Automatic Separation Research Articles

Algorithm for extracting the normal cross-section parameters of multiple ball screw shaft ball tracks based on an optical micrometer measurement system

Abstract The accurate and efficient measurement of the normal cross-section parameters of multiple ball screw shaft spiral ball tracks are pivotal for ensuring quality control in ball track machining. Given the intricate nature of the ball screw shaft spiral ball track, balancing the accuracy and efficiency of the normal cross-section parameters measurement is a significant challenge. In this study, we present a method to calculate two core parameters, arc radius and contact angle. The method consists of four parts: the automatic axial cross-section separation method, the arc symmetric extraction method, the spiral transformation method, and the parameter algorithm based on the weighted least squares method. The experimental and simulation results validated the effectiveness of our method. Compared with the traditional axial measurement and transformation (AMT) method, our algorithm reduced the errors in arc radius and contact angle by up to 13.9 µm and 4.77°, respectively, and improved the accuracy by up to 78.34% and 85.04%. Compared with the traditional AMT methods and directly normal measurement method, the measurement time of our algorithm was reduced by up to 1565 s and 3475 s, respectively, and the efficiency was improved by up to 71.01% and 84.51%, respectively.

Multi-category sorting of plastic waste using Swin Transformer: A vision-based approach

Sorting out plastic waste (PW) from municipal solid waste (MSW) by material type is crucial for reutilization and pollution reduction. However, current automatic separation methods are costly and inefficient, necessitating an advanced sorting process to ensure high feedstock purity. This study introduces a Swin Transformer-based model for effectively detecting PW in real-world MSW streams, leveraging both morphological and material properties. And, a dataset comprising 3560 optical images and infrared spectra data was created to support this task. This vision-based system can localize and classify PW into five categories: polypropylene (PP), polyethylene (PE), polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polystyrene (PS). Performance evaluations reveal an accuracy rate of 99.75% and a mean Average Precision (mAP50) exceeding 91%. Compared to popular convolutional neural network (CNN)-based models, this well-trained Swin Transformer-based model offers enhanced convenience and performance in five-category PW detection task, maintaining a mAP50 over 80% in the real-life deployment. The model's effectiveness is further supported by visualization of detection results on MSW streams and principal component analysis of classification scores. These results demonstrate the system's significant effectiveness in both lab-scale and real-life conditions, aligning with global regulations and strategies that promote innovative technologies for plastic recycling, thereby contributing to the development of a sustainable circular economy.

Anomaly Sign Detection for Automatic Ticket Gates by the Histogram Limitation Method

It is crucial to appropriately maintain automatic ticket gates (ATGs) to keep transportation operating smoothly in urban areas. Although the average failure rate of new ATGs is extremely low, continuous operation for many years might lead to unstable performance due to deterioration, and the need for periodic maintenance to avoid fatal faults might halt operations for extended periods. To detect anomalies at an early stage, “anomaly signs” can be utilized to flag ATGs for maintenance by service engineers before anomalies occur. In addition, to minimize the cost of ATG monitoring, the necessary computing resources should be minimized, which means using only light-weight statistical methods rather than deep learning or machine learning. In this paper, we focus on the automatic separation modules inside ATGs that separate multiple tickets by complicated mechatronic controls because this module is the major cause of maintenance calls from station attendants. We propose a simple anomaly sign detection, called the histogram limitation method (HLM). We evaluated the anomaly sign scores over time with maintenance timing and compared them with the conventional fast unsupervised anomaly detection method, Histogram-Based Outlier Score (HBOS) widely used in various domains. The experimental results using real field ATG monitoring data show that HLM successfully detected anomaly signs before a maintenance call was necessary, which is better performance compared with HBOS. Despite being a simple modification based on HBOS, HLM also provides anomaly sign scores that agree adequately with assessments by maintenance service engineers.

