a double-row wheeled self-propelled Chinese cabbage harvester was designed on the basis of measuring the physical parameters of 'Zaofeng 01'Chinese cabbage varieties and combining with the main local planting patterns. It can complete the root cutting, pulling, clamping and conveying, packing and collecting of double-row Chinese cabbage at one time. Through the design and selection of the key working parts of the double-row wheel self-propelled Chinese cabbage harvester prototype, the key working parameters of the profiling device, the root cutting-pulling device and the clamping conveying device were determined. The kinematics analysis of the harvester in the profiling, cutting, pulling, clamping and conveying links was carried out, and the field operation performance test of the prototype was completed. The results showed that: when the rotation speed of the cutter was 280 r/min, the rotation speed of the clamping conveyor belt was 240 r/min, and the forward speed of the machine was 1.44 km/h, the maximum qualified rate of harvesting was 95.08 %, the minimum value was 90.65 %, and the average value was 93.79 %. At this time, the working performance of the working machine is stable, the harvesting effect is good, and all performance indicators meet the relevant design requirements and standards. The research results can provide reference for the development and structural improvement of Chinese cabbage low-loss harvesting equipment.

Surface one-step modification of graphene oxide with N-cyclohexylbenzothiazole-2-sulfonamide to enhance the wear resistance of natural rubber/butadiene rubber composites

Natural rubber (NR) and butadiene rubber (BR) composites are frequently used in manufacturing tires and conveyor belts, which are susceptible to severe wear, leading to product failure and significant losses. To enhance the wear resistance of NR/BR composites, we prepared NR/BR composites reinforced with a synergy of N-cyclohexylbenzothiazole-2-sulfonamide surface one-step modified reduced graphene oxide (CZ-rGO) and carbon black N234 (CB) using a latex co-precipitation method. This study explored the effects of GO content and CZ modification on the structure, wear resistance, thermal conductivity, and mechanical properties of the NR/BR blended composites. It showed that adding 2 phr of GO resulted in excellent filler dispersion, significantly improving the tensile strength, elongation at break, tear strength, thermal conductivity, and wear resistance of the composites. The incorporation of CZ-rGO further increased the crosslinking density, vulcanization performance, and filler dispersion with enhanced mechanical properties, thermal conductivity, and wear resistance of the composites. Notably, the DIN abrasion volume was reduced to 77.88 mm³, surpassing the ISO 10247 standard for conveyor belt cover rubber in terms of wear resistance. The wear resistance of NR/BR/CZ-rGO composites is not only significantly higher than that of single NR- or SBR-based rubber composites, but also significantly higher than that of other binary rubber blend composites, and the CZ-rGO filler is simpler in composition and structure and exhibits a more prominent wear enhancement. This work provides guidance for an efficient and environmentally friendly approach to GO surface modification, as well as useful insights for the development of high wear-resistant rubber composites with excellent overall performance.

An Integrated Approach: A Hybrid Machine Learning Model for the Classification of Unscheduled Stoppages in a Mining Crushing Line Employing Principal Component Analysis and Artificial Neural Networks.

This article implements a hybrid Machine Learning (ML) model to classify stoppage events in a copper-crushing equipment, more specifically, a conveyor belt. The model combines Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) with Principal Component Analysis (PCA) to identify the type of stoppage event when they occur in an industrial sector that is significant for the Chilean economy. This research addresses the critical need to optimise maintenance management in the mining industry, highlighting the technological relevance and motivation for using advanced ML techniques. This study focusses on combining and implementing three ML models trained with historical data composed of information from various sensors, real and virtual, as well from maintenance reports that report operational conditions and equipment failure characteristics. The main objective of this study is to improve the efficiency when identifying the nature of a stoppage serving as a basis for the subsequent development of a reliable failure prediction system. The results indicate that this approach significantly increases information reliability, addressing the persistent challenges in data management within the maintenance area. With a classification accuracy of 96.2% and a recall of 96.3%, the model validates and automates the classification of stoppage events, significantly reducing dependency on interdepartmental interactions. This advancement eliminates the need for reliance on external databases, which have previously been prone to errors, missing critical data, or containing outdated information. By implementing this methodology, a robust and reliable foundation is established for developing a failure prediction model, fostering both efficiency and reliability in the maintenance process. The application of ML in this context produces demonstrably positive outcomes in the classification of stoppage events, underscoring its significant impact on industry operations.

