Field Operations Research Articles

Determination of Temperature‐ and Carrier‐Dependent Surface Recombination in Silicon

Knowledge regarding the temperature dependence of the surface recombination at the interface between silicon and various dielectrics is critically important as it 1) provides fundamental information regarding the interfaces and 2) improves the modeling of solar cell performance under actual operating conditions. Herein, the temperature‐ and carrier‐dependent surface recombination at the silicon–oxide/silicon and aluminum–oxide/silicon interfaces in the temperature range of 25−90 °C using an advanced technique is investigated. This method enables to control the surface carrier population from heavy accumulation to heavy inversion via an external bias voltage, allowing for the decoupling of the bulk and surface contributions to the effective lifetime. Thus, it offers a simple and versatile manner to separate the chemical passivation from the charge‐assisted population control at the silicon/dielectric interface. A model is established to obtain the temperature dependence of the capture cross sections, a critical capability for the optimization of the dielectric layers and the investigation of the fundamental properties of the passivation under field operating conditions.

Sensitivity analysis of frequency response functions with imaginary parts decoupling based on multicomplex-step perturbation

Imaginary perturbation is used in the complex step differentiation method to compute first-order derivatives, widely known as an effective approach for sensitivity analysis in structural dynamics. However, coupling of imaginary parts occurs in the damped frequency response functions when employing this method. To mitigate this coupling, a novel approach for sensitivity analysis based on multicomplex-step perturbation is proposed in this paper, for sensitivity analysis of Frequency Response Functions in structural dynamics. The structural parameters are perturbed in multicomplex domain, the dimensions of structural matrices are expanded using the Cauchy Riemann matrix representation, the equation of motion for sensitivity analysis in frequency domain is transformed to matrix operation in field of real numbers, imaginary term will not exist in the equation of motion for sensitivity analysis, the imaginary part of the frequency response function and the imaginary part of the perturbation are decoupled, the structural frequency response functions and the corresponding sensitivities are obtained from the dimension-expanded equation of motion. A truss structure and a solar wing are adopted to verify the accuracy of the proposed method. Results show that the sensitivity of FRFs can be effectively calculated using the proposed method. Compare to the finite difference method, the proposed method is not depended on the step-size selection procedure. The multi-order and mixed-order sensitivity matrices, especially Hessian matrix can also be obtained using the proposed method.

From Do-It-Yourself Design to Discovery: A Comprehensive Approach to Hyperspectral Imaging from Drones

This paper presents an innovative, holistic, and comprehensive approach to drone-based imaging spectroscopy based on a small, cost-effective, and lightweight Unmanned Aerial Vehicle (UAV) payload intended for remote sensing applications. The payload comprises a push-broom imaging spectrometer built in-house with readily available Commercial Off-The-Shelf (COTS) components. This approach encompasses the entire process related to drone-based imaging spectroscopy, ranging from payload design, field operation, and data processing to the extraction of scientific data products from the collected data. This work focuses on generating directly georeferenced imaging spectroscopy datacubes using a Do-It-Yourself (DIY) imaging spectrometer, which is based on COTS components and freely available software and methods. The goal is to generate a remote sensing reflectance datacube that is suitable for retrieving chlorophyll-A (Chl-A) distributions as well as other properties of the ocean spectra. Direct georeferencing accuracy is determined by comparing landmarks in the directly georeferenced datacube to their true location. The quality of the remote sensing reflectance datacube is investigated by comparing the Chl-A distribution on various days with in situ measurements and satellite data products.

Unlocking the potential of unlabeled data: Self-supervised machine learning for battery aging diagnosis with real-world field data

Accurate aging diagnosis is crucial for the health and safety management of lithium-ion batteries in electric vehicles. Despite significant advancements achieved by data-driven methods, diagnosis accuracy remains constrained by the high costs of check-up tests and the scarcity of labeled data. This paper presents a framework utilizing self-supervised machine learning to harness the potential of unlabeled data for diagnosing battery aging in electric vehicles during field operations. We validate our method using battery degradation datasets collected over more than two years from twenty real-world electric vehicles. Our analysis comprehensively addresses cell inconsistencies, physical interpretations, and charging uncertainties in real-world applications. This is achieved through self-supervised feature extraction using random short charging sequences in the main peak of incremental capacity curves. By leveraging inexpensive unlabeled data in a self-supervised approach, our method demonstrates improvements in average root mean square errors of 74.54% and 60.50% in the best and worst cases, respectively, compared to the supervised benchmark. This work underscores the potential of employing low-cost unlabeled data with self-supervised machine learning for effective battery health and safety management in real-world scenarios.