Low-Thrust Fuel-Optimal Guidance with Automatic Control Sequence Detection and Separation

This paper develops a novel fuel-optimal guidance scheme using differential algebraic techniques that is capable of handling changes in the optimal thrust sequence, in addition to a large domain of uncertainty. Taylor polynomials of the solution flow exhibit large approximation errors when the control sequence changes within the domain of operation. This property is leveraged through automatic domain splitting, a technique that tracks the approximation error and splits the domain into smaller subdomains when high errors are observed. After multiple splits, the domain is autonomously separated along the boundaries between different optimal control sequences. Crucially, each resulting subdomain encompasses only a single control sequence, ensuring that any guidance updates obtained from the final polynomial map provide the corresponding optimal control sequence. To ensure high accuracy of the guidance updates, this work also develops an efficient and iterative-less differential-algebra-based refinement algorithm, which is applied to the outputs of the polynomial map. The overall guidance scheme provides highly accurate guidance updates in response to large trajectory deviations, including new optimal control sequences, as demonstrated in an Earth–Psyche transfer.

Industrial optical sorting for marine plastic litter management

The aim of this study is to evaluate the possibility of incorporating marine litter from the coast into the management system of the non-selectively collected fraction of Municipal Solid Waste (MSW). With this purpose, the technical feasibility of automatic separation of marine plastic litter is evaluated, which would allow for subsequent recycling. This solution requires mechanical–biological-treatment plants (MBT) to be able to sort marine plastic litter. Poly (ethylene terephthalate) (PET) bottles constitute one of the most commonly reported types of marine plastics. Considering the sorting methods employed in MBT plants, optical sorting tests were performed with marine PET litter and compared with those for post-consumer municipal PET waste, in order to assess whether the current technology implemented in these plants would allow their automatic sorting. Results show that the separation efficiency of PET degraded under marine conditions and outdoors is very high, and close to that of post-consumer municipal PET waste. The separation efficiency by transparent colour for different types of PET materials evaluated is also very effective and similar to that obtained for separation by chemical composition. Therefore, the chemical differences observed would not affect the efficiency of automatic sorting of marine origin PET in a waste treatment plant.

Start-Up and Steady-State Regimes Automatic Separation in Induction Motors by Means of Short-Time Statistics

Induction motors are widely used machines in a variety of applications as primary components for generating rotary motion. This is mainly due to their high efficiency, robustness, and ease of control. Despite their high robustness, these machines can experience failures throughout their lifespan due to various mechanical, electrical, and environmental factors. To prevent irreversible failures and all the implications and costs associated with breakdowns, various methodologies have been developed over the years. Many of these methodologies have focused on analyzing various physical quantities, either during start-up transients or during steady-state operations. This involves the use of specific techniques depending on the focus of the methodology (start-up transients or steady-state) to obtain optimal results. In this regard, it is of great importance to develop methods capable of separating and detecting the start-up transient of the motor from the steady state. This will enable the development of automatic diagnostic methodologies focused on the specific operating state of the motor. This paper proposes a methodology for the automatic detection of start-up transients in induction motors by using magnetic stray flux signals and processing by means of statistical indicators in time-sliding windows, the calculation of variances with a proposed method, and obtaining optimal values for the design parameters by using a Particle Swarm Optimization (PSO). The results obtained demonstrate the effectiveness of the proposed method for the start-up and steady-state regimes automatic separation, which is validated on a 0.746 kW induction motor supplied by a variable frequency drive (VFD).

Dynamic region growing approach for leaf-wood separation of individual trees based on geometric features and growing patterns

ABSTRACT The separation of leaf and wood points remains challenging due to the diversity of tree species and structures. We propose an automatic leaf-wood separation method from tree point clouds, leveraging trees’ shape information, growing patterns, and topological information for higher separation accuracy. First, the tree point clouds are roughly classified into initial planar, linear, and scattered points based on the dimensional structure of each point. Then, a slicing strategy combined with the DBSCAN (density-based spatial clustering of application with noise) clustering algorithm is used to exploit the potential wood clusters in each layer. A novel dynamic region growing algorithm based on an energy function is proposed for extracting tree trunk and large branches from initial planar points, incorporating the geometric features and growing patterns of trees. Finally, a novel inner-to-outer region growing method is proposed to extract the tiny branches based on voxel density and the principal direction. Twelve tree samples covering twelve species and locating tropical, temperate, and boreal areas are used to evaluate the method’s performance. The mean separation accuracy and kappa coefficient of the proposed method are 0.942 and 0.805, respectively. Experimental results indicate that the proposed method exhibits a strong ability to distinguish the wood and leaf points for various trees with different species and structures.

