Fill Level Research Articles

Waste Segregator: Waste Management Prototype

Smart Waste Dustbins utilize IoT, AI, and computer vision for efficient waste management. They feature multiple compartments, sensors, and automatic lid mechanisms for real-time waste sorting. Computer vision and AI algorithms accurately identify and segregate waste. Smart Waste Dustbins monitor fill levels and offer a user-friendly interface for public engagement. Sensors include ultrasonic, moisture, and metal sensors for proximity, moisture analysis, and metal item segregation. Smart Waste Dustbins revolutionize waste management, enhancing efficiency in segregation, recycling, and collection processes, leading to reduced environmental impact and improved resource utilization for sustainable communities.

Optimizing Object Detection in Bio-Waste Management with Improved Hyperbolic Tangent YOLOv5 and Binarization-Adapted K-Means Algorithm

<p style="margin-bottom:11px;text-align:justify;"><span style="font-family:Calibri,sans-serif;font-size:11pt;line-height:107%;">With the increasing importance of green development, bio-waste classification has become an important element of green development. However, in garbage stacking scenarios, the task of segmenting highly overlapping objects is difficult because the bottom garbage is in an occluded state and it is usually difficult to distinguish its contours from occluded boundaries. This paper presents a rotationally invariant system for solid bio-waste container detection and bio-waste level classification. This paper proposes a rotationally invariant system for detecting solid bio-waste containers and classifying their fill levels. It employs Hough line detection to identify container orientations and positions, complemented by cross-correlation for distinguishing similar objects. Features extracted from within and outside the container area are utilized, enabling precise determination of discard levels. The system accommodates varying bio-waste amounts through transformation and rotation of container images. Object detection utilizes an Improved Hyperbolic Tangent-based YOLOv5 model with a Binarization-Adapted K-Means Algorithm (BKA) to enhance sorting accuracy. Additionally, the method efficiently calculates object motion through segmented BKA-detected objects. Experimental validation with a bio-waste image dataset demonstrates superior performance in accuracy, specificity, sensitivity, and overall precision compared to existing methodologies.</span></p>

Exploring pharmaceutical powder behavior in commercial-scale bin blending: A DEM simulation study

Bin blending is one of the main steps in pharmaceutical production processes. Commercial-scale production of expensive products typically does not allow to perform a large number of experiments in order to optimize the process. Alternatively, Discrete Element Method (DEM) simulations can be used to evaluate the powder behavior (flow and blending pattern) during blending, identify the risks (e.g., segregation), and provide solutions to mitigate them. In this work, DEM simulations are used to investigate the blending of two granulated powders in commercial-scale cone and cylindrical (hoop) blenders. The DEM contact model parameters were calibrated based on the experimental compression and ring shear tests for both granulated powders to mimic the bulk powder behavior in the simulations. The model's output was compared to the experiments in one of the blending cases. The blending efficiency in the cone blenders was evaluated considering the fill levels, the presence of baffles, the rotating directions, the filling order, and the bin sizes. Furthermore, for the hoop blenders, the effects of blender's angle, rotation speed, and filling order were addressed. The main findings of the work were that, in cone blenders, the blending can be improved by introducing baffles and changing in the rotational direction frequently. In hoop blenders, blending can be improved by increasing the inclination angle from the horizontal plane and the rotational speed.

Dynamic Behavior of Rectangular Tanks with Limited Freeboard Under Seismic Loads: Experimental, Analytical and Machine Learning Investigations

Abstract One of the most common types of fluid storage tanks are rectangular concrete tanks placed on the ground. Ensuring the seismic performance of these tanks is crucial as, besides damage and losses during a crisis, they can lead to other crises such as fires or toxic material leaks. One pivotal factor influencing the seismic behavior of these tanks is the height of the freeboard. A comprehensive understanding of how freeboard height impacts the impulsive and convective masses is essential for the effective design and dynamic analysis of rectangular tanks. While numerous numerical and experimental studies have explored this field, the influence of various filling levels, fluid properties, and tank geometries under seismic loading conditions requires further investigation. In this study, both an experimental model and a theoretical model were subjected to different tank fill levels, tank geometries, and various freeboard heights under the influence of different earthquake records. The results showed that tank height and geometry significantly affect the changes in impulsive and convective masses. Notably, in contrast to previous studies, our results demonstrate a nonlinear relationship between freeboard height and the variations in these masses. These new findings are crucial for refining current design practices and improving the resilience of rectangular tanks in earthquake-prone regions.