A Framework for Multi-Physics Modeling, Design Optimization and Uncertainty Quantification of Fast-Spectrum Liquid-Fueled Molten-Salt Reactors

The analysis of liquid-fueled molten-salt reactors (LFMSRs) during steady state, operational transients and accident scenarios requires addressing unique reactor multi-physics challenges with coupling between thermal hydraulics, neutronics, inventory control and species distribution phenomena. This work utilizes the General Nuclear Field Operation and Manipulation (GeN-Foam) code to perform coupled thermal-hydraulics and neutronics calculations of an LFMSR design. A framework is proposed as part of this study to perform modeling, design optimization, and uncertainty quantification. The framework aims to establish a protocol for the studies and analyses of LFMSR which can later be expanded to other advanced reactor concepts too. The Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) statistical analysis tool was successfully coupled with GeN-Foam to perform uncertainty quantification studies. The uncertainties were propagated through the input design parameters, and the output uncertainties were characterized using statistical analysis and Spearman rank correlation coefficients. Three analyses are performed (namely, scalar, functional, and three-dimensional analyses) to understand the impact of input uncertainty propagation on temperature and velocity predictions. Preliminary three-dimensional reactor analysis showed that the thermal expansion coefficient, heat transfer coefficient, and specific heat of the fuel salt are the crucial input parameters that influence the temperature and velocity predictions inside the LFMSR system.

Evaluating the Effects of Proppant Flowback on Fracture Conductivity in Tight Reservoirs: A Combined Analytical Modeling and Simulation Study

This work establishes an analytical model for determining the critical velocity for proppant flowback, and evaluates how proppant flowback affects fracture conductivity for tight reservoirs. The multiphase effects are considered for determining the critical velocity for proppant flowback before and after fracture closure, respectively. The model’s performance is demonstrated by comparing the results against previous models. A finite-element model is built to simulate the proppant flowback process for a hydraulic-fractured well completed in the Ordos Basin. The change in fracture conductivity caused by proppant flowback for several scenarios with varying saturation and net pressure in fractures is further quantitatively assessed. Our results highlight the importance of multiphase effects in determining the critical velocity for proppant flowback at relatively low water saturation in fractures. The critical velocity generally increases with increasing water saturation in fractures and net pressure in fractures. At a flowback velocity higher than the critical value, the loss in fracture conductivity becomes relatively more pronounced at a lower water saturation in fractures and a lower net pressure in fractures. The findings of this work are expected to provide insights into the mechanisms of proppant flowback and flowback drawdown management for field operations in tight reservoirs.

Semi-automatic line-system provisioning with an integrated physical-parameter-aware methodology: field verification and operational feasibility

We propose methods and an architecture to conduct measurements and optimize newly installed optical fiber line systems semi-automatically using integrated physics-aware technologies in a data center interconnection (DCI) transmission scenario. We demonstrate, for the first time to our knowledge, digital longitudinal monitoring (DLM) and optical line system (OLS) physical parameter calibration working together in real-time to extract physical link parameters for fast optical fiber line systems provisioning. Our methodology has the following advantages over traditional design: a minimized footprint at user sites, accurate estimation of the necessary optical network characteristics via complementary telemetry technologies, and the capability to conduct all operation work remotely. The last feature is crucial, as it enables remote operation to implement network design settings for immediate response to quality of transmission (QoT) degradation and reversion in the case of unforeseen problems. We successfully performed semi-automatic line system provisioning over field fiber network facilities at Duke University, Durham, North Carolina. The tasks of parameter retrieval, equipment setting optimization, and system setup/provisioning were completed within 1 h. The field operation was supervised by on-duty personnel who could access the system remotely from different time zones. By comparing Q-factor estimates calculated from the extracted link parameters with measured results from 400G transceivers, we confirmed that our methodology has a reduction in the QoT prediction errors ( ±0.3dB ) over existing designs ( ±0.6dB ).