L1-Tree: A novel algorithm for constructing 3D tree models and estimating branch architectural traits using terrestrial laser scanning data

Branch architecture provides crucial information for the understanding of plant trait variability and the adaptive strategies employed by trees in response to their environment. High-fidelity terrestrial laser scanning (TLS) data provide an accurate, efficient, and non-destructive means for constructing three-dimensional (3D) tree models and estimating architectural traits. However, the complex canopy structure of trees in natural forests and the presence of occlusion in TLS data pose significant challenges to achieving this goal. In this study, we present a novel algorithm, L1-Tree, for the construction of 3D tree models and the estimation of architectural traits from TLS data. This algorithm is grounded in the L1-Median algorithm and integrates a tree skeleton optimization procedure that considers the structural characteristics of tree branches. By comparing modeling results and manually derived branch traits for 24 trees of 24 species, we found that the L1-Tree algorithm achieved precision, recall, and F-score values of 0.94 for branch identification, coefficient of determination, root-mean-squared error, and normalized root-mean-squared error of 0.998, 0.068 m, and 0.3 % for branch length estimation, and a respective value of 0.958, 0.257 cm and 0.9 % for branch radius estimation. Additionally, the branch identification accuracy and accuracy in branch architectural trait estimation remained satisfactory across branch orders. Compared to established 3D tree model construction algorithms (e.g., TreeQSM), our L1-Tree algorithm demonstrated a superior capability in handling noisy environments and data gaps, making it a robust tool for TLS data-based tree architecture studies. Leaf-wood separation emerged as a crucial step influencing the performance of the L1-Tree algorithm. We observed significant drop in branch identification accuracy when using an automatic leaf-wood separation algorithm as input, highlighting the urgent need to develop effective leaf-wood separation algorithms to generate high-quality wood point clouds for tree architecture studies.

Sustainable investigation and process intensification on the separation of ternary organic wastewater with heterogeneous characteristics

The tetrahydrofuran (THF)/water/n-propyl acetate (PROAC) mixture, which is categorized into class 2.0-2b-C, exhibits pressure-sensitive property along with heterogeneous regions in its phase diagram. Conventional pressure-swing distillations with different separation sequences (PSD-PTW, PSD-PWT) and a novel three-column heterogeneous azeotropic pressure-swing distillation (T-HAPSD) are designed to complete the separation. The PSD section of T-HAPSD combines three columns into two columns. The HAD section of T-HAPSD is more energy-efficient owing to the automatic separation in the decanter. T-HAPSD is more economical than conventional pressure-swing distillation. Heat integration and Self-heat recuperation technology (SHRT) are then introduced to reduce energy consumption and costs. Finally, the five processes are evaluated from four aspects (economy, energy, environment and exergy). The best of five processes is T-HAPSD-SHRT with 37.83% reduction in TAC, 51.17% reduction in TEC, 48.83% reduction in gas emissions and 52.63% reduction in exergy destruction when compared with PSD-PTW.

Automated preparation of Actinium-225 with accelerator-based production from irradiated natural thorium targets

Towards the goal of accelerator-based 225Ac production from proton and nitrogen irradiated natural thorium targets, this work hereby presents a self-designed preparation system that allows for continuous operation with improved multi-column ion-exchange method. The system with advantageous features validated the feasibility of the automatic separation and purification of actinium from hundreds of impurity nuclides. Through automated experiments with multiple targets, the recovery yield and the radionuclide purity of final 225Ac products was stabilized to be around 63 % and 92 % ∼ 96 %, respectively. The total content of stable impurities is measured to be less than 2.4 μg in the final actinium product. The radioactivity levels of 226Ac and 227Ac in the product are evaluated over time. Based on the results of evaluation, the optimal time to start separation is determined to be around the 10th day after the end of bombardment. Additionally, the 225Ac-PSMA-617 compound was synthesized with the 225Ac product that meets the requirement of nuclear medicine.