Quantitative Assessment of Green Inventory Management in Supply Chains: Simulation-Based Study of Economic and Environmental Outcomes Aligned with ISO 14083 Standard

This research employs numerical simulations and scenario analysis to assess a supply chain model’s economic and environmental performance operating under stochastic market demand, with inventory levels managed by a periodic review (R, s, S) inventory system. The inventory model in this research is designed to determine the minimal inventory levels required to achieve predefined fill rates across various operational constraints. The supply chain’s inventory model simulates optimal responses to normally distributed market demand within 365-day periods characterized by mean and two levels of demand variability through two fill rate levels, two workweek schedules, 15 review periods, and 16 lead times. By conducting an extensive analysis of the 192000 simulation experiments of the supply chain under periodic review (R, s, S) inventory system, complex influences between system variables and economic outcomes of supply chain operation measured by ordering, transportation, holding, penalty, and total costs along with greenhouse gas emissions arising from inventory-related transportation according to the ISO 14083 standard are analyzed. The insights from this research have significant practical implications, providing valuable guidance for supply chain managers, researchers, and freight companies offering guidance for improving economic and environmental performance.

Small-scale BLEVE: Near-field aerial shock overpressure and impulse

This paper presents an investigation into one of the most damaging hazards associated with Boiling Liquid Expanding Vapor Explosion (BLEVE), specifically focusing on the near-field overpressure and impulse effects. Experiments were conducted at a small-scale to study the overpressure and impulse using aluminum tubes with a diameter of 50 mm and a length of 300 mm. The tubes were filled with pure propane liquid and vapor. The controlled variables, in this work included the failure pressure, the liquid fill level, and the weakened length along the tube top. These variables control the strength of the overpressure characterized by the peak overpressure amplitude, duration of this overpressure event, and the resultant impulse. Notably, these experiments at a small scale included experiments with a 100 % liquid fill level. This further confirmed that the vapor space is the main driver of the lead overpressure hazard. High-speed cameras and blast gauges effectively illustrated the progressive formation of the shock wave in both temporal and spatial dimensions. Furthermore, various predictive models available in the literature are discussed in this paper and new correlations were developed to quantify the overpressure duration and impulse. The current analysis aims to predict the potential consequences of overpressure events during a BLEVE.

IoT-Enabled Street Dustbin Monitoring System Based on Machine Learning

Abstract In this article, a novel method is proposed by using the power of the Internet of Things (IoT) and Machine Learning (ML) to monitor and manage street dustbins effectively. The proposed innovative solution aimed at optimizing waste management processes and improper disposing of the waste in street dustbins. The system involves the installation of sensors in dustbins, which collect real-time data about the fill level of the bins. The capability of the proposed model allows for dynamic adjustment of waste collection schedules, reducing unnecessary trips and thus saving time, energy, and resources. In addition to that, the model is also trained in various waste disposal scenario such as improper disposal, massive garbage dumps, and e-waste. The proposed model is able to predict and report the trained conditions with the 100% accuracy. The system is implemented using Raspberry Pi 4, a single-board computer with the necessary hardware interface. A suitable web interface has been developed to oversee the status of dustbins and provide optimum travel route for collection of waste from the street dustbins. The proposed system not only enhances operational efficiency but also contributes to environmental sustainability.

An experimental setup for studying conduction and radiation heat transfer in a rotary drum

This study presents the development of a novel heating system that aims to bridge the experimental gap for analyzing both radiation and conduction heat transfer in a rotary drum. The experiments were conducted by loading the drum with 4 mm silica glass beads at varying fill levels (10 %, 17.5 %, and 25 %) and rotation rates (2, 5, and 10 RPM), and monitoring the temperature evolution with an infrared camera. Higher fill levels resulted in lower average particle bed temperatures, while rotation rate influenced the contact time between particles and the drum wall and the exposure to radiative heat. A rotation rate of 5 RPM resulted in the highest average bed temperature due to optimal contact and exposure time for conduction and absorption of radiative heat, respectively. Thus, a balance between adequate particle mixing and optimal contact/exposure time is crucial for maximizing heat transfer efficiency in rotary drums exhibiting conduction and radiation.