Effects of water chemistry on corrosion and plugging in the copper heat exchangers of water-cooled generators in power plants

Corrosion and plugging of hollow copper conductors in water-cooled generators can cause system failures, such as flow restrictions and forced unit outages. Although both neutral water chemistry (NWC) and slightly alkaline water chemistry (SAWC) have been proposed as solutions, field operations show that NWC frequently suffers from system failures, while SAWC demonstrates high reliability. The scientific basis for this phenomenon has not been well understood. In this paper, the thermodynamics of NWC and SAWC were established to compare their tendencies toward copper corrosion and plugging. SAWC keeps copper in a stable CuO passivation region, exhibiting a superior buffer capacity against CO2 inleakage and the temperature rise of the cooling water. This buffer capacity is further enhanced by increasing the degree of the slight-alkalization. Under the SAWC with a pH value of 9.0 at 298.15 K, the concentration of the dissolved copper species is extremely low, and the positive temperature coefficient of CuO solubility prevents the accumulation of copper corrosion products in high-temperature regions. In contrast, NWC is susceptible to corrosion and plugging due to its poor buffer capacity. This study provides new insights into the effects of water chemistry on copper corrosion and plugging from a thermodynamic perspective, offering a sound theoretical basis for field operations.

Configuration design methodof hybrid electric agricultural machinery system based on layered two-dimensional matrix

The working environment and objects of agricultural machinery are different from those of automobiles, andagricultural machinery is greatly affected by the working environment and working conditions, and the power output systemismore complex. Agricultural machinery not only has drive output, but also PTO output and hydraulic output, whichtogetherconstitute the output system of agricultural machinery. Agricultural machinery conditions can be dividedintoroadtransportation working conditions and field operation working conditions. The working conditions of agricultural machinerycan be divided into different load conditions according to the different traction tools and whether the hydraulic andPTOwork,such as ploughing, rotary tillage, fertilization, and transportation. Therefore, developing hybrid electric agricultural machinerysystems that are suitable for various complex working conditions holds great theoretical significance and practical value. Giventhe complex working conditions of agricultural machinery systems in agricultural work and the intricate challengesindesigning hybrid agricultural machinery systems. In this paper, the two-dimensional matrix is used to represent thephysicalstructure and dynamics of the multi-channel power output agricultural mechanism. A hierarchical two-dimensional matrixmethod for the generation and screening of hybrid electric agricultural machinery systems with multi-power output powerisproposed. The components of agricultural machinery are divided into an input layer and an output layer, and these componentsare coded and defined, and then transformed into a matrix. The hierarchical two-dimensional matrix method is usedtogenerateand screen the hybrid electric agricultural mechanism type. Through the stratification of the matrix, the complexityoftheconfiguration generation is reduced, and the constraints are applied to the basic screening of the generated configurations. Therationality of the configurations obtained after generation and screening is verified by Simulink simulation. The resultsshowthat the configuration screened by this method can meet the performance requirements of agricultural machinery.

Numerical and experimental study on the performance of a stabilising turbine inside a seed distribution device

A design was proposed for a seed turbine installed within the distribution device to reduce the influence of surface slope variations on the uniformity of seeding mass at each row. Through a comparative analysis based on the computational fluid dynamics (CFD) 6 degrees of freedom (DOF) dynamic mesh model simulation and the bench test, the influence of five different stabilising turbines on airflow distribution performance was investigated. The type Ⅰ stabilising turbine, characterised by acute inlet and outlet angles, exhibited a smaller vortex region at the blade inlet and improved the conveying and mixing performance of the seed. A CFD 6DOF simulation experiment was conducted to investigate the influence of the type I stabilising turbine on the airflow field distribution. As the number of blades increased from 4 to 10, the stability and uniformity of the conveying airflow distribution were enhanced at the turbine outlet. Simulations using a comprehensive performance test platform of the planter evaluated the influence of stabilising turbines with different numbers of blades on uniformity during field operations at varying surface slopes. When the angles of the front-rear and lateral one-way oscillation combination, and the front-rear and lateral reciprocating oscillation combination varied within the range of −5°–5°, the stabilising turbine with 8 blades exhibited the smallest uniformity coefficient of variation of the seeding mass at each row. The values ranged from 4.1 % to 5.8 % for rapeseeds and from 3.8 % to 5.0 % for wheat seeds.