Rainfall event separation and determination of the water quality volume

A procedure is presented and demonstrated for identifying the minimum interevent time for automatic separation of rainfall events and the subsequent use of the event statistics to determine the water quality volume. The proposed procedure for identifying the minimum interevent time is more robust that the conventional approach that requires interevent times to have an exponential distribution. The procedure is validated in South Florida and at locations in Athens (Georgia), Roanoke (Virginia), and St.Louis (Missouri). Minimum interevent times of 6h, 6h, 12h, and 9h, respectively, were found at the study locations, and it was demonstrated that the interevent times do not generally have an exponential distribution. Conventional determination of the water quality volume does not formally consider the influence of the drain time in sizing the water quality storage basin, and a procedure is presented herein for taking the drain time into account. The procedure is demonstrated at the study locations using multiple rainfall-runoff models. It is shown that a maximum drain time of 24h would not generally be adequate in South Florida, but would be adequate in Athens, Roanoke and St.Louis, however, a maximum drain time of 48h would not generally be adequate at all locations.

Automatic Tumor Cellularity Measurement: AI-Based Pipeline for Multi-Organ Pathology Imaging.

Tumor Cellularity (TC) is an important metric for assessing organ tumor burden. However, manual cell counting is not feasible due to large volumes of pathology images and inconsistent measurements between pathologists. The PAIP 2023 Challenge aimed to solve this problem using AI. The challenge presented two main obstacles: the need to evaluate pancreas-trained data for effective use in the colon, and the common miscounting of clustered cells in segmentation results. To address these, we proposed a novel pipeline. It included channel normalization, which standardizes RGB values to ensure consistent model performance across different organs. By introducing CacoX, a specialized model for accurate cell segmentation, we used Coordinate Attention Gates for accurate cell localization and non-local learning. Finally, the implementation of a watershed algorithm allowed the automatic separation of clustered cells. This approach secured 3rd place in the PAIP 2023 Challenge with an impressive ICC score of 95.69%.

Quality in beans: tracking and tracing coffee through automation and machine learning

PurposeCoffee is one of the most consumed beverages in the world and the global coffee industry is worth over $100bn. However, the industry faces significant sustainability challenges. Developing a quality traceability system to select the coffee beans and to ensure their authentication would result in economic advantages, because it allows for fraud to be avoided and increases consumer confidence.Design/methodology/approachTraceability is one of the key elements of sustainability in the coffee sector. The literature reveals that near-infrared (NIR) approaches have a huge potential for gaining rapid information about the origin and properties of coffee beans, without invasive procedures. This study demonstrates the scalability potential of automated methods of manipulation and image acquisition of coffee beans, from experimental scale to industrial lines.FindingsA solution based on the interaction of a manipulation system, a NIR spectrometer acquisition station integrated with a machine learning infrastructure and a compressed air classifier allows for the automatic separation of coffee beans into different classes of origin.Originality/valueApart from traceability, the wide industrialization of this system offers further advantages, including reduced workforce, decreased subjectivity in the evaluation and the acquisition of real-time data for labeling.

Investigation of simultaneous production of H2 and separable carbon from CH4 in a fluidised bed of NiO/Ca2Fe2O5/CaO particles

This work investigated the possibility of simultaneous production of hydrogen/syngas and separable solid carbon in a fluidised bed by using CH4-CO2 cycles and exploring the carbon growth over solid catalytic particles of NiO/Ca2Fe2O5/CaO. This two-stage process included i) stage I: H2-rich gas and carbon were simultaneously generated from the interaction between CH4 and NiO/Ca2Fe2O5/CaO particles; and ii) stage II: the reduced/carbon-deposited particles were regenerated in CO2. Following our previous successful demonstration of the process for stable hydrogen production over cycles, this study focused on the formation of solid carbon and aimed at understanding its growth mechanism as well as demonstrating the potential of using fluidisation as a means of automatic carbon separation. By tracking the carbon growth history and the change in phases of the reacted NiO/Ca2Fe2O5/CaO particles at temperatures from 700 to 900 °C, we discovered the formation of various forms of nano-structured carbon from CH4 conversion (e.g. partial oxidation, pyrolysis). These include carbon nanotubes (CNTs), carbon graphite sheets, carbon onions (CNOs), and carbon fibres. Amorphous carbon was also observed, particularly in the initial stage of CH4 conversion. Higher temperatures like 800 °C and 900 °C gave faster kinetics of CH4 conversion and more formation of solid carbon. Fe3C phase was observed on the reduced catalytic particles and could act as an intermediate phase or carbon sink for carbon growth. The results suggested that other mechanisms for carbon deposition, such as directly over Fe sites, may also exist. Fluidisation with a higher U/Umf ratio separated more carbon-rich fines from the bulk catalytic particles in the fluidised bed. Fluidising with air at 700 °C effectively removed amorphous carbon on the reacted catalytic particles, whilst structured carbon remained. Full separation and purification to obtain pure carbon require furthers optimisation of e.g. fluidisation parameters and material design.