Demonstrating scalability between two blender types using DEM

Powder blending is a critical step in pharmaceutical manufacturing that can impact product quality such as tablet tensile strength. This study utilized the Discrete Element Method (DEM) to investigate blending in a 5-liter mini-batch and a 2-liter Turbula blender. DEM parameters were calibrated using small-scale powder characterization tests, so that the particle behavior in the DEM simulations matches the measured behavior. The research explored the effects of blender designs and process conditions on blending and lubricant dispersion. A predictive model for tablet tensile strength was developed. The model takes the lubricant’s dispersion via the lubrication energy into account. The model is then used to predict the tablet tensile strength depending on the chosen process parameters, blending speed, duration, and fill level. DEM simulations enabled scaling between the two blenders, providing valuable insights for a semi-continuous manufacturing process based on mini-batch blending. The findings contribute to a deeper understanding of blending mechanics, offering potential enhancements in pharmaceutical manufacturing efficiency and product consistency.

Spatial impulse response analysis and ensemble learning for efficient precision level sensing

In this paper, we propose an innovative method for determining the fill level of containers, such as trash cans, addressing a critical aspect of waste management. The method combines spatial impulse response analysis with machine learning (ML) techniques, offering a unique and effective approach for sound-based classification that can be extended to various domains beyond waste management. By employing a buzzer-generated sine sweep signal, we create a distinctive signature specific to the fill level of the waste container. This signature, once accurately decoded, is then interpreted by a specially developed ensemble learning algorithm. Our approach achieves a classification accuracy of over 90% when implemented locally on a development board, optimizing operational efficiencies and eliminating the need to delegate complex classification tasks to external entities. Using low-cost and energy-efficient hardware components, our method offers a cost-effective approach that contributes to sustainable and efficient waste management practices, providing a reliable and locally deployable solution.

Characterizing fluid delivery of targeted application devices for weed control

AbstractPrecision weed management in turfgrass and ornamental landscape systems is utilized to offset chemical costs, reduce nontarget plant injury, and control potential herbicide‐resistant weed escapes. Targeted application devices (TADs), sold as wands, dabbers, daubers, sponges, and so forth, are regularly marketed for use in turfgrass and ornamental landscape systems, but TAD use has been limited to nonselective herbicides due to limited information regarding their calibration and use. Five commonly utilized TADs were evaluated to test for variability in fluid output within a given device and between evaluated devices. Fluid output for all evaluated devices ranged 3.2–6.9 kL ha−1 and were higher than spray outputs typical of broadcast applications. High levels of variability were detected within and among specific devices. Variance of output from devices with large fluid reservoirs was driven by reservoir fill level, with more fluid output decreasing linearly as reservoir fill level decreased. Additionally, when airlock was not maintained during application, fluid output was ∼178% greater than when airlock was maintained. Average force, maximum force, contact time, and fluid output following 25 applications were measured from five TAD users. Contact time was highly correlated (R2 = 0.93) to fluid output, with fluid output increasing as contact time increased. Subsequent economic analysis indicated the maximum percentage weed coverage ha−1 at which TAD use is economically equivalent or less than broadcast application is 3%, 8.1%, 4.3%, and 0.1% with iron HEDTA (hydroxyethlethydiaminetriacetate), acetic acid, methiozolin, and glyphosate, respectively. These data indicate the primary sources of TAD output variability are reservoir fluid fill level, reservoir airlock, and duration of application.

Analysis of the behavior of a gas-generating system under runaway conditions in a closed vessel

In the event of a runaway reaction, emergency relief systems (ERS) act as the last line of defense against the vessel explosion. To date, ERS design for scenarios involving the runaway of chemical mixtures that generate gaseous reaction products remains very challenging as it requires the prediction of the gas generation rate under runaway conditions at large scale. Current methodologies for the assessment of the gas generation rate are based on pseudo-adiabatic calorimetric experiments at laboratory scale in which the temperature and pressure profiles resulting from the runaway are used to assess the gas generation rate using the ideal gas law. The gas generation rate needs to be further corrected for the thermal inertia (φ-factor), of the calorimetric cell used (φ>1) to predict large scale behavior (φ≈1). The work described in this paper focuses on evaluating the capability of this approach to accurately assess the gas generation rate. It uses a dynamic simulator tool based on rigorous thermodynamics to calculate the temperature, pressure, and the composition of each component in a closed vessel containing a gas generating reactive mixture (decomposition of 20 % w/w di-tert butyl peroxide in toluene) under runaway conditions over a range of initial vessel fill levels. The results were analyzed to develop a better understanding of the phenomena that govern the thermal behavior of the mixture, the overall pressurization of the vessel, rate of generation of the gaseous products and its distribution in the liquid and vapor phase of the vessel as the runaway reaction occurs. The simulated pressure, temperature and phase composition data rigorously calculated by the simulator were used to assess and highlight the limitations of using the temperature and pressure in a closed vessel and the ideal gas law to evaluate the gas generation rate. The simulations were also used to evaluate the validity of φ-factor correction methods currently available in the literature.