Perceptions of Autonomous Robot Teammates During Field Operations

Perceptions of Autonomous Robot Teammates During Field Operations

Modeling Ocean Swell and Overtopping Waves: Understanding Wave Shoaling with Varying Seafloor Topographies

One risk posed by hurricanes and typhoons is local inundation as ocean swell and storm surge bring a tremendous amount of energy and water flux to the shore. Numerical wave tanks are developed to understand the dynamics computationally. The three-dimensional equations of motion are solved by the software ‘Open Field Operation And Manipulation’ v2206. The ‘Large Eddy Simulation’ scheme is adopted as the turbulence model. A fifth-order Stokes wave is taken as the inlet condition. Breaking, ‘run-up’, and overtopping waves are studied for concave, convex, and straight-line seafloors for a fixed ocean depth. For small angles of inclination (<10°), a convex seafloor displays wave breaking sooner than a straight-line one and thus actually delivers a smaller volume flux to the shore. Physically, a convex floor exhibits a greater rate of depth reduction (on first encounter with the sloping seafloor) than a straight-line one. Long waves with a speed proportional to the square root of the depth thus experience a larger deceleration. Nonlinear (or ‘piling up’) effects occur earlier than in the straight-line case. All these scenarios and reasoning are reversed for a concave seafloor. For large angles of inclination (>30°), impingement, reflection, and deflection are the relevant processes. Empirical dependence for the setup and swash values for a convex seafloor is established. The reflection coefficient for waves reflected from the seafloor is explored through Fourier analysis, and a set of empirical formulas is developed for various seafloor topographies. Understanding these dynamical factors will help facilitate the more efficient designing and construction of coastal defense mechanisms against severe weather.

Sand Production Prediction with Machine Learning using Input Variables from Geological and Operational Conditions in the Karazhanbas Oilfield, Kazakhstan

This paper describes a comprehensive approach to predict sand production in the Karazhanbas oilfield using machine learning (ML) techniques. By analyzing data from 2000 wells, the research uncovered the complex dynamics of sand production and emphasized the critical need for accurately predicting the peak sand mass and its occurrence time. ML techniques can have a significant impact on prediction of sand production and on the optimization of oilfield operation, which can be improved with the combined use of enriched training data and domain-specific knowledge. The research underscored the influence of geological factors, especially fault proximity, on prediction accuracy. Domain and field knowledge is needed to formulate different production scenarios for prediction purposes such that the relevant data can be selected for the training of ML models. Moreover, new metrics are needed to evaluate model performance as the applied method is tailored for different operational strategies. As the peak sand mass is considered a pivotal event in field operation, new metrics in terms of peak prediction accuracy and peak time prediction accuracy were introduced to evaluate the performance of ML models. A suite of ML algorithms was employed in the study, which demonstrated notable accuracy in the classification of sand-producing wells.

(Invited) Growing Production Capacity in PEM Electrolysis: Learnings and Technical Accomplishments

Development of hydrogen production capacity with low carbon intensity (low carbon dioxide emissions) is an essential part of reaching global sustainability targets and mitigating long term climate change. While electrolysis has been commercial for decades, many applications have been smaller scale, and only a small fraction of the total hydrogen production is made from water rather than methane. In the US, there have also now been 7 Hubs selected which each have minimum targets of 50 metric tons of low carbon hydrogen production per day. The only way to meet these capacities in the near term is with proven commercial technologies such as liquid alkaline and proton exchange membrane (PEM) electrolysis. However, even though PEM technology has been commercial for decades, it has only recently reached megawatt scale, and most “gigafactories” that have been announced are not completely built out or staffed for these demands. It is therefore essential that PEM developers scale manufacturing capacity of cell stacks significantly in the next few years.Experience in commercial installations, manufacturing, and previous scale up are critical to success. Nel Hydrogen US has successfully scaled up cell stack active area by almost 2 orders of magnitude from its earliest products, and has gained decades of real world experience in how different field operation can be from controlled internal tests. The smaller platforms also offer built in verification testing for new designs and manufacturing methods. This talk will discuss Nel’s stack scale up journey, as well as updates on current expansion efforts.

Seismic anisotropy of granitic rocks from a fracture stimulation well at Utah FORGE using ultrasonic measurements