Mapping off-road tracks and animal paths in protected areas using high-resolution GeoEye-1 panchromatic satellite imagery

ABSTRACT Off-road driving activities are common in many protected areas. This study aimed at applying a curvelet-based approach for the automatic extraction, mapping and separation of off-road tracks and animal paths in Masai Mara National Reserve, Kenya, using high-resolution GeoEye-1 panchromatic satellite imagery (50 cm). A novel hybrid remote sensing-GIS method comprising three main blocks is proposed: (1) extracting the high-contrast curvilinear feature from curvelet magnitudes derived from the finer scale of curvelet coefficient; (2) extracting the low-contrast curvilinear feature from coarser scale curvelets and refine the shape by deformable active contour (Snake), and (3) categorizing the extracted curvilinear feature into vehicle tracks and animal paths by a fuzzy logic inference system. Results from quantification of extraction and categorization performance of the trails were 77.5% for completeness, 89.2% for correctness, and 4.5% for redundancy, with an overall accuracy of 79.5%. Using grid matching, we find a high correlation between off-road vehicle tracks and animal paths in the area (r = 0.75, p < 0.05) indicating co-occurrence of these two types of trails. The proposed approach provides a basis for large-scale mapping and monitoring of off-road tracks and animal paths in the African savanna from space.

Self-separating core-shell spheres with a carboxymethyl chitosan/acrylic acid/Fe3O4 composite core for soil Cd removal

Cadmium (Cd) removal from soil is crucial as Cd enters the food chain and affect food safety, thus impose severe threaten to human health. We developed PPC@PC-Fe, a dual-functional core-shell sphere for efficient soil Cd reduction. The shell (PPC) was composed of encapsulated citric acid (CA) in a polylactic acid (PLA) and polyethylene glycol (PEG) network, which endows a function of activating Cd; and the core (PC-Fe) consisted of a polyacrylic acid/carboxymethyl chitosan (PAA/CMC) hydrogel with Fe3O4 nanoparticles to adsorb adjacent activated Cd. Upon water contact, the shell dissolved, releasing CA to activate soil Cd. Simultaneously, the swellable PC-Fe core absorbed water and expanded in size, promoting the disintegration of PLA in the shell, which triggered the automatic separation of core from shell, enabling the exposed PC-Fe core to rapidly adsorb Cd. Furthermore, the PC-Fe core can be magnetically removed after adsorption of Cd. Soil culture tests showed that 2 % PPC@PC-Fe reduced soil Cd from 6.009 mg/kg to 4.834 mg/kg in 10 days, with the acid-soluble Cd being the predominantly target to be activated and remove. This study demonstrates an effective stepwise activation and adsorption mechanism by a single carrier, with simple magnetic collection minimizing secondary pollution. It offers an innovative approach to the remediation of cadmium-contaminated sites in the field.

Development and verification of a novel bone collector with automatic size separating function for orthopedics surgery

ABSTRACT Background Autologous bone dust can be filled in bone defects to promote effective bone healing but typically it is lost when using suction during surgery. The aim of this study was to develop a novel bone collector that can be used to collect bone chips/dust of varying sizes without changing current surgical procedures. Research design and methods This collector was designed to connect to a surgical continuous suction system and comprised a plate filter with a 3 mm hole and featured a taper filter with a mesh size of 0.27 mm for the separation and collection of both coarse and fine bone chips/dust. The bone collector was manufactured using nylon 3D printing and plastic injection with biocompatible materials. Results The bone collector functional test revealed high bone chip collection efficiency (93%) with automatic size separation function. Low (3.42%) filtration errors showed that most of the water can be drained smoothly from the bone collector. In clinical usability testing, bone collectors can provide functions demonstrated in in vivo spinal fusion and femoral fracture surgeries with different bone grafting size requirements. Conclusions The novel bone collector has been validated as a viable and effective surgical device, offering surgeons an additional option to enhance patient outcomes.