Characteristics and control of impact vibration and noise in rail belt conveyor

The rail belt conveyor is an innovative energy-saving belt conveyor. Revolutionizing the traditional roller support, it utilizes interval-spaced carriages to support the conveyor belt. The study of impact vibrations in rail belt conveyors remains underexplored in current research. This paper investigates the impact vibration and noise characteristics of the wheel–rail-steel structure coupling system, as well as their control and optimization strategies. First, vibration and noise measurements were conducted along the track. Results indicate that impact vibrations and noise originating from expansion joints and inserted joints fall within the frequency range of 1.5 kHz–5 kHz. Particularly noteworthy is the pronounced impact noise near inserted joints, with dominant frequencies ranging from 1.5 kHz to 2.5 kHz. Further investigation has revealed that the impacts on inserted joints encompass interactions between the wheel and rail, as well as between different sections of the rail itself. Next, the characteristics of filler materials such as polyurethane rubber, silicone, polyurethane resin, and epoxy resin were analyzed and compared. The experimental results show that filling rubber in expansion joints can reduce lateral impact vibrations by 61% and vertical impact vibrations by 66%. Additionally, the sound pressure level was reduced by approximately 1.7 dBA. Finally, welding the guiding rail to its preceding rail and grinding the welded surface effectively eliminated the composite impact vibrations and sharp noise at the inserted joint, resulting in a decrease of approximately 3 dBA in the sound pressure level. This paper provides guidance on the structural improvement and acoustic optimization of rail belt conveyors.

Optimalisasi Penggunaan Conveyor Belt Dalam Menunjang Pelayanan Di Bandar Udara Internasional Syamsudin Noor Banjarmasin

This study examines the optimization of conveyor belt usage in supporting passenger services at Syamsudin Noor International Airport, Banjarmasin. Conveyor belt is one of the important facilities in handling luggage that has a crucial role in accelerating the process of handling goods and reducing passenger waiting time. The less than optimal use of conveyor belts at Syamsudin Noor International Airport causes less than optimal passenger service, especially causing a buildup of passengers in the arrival terminal area. Therefore, the author conducted this research with the aim of finding out the causes of non-optimal use of conveyor belts at Syamsudin Noor Airport and the solutions that must be implemented to overcome them.This research was conducted from January 31 to February 10 2024 at Syamsudin Noor International Airport, Banjarmasin. This research uses qualitative research methods in the form of primary and secondary data. Primary data in this research was obtained from interviews conducted with 1 technical officer and 2 Terminal Service Officers. Meanwhile, secondary data in this research was obtained from literature reviews and journals related to this research. Qualitative data analysis techniques used include Data Reduction, Triangulation, and Drawing Conclusions. The research results show that optimizing and increasing conveyor belt capacity as well as implementing automatic monitoring and maintenance technology can reduce the risk of operational disruption and increase passenger satisfaction. This research also identifies solutions for terminal service area officers and mechanics to improve conveyor belt conditions that are not optimal, where the solutions suggested by the author include replacing rollers and pulleys, cleaning and lubricating components, and applying strengthening techniques.