For effective fracture stimulation in Enhanced Geothermal Systems (EGS), characterization of subsurface rock elastic properties is essential. Because seismic anisotropy significantly influences hydraulic fracturing, locating and characterizing microseismic events, and forecasting stress trajectories, our research undertakes laboratory experiments to study seismic anisotropy parameters of core samples from the Utah FORGE EGS site. We evaluated two rock samples from well 58–32, at two depths of 2074.16 m (6,805 feet) and 2268.01 m (7,441 feet), respectively. We analyze the mineralogy and classify the shallower rock as syenogranite, and the deeper rock as orthogneiss and monzonite. We cut cylindrical plugs from these two core samples, imaged them by computed tomography (CT) X-ray imaging, and measured their seismic anisotropy using ultrasonic waves at 1 MHz and under varying confining pressures (5–50 MPa) in ambient temperature conditions. With the assumption of the transversely isotropic (TI) medium, we measured three P-wave velocities and two S-wave velocities along different propagation directions to compute the Thomsen anisotropy parameters. Our results showed that the measured seismic velocities of each rock sample increase with increasing effective pressures, a behavior likely caused by closure of microcracks identified in our CT images. We also find that the measured anisotropies are higher at lower effective pressures. The maximum measured anisotropies for P-waves and S-waves are ∼23% and ∼20%, respectively. Therefore, we expect in the field operation that anisotropy should increase with increasing pore pressure caused by injection, which could be valuable in future EGS reservoir management and monitoring.

First-principle calculations of the electronic, vibrational, and thermodynamic properties of nitrogen-rich salt of 3,6-dinitramino-1,2,4,5-tetrazine [(NH4)2(DNAT)

Energy-containing materials such as explosives have attracted considerable interest recently. In the field of high-energy materials, tetrazine and its derivatives can largely meet the requirements of high nitrogen content and oxygen balance. Nitrogen-rich energetic salts are important research subjects. Nitrogen-rich salt of 3,6-dinitramino-1,2,4,5-tetrazine is a high-energy nitrogen-rich material, but there are few related studies. This paper systematically studies the crystal structure and electronic, vibrational, and thermodynamic properties of (NH4)2(DNAT). The lattice parameters of (NH4)2(DNAT) are observed to align well with the experimental values. The properties of electrons are analyzed by band structure and density of states (DOS). The phonon dispersion curves indicate that the compound is dynamically stable. The vibrational modes of bonds and chemical groups are described in detail, and the peaks in the Raman and infrared spectra are assigned to different vibration modes. Based on the vibration characteristics, thermodynamic properties such as enthalpy (H), Helmholtz free energy (F), entropy (S), Gibbs free energy (G), constant volume heat capacity (CV), and Debye temperature (Θ) are analyzed. This article can pave the way for subsequent work or provide data support to other researchers, promoting further research. In this study, we utilized the density functional theory (DFT) for our calculations. The exchange-correlation potential and van der Waals interactions were characterized based on the GGA-PBE + G function calculation. We obtained Brillouin zone integrals using Monkhorst-Pack k-point grids, with the k-point of the Brillouin zone set to a 2 × 2 × 2 grid. During the self-consistent field operation, we set the total energy convergence tolerance to 5 × 10-6eV per atom. The cut-off energy for the calculation was established at 830eV. Additionally, the states of H (1s1), C (2s2 2p2), N (2s2 2p3), and O (2s2 2p4) were treated as valence electrons in our study.

Mechanical Stability of PV Modules

A significant increase in reported glass breakages from the field was recognized during the past three years, where a disproportionately high number of modules were affected by glass breakage. Different substructures and module designs are affected, framed and unframed modules, tracked and fixed systems. What all inquiries have in common, however, is that modules with a double-glazed design with ≤ 2.5 mm glass thicknesses are affected and the problems were observed after just a few months in field operation. Various factors such as heavy weather events, faulty installation or errors in the structural design could be excluded as root causes and our experience points on additional, more fundamental problems that are associated in particular with the continuing trend towards larger modules > 3 m2 and thinner module glass ≤ 2mm. Furthermore, it seems that the residual compressive surface stress of the glass as one major parameter that determines the stability of glass panes has not been considered in this context in the PV module industry yet. In this work, we focus on the glass thickness in combination with the compressive surface stress. Besides qualitative methods, one possibility to investigate the surface stress quantitatively is a scattered light polariscope (SCALP), previously used in the glass industry. In particular, the aim is to validate the SCALP measurement method for the use on PV modules. Furthermore, a potential correlation between the surface compressive stress and the mechanical stability of various common module designs with 2 mm and 1.6 mm glass is investigated.