Efficient capture of ReO4−/TcO4− on anion exchange resin from wastewater

It is still a challenge to capture TcO4− from wastewater due to its nature of non-complexing and radioactive. Herein, two types of anion exchange resins (D201 and IRA-410 resin) were selected and explored the adsorption properties for perrhenate (ReO4−, as a surrogate for TcO4−). It is found that both D201 and IRA-410 resin exhibited fast adsorption kinetic (within 120 min), and superior adsorption capacities, which could reach 671.1 mg g−1 and 561.8 mg g−1 for D201 and IRA-410 resin, respectively. In addition, D201 and IRA-410 resin showed excellent adsorption selectivity for ReO4− in the presence of a variety of 1000-fold competing anions (NO2−, Cl−, SO42−, and PO43−). Importantly, the two resins still demonstrated outstanding performance after acid/alkaline soaking, calcination procedures as well as high doses of ionizing radiation, they also could remove ReO4− from simulated Hanford and SRS wastewater by automatic separation system. Finally, the anion-exchange mechanism was revealed by characterization and DFT. This work demonstrates that D201 and IRA-410 resin are efficient adsorbents for 99Tc removal from nuclear wastewater and provides a basis for designing other novel anion exchange materials for capturing ReO4− and TcO4−.

Efficiency assessment of a novel automatic mosquito pupae sex separation system in support of area-wide male-based release strategies

This study provides a comparative analysis of two state-of-the-art automatic mosquito pupae sex sorters currently available: the ORINNO and the WOLBAKI Biotech pupae sex separation systems, which both exploit the sexual size dimorphism of pupae. In Aedes aegypti, the WOLBAKI sex sorter and the ORINNO with a sieve mesh size of 1.050 mm achieved sex separation with female contamination rates below 1%, low pupae mortality rates and high male flight capacity. However, in Ae. albopictus, there was more variability, with female contamination rates above the 1% threshold and pupae mortality reaching 27% when using the ORINNO sorter. On the other hand, the WOLBAKI sorter achieved a male pupae recovery of 47.99 ± 8.81% and 50.91 ± 11.77% in Ae. aegypti and Ae. albopictus, respectively, while the ORINNO sorter with a smaller sieve size achieved male pupae recoveries of 38.08 ± 9.69% and 40.16 ± 2.73% in Ae. aegypti and Ae. albopictus, respectively. This study provides valuable information for researchers and practitioners in the field, assisting in the selection of the most suitable system for mosquito control, management and research programs depending on their specific requirements.

Unveiling the innovation: Optimized biodiesel production from emerging Acer truncatum Bunge seed oil using novel and highly effective alkaline ionic liquid catalyst

Herein, three novel alkaline ionic liquids (ILs) with nitrogen-containing heterocycles were prepared via a two-step method, which were then utilized as excellent catalysts for biodiesel synthesis through transesterification of emerging Acer truncatum Bunge seed oil (ATBSO) and methanol. The alkaline ILs exhibited favorable catalytic performance, with 1-(2,3-dihydroxy)-propyl-1,5,7-triazabicyclo[4.4.0]dec-5-ene) hydroxide ([DPTD][OH]) exhibiting the highest catalytic efficiency among them. Furthermore, the effect of the independent variables and their interactions on biodiesel yield were assessed and optimized using response surface methodology (RSM). Under the optimal parameters, which consisted of a reaction temperature of 71 °C, a reaction time of 137 min, a substrate molar ratio of methanol to ATBSO of 12:1, and a catalyst loading of 1.3 wt%, the biodiesel yield reached a maximum of 97.3 %. The catalytic transesterification of ATBSO for the preparation of biodiesel exhibited an activation energy (Ea), enthalpy (ΔH), entropy (ΔS) and the Gibbs free energy (ΔG) of 35.14 kJ·mol−1, 32.37 kJ·mol−1, −178.80 J·mol−1·K−1 and 93.70 kJ·mol−1 at 343 K, respectively. It is worth noting that, after the reaction, an automatic phase separation between biodiesel and the catalyst occurred, minimizing the processes of product isolation and purification. Additionally, [DPTD][OH] exhibited favorable reusability over the four reaction cycles, providing a sustainable and environmentally friendly approach for biodiesel production. The biodiesel obtained from ATBSO demonstrated excellent physicochemical properties, such as good cold flow property and flash point, which conform to the biodiesel standards outlined in ASTM D6751 and EN 14212.