Enhancing Conveyor Belt Performance: Evaluating the Impact of In-creased Capacity Using Belt Analyst Software

This study investigates the effects of increasing conveyor belt capacity from 148.5 tons per hour (t/h) to 180 t/h on the overall system performance, employing both manual measurements and simulations using Belt Analyst software. The research aims to evaluate critical parameters such as effective pulling force, motor power requirements, structural load, and belt deflection, which are essential for determining the feasibility and impact of such an upgrade. The analysis reveals that with the capacity increase, the effective pulling force required rises to 14,072 N, while the motor power usage escalates to 15 kW. Concurrently, the structural load experiences a significant increase from 46.144 kg/m to 56.238 kg/m, and belt deflection intensifies from 22 mm to 27 mm. These findings suggest that increasing the conveyor belt capacity to 180 t/h, may lead to increased stress on the structure and belt, which could potentially affect the lifespan and performance of the conveyor system. Furthermore, while the conveyor system's performance enhances at the higher capacity, it also places additional stress on the system's components. The study further examines the implications of these changes, emphasizing the potential risks to the conveyor belt’s structural integrity and the possible reduction in its lifespan due to the increased mechanical stress. It is highlighted that careful consideration and precise engineering adjustments are necessary when planning capacity enhancements to avoid adverse effects on the system's longevity and reliability.

In-line semantic segmentation of kimchi cabbage deterioration using YOLOv8n and DeepLabv3+

To ensure the safety of kimchi cabbage (KC), the effective identification of deterioration is required. In this study, we aimed to develop and validate a semantic segmentation pipeline to detect and segment the mild and severe deterioration of KC on moving industrial conveyor belts. The pipeline integrates the YOLOv8n and DeepLabv3+ models, image processing and semantic labelling models, respectively. The YOLOv8n model was employed for initial detection, achieving an F1 score (a measure of precision and recall) of 0.988 and an AP50 (average precision at 50 % intersection over union (IoU) with the ground truth) of 0.994. For detailed segmentation, the DeepLabv3+ model with Resnet101 and Mobilenetv3 backbones was used, and the performance of these models was evaluated based on F1 scores. The YOLOv8n model demonstrated high accuracy in detection. Further, the DeepLabv3+ model with the Resnet101 backbone achieved F1 scores of 0.772 and 0.851, and IoUs of 0.628 and 0.741 for mild and severe deterioration, respectively. Notably, the DeepLabv3+ model with Mobilenetv3 backbone excelled in identifying severe deterioration, indicating its potential for generating rapid alerts in edge devices. Crucially, the developed methodology enables the real-time, in-line monitoring of KC deterioration and is adaptable for future edge device applications. The results of this study highlight the potential of accessible technologies in advancing agricultural quality control, setting a benchmark for future research to expand data sources and enhance detection capabilities.

Applications of Artificial Intelligence for Smart Conveyor Belt Monitoring Systems: A Comprehensive Review

Applications of Artificial Intelligence for Smart Conveyor Belt Monitoring Systems: A Comprehensive Review

太阳能-热泵联合干燥条件下紫花苜蓿动态干燥特性

To promote the application of solar energy-heat pump combined drying technology in forage processing and enhance the drying efficiency of alfalfa, an experimental study was conducted. The research utilized a solar energy-heat pump drying system and a mesh belt dynamic drying device to investigate the drying characteristics of alfalfa. Drying characteristic curves were obtained, and the drying model and parameters were determined through model comparison. The study also analysed the impact of factors such as hot air velocity, temperature, alfalfa stacking thickness, turning angle of the spinner rack, and conveyor belt speed on the drying characteristic curves and parameters of alfalfa. A predictive model for alfalfa moisture content was developed, and the effective moisture diffusivity and activation energy were calculated. The findings revealed that alfalfa does not exhibit a constant speed drying stage, and its drying primarily occurs during the deceleration drying stage. The Logarithmic model was found to accurately describe the moisture change pattern during the dynamic drying process of alfalfa, with a model fitting coefficient R^2 exceeding 0.994, with an effective moisture diffusivity ranging from 2.776×10-10 m2/s to 4.7324×10-9 m2/s, and an activation energy of 37.02 kJ/mol. The study suggests that increasing the hot air velocity and temperature, reducing the conveyor belt speed, and adjusting the alfalfa stacking thickness can further enhance the drying rate and reduce the drying time.