Optimization of Methodologies to Study Freeze/Thaw Processes in Drug Substance Bottles.

Biological drug substance (DS) is often frozen to enhance storage stability, prolong shelf life, and increase flexibility during manufacturing. However, the freezing and thawing (F/T) of bulk DS at the manufacturing scale can impact product quality as a result of various critical conditions, including cryo-concentration during freezing, which are influenced, among other things, by product-independent process parameters (e.g., container type, fill level, F/T equipment, and protocols). In this article, we report the optimization of two major methodologies to study product-independent process parameters in DS bottles at the manufacturing scale, namely the recording of temperature profiles and liquid sampling after thawing to quantify the concentration gradients in the solution. We report experimentally justified measuring positions for temperature recordings, especially for the selection of the last point to freeze position, and highlight the implementation of camera-assisted inspection to determine the last point to thaw and the actual thawing time. In particular, we provide, for the first time, a detailed description of the technical implementation of these two measuring set-ups. Based on the reported case studies, we recommend choosing relevant measuring positions as a result of initial equipment characterization, resulting in a resource-conscious study set-up.

Heat transfer of cohesive particles in a rotary drum: Effect of material properties and processing conditions

This study examines the effect of cohesive particle properties and rotary drum operating conditions on heat transfer. Simulations were performed using the Discrete Element Method and Johnson–Kendall–Roberts contact model. Key variables analyzed include material properties (surface energy, particle size, thermal conductivity), fill level, and rotational speed. Angle of repose (AOR) tests were simulated for varying surface energies to establish a correlation between cohesion and bulk properties. Results showed that the AOR was proportional to the particle Bond number and maintaining a constant Bond number across different particle sizes produced similar heating patterns. This enables practical mapping of properties for future modeling and experimentation. Cohesion between particles was found to linearly decrease the rate of heat transfer, whereas adhesion between particles and the drum walls linearly increased it, leading to temperature non-uniformity among particles. Additionally, baffles enhanced heat transfer under cohesive conditions, although their effectiveness decreased with increased adhesion.

An Improved Method to Calculate Gas-Lift Valve Set Pressure

Summary This study aims to enhance the accuracy of gas-lift valve (GLV) design set pressure calculations by incorporating the effects of silicone thermal expansion, silicone compression, and dome thermal expansion. The proposed method is compared with experimental measurements and current industry methods. The analysis includes an experimental assessment of the impact of internal dome volume changes on the design set pressure, considering various silicone fill levels, pressures, and temperatures. Experimental conditions cover silicone fills of 25%, 50%, and 75%, initial pressures from 975 psig to 1,250 psig, and temperatures from 60°F to 175°F. These conditions cover the conventional operating range in the industry. The results show that silicone expansion is the most dominant factor among silicone compression and chamber thermal expansion. The proposed method significantly improves GLV design set pressure calculations, especially for high-pressure and high-temperature conditions. The study suggests improvements in injection-pressure-operated (IPO) valve design practices to avoid multipoint injection and unloading failures. To the best of the authors’ knowledge, the impact of silicone expansion on GLV set pressure has not been investigated in the open literature. The enhanced method proposed in this research could significantly impact well unloading and production operations for wells equipped with GLVs. In addition, this work suggests an improvement on the American Petroleum Institute (API) recommended practice, especially when dealing with high-pressure, high-temperature wells and when utilizing refurbished GLVs.