A Deep Learning Approach to Investigating Clandestine Laboratories Using a GC-QEPAS Sensor

Illicit drug production in clandestine laboratories involves the use of large quantities of different chemicals that can be obtained for legitimate purposes. The identification of these chemicals, including reagents, catalyzers and solvents, is crucial for forensic investigations. From a legal point of view, a drug precursor is a material that is specific and critical to the production of a finished chemical and that constitutes a significant portion of the final molecular structure of the drug. In this study, a gas chromatography quartz-enhanced photoacoustic spectroscopy (GC-QEPAS) sensor—in conjunction with a deep learning model—was evaluated for its effectiveness in the detection and identification of interesting compounds for the production of amphetamine, methamphetamine, 3,4-methylenedioxymethamphetamine (MDMA), phenylcyclohexyl piperidine (PCP), and cocaine. The GC-QEPAS sensor includes a gas sampler, a fast GC for separation, and a QEPAS detector, which excites molecules exiting the GC column using a quantum cascade laser to provide the infra-red (IR) spectrum. The on-site capability of the GC-QEPAS system offers significant advantages over the current instruments employed in this field, including rapid analysis, which is crucial in field operations. This allows law enforcement to quickly identify specimens of interest on site. The system’s performance was validated by taking into account the limit of detection, repeatability, and within-laboratory reproducibility. The results showed excellent repeatability and reproducibility for both the GC and QEPAS modules. The deep learning model, a multilayer perceptron neural network, was trained using IR spectra and retention times, achieving very high classification accuracy in the testing conditions. This study demonstrated the efficacy of the GC-QEPAS sensor combined with a deep learning model for the reliable identification of drug precursors, providing a robust tool for law enforcement during criminal investigations in clandestine laboratories.

Optimization of Operation Parameters and Performance Prediction of Paddy Field Grader Based on a GA-BP Neural Network

Paddy field leveling is an essential step before rice transplanting. During the operation of a paddy field grader, a common issue is the wrapping of rice straw around the blades, resulting in a low rice straw burial rate. This study focused on analyzing the operating parameters of a disc spring–tooth-combined paddy field grader. A soil–straw mechanism simulation model was created using EDEM 2021 software to simulate the field operation status. Firstly, the single-factor test was carried out, with the working speed, the working depth of the disc cutter roller, and the rotation speed of the cutter roller as the factors and the straw-buried rate (SBR) and the machine forward resistance (MFR) as the test indexes, and the parameter range was optimized. The parameters were optimized by the response surface method (RSM) and machine learning algorithms. The results indicated that the genetic algorithm–back propagation (GA-BP) neural network outperformed other optimization models in terms of prediction accuracy and stability. By utilizing the GA-BP regression model and RSM model for regression fitting, two sets of optimal parameter combinations were obtained. Verification experiments were carried out using two sets of parameter combinations. Taking the average of the experimental results, the simulation results showed that the straw burial rate was 93.47% and the forward resistance was 6487 N for the parameter combinations of RSM, and the straw burial rate was 94.86% and the forward resistance was 6352 N for the parameter combinations of GA-BP; the field experiments showed that the straw burial rate was 92.86% and the forward resistance was 6518 N for the parameter combinations of RSM, and the straw burial rate was 95.17% and the forward resistance was 6249 N for the parameter combinations of GA-BP. The results demonstrated that the GA-BP prediction model exhibited better predictive capabilities compared to the traditional RSM, providing more accurate predictions of the paddy field grader’s field operation performance.

Ant-observer: A new approach for automatic acquisition and autonomous analyses of individual species abundance and interactions

Traditional methods, such as direct observations or manually photographing bait stations, are limited in both data collection efficiency and accuracy. Consequently, they fail to capture the dynamic changes and complex relationships between ant species. We developed a novel system as a potential solution that combines processed consumer-grade camera equipment and software that acquires data needed for in situ experiments. This software features a user-friendly graphics user interface (GUI) for data collection and post-hoc analysis from videos captured by the camera equipment. Researchers can use this system to (1) count species abundance per frame; (2) calculate species speed; (3) detect ant interactions; and (4) count the abundance of different subcastes within species on baits. To demonstrate the complete workflow, we applied this system on ant videos collected in Hong Kong. Practical guides (supplement materials) detailing suggestions for camera equipment and software operations in both field and laboratory studies are provided. We present the results of ten in situ experiments of ants (each lasting 1–1.5 h) conducted in Hong Kong though processed consumer-grade camera equipment, with videos analyzed using the newly developed software. The analysis captured a range of variables related to ant foraging behavior, including resource discovery time, nestmate recruitment rate, and interspecific interactions. Our results confirm that the developed software provides an integrated and efficient approach for data collection, extraction, and analysis in in situ experiments, enhancing the capabilities of insect ecology research.