Real-time visual intelligence for defect detection in pharmaceutical packaging

Defect detection in pharmaceutical blister packages is the most challenging task to get an accurate result in detecting defects that arise in tablets while manufacturing. Conventional defect detection methods include human intervention to check the quality of tablets within the blister packages, which is inefficient, time-consuming, and increases labor costs. To mitigate this issue, the YOLO family is primarily used in many industries for real-time defect detection in continuous production. To enhance the feature extraction capability and reduce the computational overhead in a real-time environment, the CBS-YOLOv8 is proposed by enhancing the YOLOv8 model. In the proposed CBS-YOLOv8, coordinate attention is introduced to improve the feature extraction capability by capturing the spatial and cross-channel information and also maintaining the long-range dependencies. The BiFPN (weighted bi-directional feature pyramid network) is also introduced in YOLOv8 to enhance the feature fusion at each convolution layer to avoid more precise information loss. The model's efficiency is enhanced through the implementation of SimSPPF (simple spatial pyramid pooling fast), which reduces computational demands and model complexity, resulting in improved speed. A custom dataset containing defective tablet images is used to train the proposed model. The performance of the CBS-YOLOv8 model is then evaluated by comparing it with various other models. Experimental results on the custom dataset reveal that the CBS-YOLOv8 model achieves a mAP of 97.4% and an inference speed of 79.25 FPS, outperforming other models. The proposed model is also evaluated on SESOVERA-ST saline bottle fill level monitoring dataset achieved the mAP50 of 99.3%. This demonstrates that CBS-YOLOv8 provides an optimized inspection process, enabling prompt detection and correction of defects, thus bolstering quality assurance practices in manufacturing settings.

Smartbin Track

This project presents a sophisticated waste management system that combines IoT technology with an Android application to optimize garbage collection processes. The core infrastructure includes IoT-enabled smart trash cans equipped with sensors to detect fill levels autonomously. Complementing this hardware, the Android app serves as a comprehensive platform for managing various aspects of waste collection operations. Divided into modules for administration, employees, and truck drivers, the app offers functionalities such as real-time monitoring of trash levels, route optimization for collection trucks, and automated alerts for full bins. Through seamless integration of IoT capabilities and mobile technology, the system facilitates efficient and environmentally responsible waste management practices, contributing to a cleaner and more sustainable urban environment. Furthermore, the system enhances coordination and transparency within the municipal waste management framework. Municipal administrators gain valuable insights into waste collection efficiency through the app's dashboard. Employees can input data and perform tasks seamlessly, while truck drivers receive real-time updates on bin fill levels and optimized routes, improving overall operational efficiency. By harnessing IoT technology and mobile platforms, this innovative solution not only streamlines waste collection processes.

Towards the prediction of barrel fill level in twin-screw wet granulation

The barrel fill level is defined as the fraction of the free available volume for a given screw configuration that is occupied by the wet material and is an interplay of the material throughput, screw speed, screw setup, barrel length of the twin-screw granulator used and the properties of the starting material. The fill level has a major impact on mixing and densification of the wetted mass and thus on the granules produced. It influences the twin-screw granulation process accordingly.In the current study, a model has been developed which is predictive in terms of material hold-ups in the barrel at various process settings by considering the geometries of the different screw elements in a configuration and the conveying velocity of the wet mass through the barrel. The model was checked on two granulators of different dimensions with various screw configurations, different materials and at different process settings.The model represents a step forward in predicting the barrel fill level but further research with a broader spectrum of materials, screw configurations and process settings is still needed and additional twin-screw granulators of other dimensions must be investigated.

Loads-based optimal fuel-usage strategy by using a neural-network-based reduced-order model for vertical sloshing

This paper explores methods to reduce aircraft design loads through an optimal fuel usage strategy, which maximises the beneficial effects of wing-tank sloshing-induced damping. A reduced-order neural network models the nonlinear sloshing behaviour in various tank fill level scenarios. Integrated into an aeroelastic framework, this model allows for the assessment of incremental sloshing-induced damping in gust response of wings. The optimised fuel usage strategy enables control of fuel consumption from each individual tank to provide maximisation of load alleviation.

Discrete Element Method Simulation of Vibratory Filling of Convoluted Hot Isostatic Pressing Canisters Using Idaho Calcine Waste Simulant

This research presents a discrete element method (DEM) model for simulating the vibratory filling of the Idaho calcine waste simulant into various convoluted hot isostatic pressing canisters. The simulation closely emulates the experimental vibratory powder-filling processes, achieving accurate representations of surface profiles and powder bed heights. Notably, the model underestimates lower fill levels but demonstrates improved accuracy at higher levels due to diminished air influence. Executed on a consumer-grade desktop PC, the DEM model replicates tapped powder bed heights to within millimeters, proving its capability to efficiently simulate commercial-scale bulk material handling processes using standard computing hardware.