Field Operations Research Articles

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.

Comprehensive analysis of the effect of structural parameters on erosion wear, structural stress, and deformation of high-pressure double-elbow in shale-gas fracturing

In field hydraulic fracturing operation of shale gas development, the high pressure and large displacement liquid-particle two-phase fracturing fluid can be forced to change direction many times through high-pressure double-elbow, and be transported from the outlet pipeline of the fracturing pump to the main pipeline. The high-pressure double-elbow is prone to be affected by erosion wear and Fluid-Structure Interaction (FSI), resulting in perforation and fracture, posing a potential safety threat to field operation. In this study, we conducted the erosion wear experiments on 35CrMo steel used for high-pressure double-elbow in shale-gas fracturing. The erosion rates under different impact angles and flow velocities were obtained, and proposed a novel model of erosion prediction for high-pressure double-elbow. Then the numerical investigation was employed to conduct a comprehensive analysis of erosion wear, structural stress and deformation by the coupling of Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA). The effects of structural parameters such as connection straight pipe length, pipe inner diameter and fluid turning direction were discussed. The results indicate that with the increase of connection straight pipe length, the flow erosion decreases first then varies little, and the deformation gradually increases. Slight erosion wear but large structural stress and deformation in major inner diameter pipe. And the minimum degree of erosion and flow-induced deformation present with the fluid turning direction of double-elbow as 0°. The study can provide references for the design, installation and detection of high-pressure double-elbow and ensure safety in the process of shale gas fracturing.

Process and pit design for waste water-based mud treatment and drilled cuttings stabilization for cluster of oil wells

Environmental degradation through crude oil and gas exploration has attracted international attention over the past decades. This prompted the development of dewatering and clarification processes to treat the waste water-based mud emanating from drilling activities. There is also the need to carry out proper solidification and inertisation and stabilization process for the drilled solids. It has caused the development of many chemicals to assist solidification and inertisation and stabilization process. The costs of treatment of waste water based mud and drilled solidification and stabilization are high. Economy of proper waste pit design is also necessary to enhance fluid and process control to minimize the cost of operation. This research work is coming out from practical field operation and is used to illustrate how proper process and pit design can enhance effective and economic waste water based mud and drilled solids inertisation and stabilization process in the oil and gas industry.

Research on Weed Reverse Detection Methods Based on Improved You Only Look Once (YOLO) v8: Preliminary Results

The rapid and accurate detection of weeds is the prerequisite and foundation for precision weeding, automation, and intelligent field operations. Due to the wide variety of weeds in the field and their significant morphological differences, most existing detection methods can only recognize major crops and weeds, with a pressing need to enhance accuracy. This study introduces a novel weed detection approach that integrates the GFPN (Green Feature Pyramid Network), Slide Loss, and multi-SEAM (Spatial and Enhancement Attention Modules) to enhance accuracy and improve efficiency. This approach recognizes crop seedlings utilizing an improved YOLO v8 algorithm, followed by the reverse detection of weeds through graphics processing technology. The experimental results demonstrated that the improved YOLO v8 model achieved remarkable performance, with an accuracy of 92.9%, a recall rate of 87.0%, and an F1 score of 90%. The detection speed was approximately 22.47 ms per image. And when shooting from a height ranging from 80 cm to 100 cm in the field test, the crop detection effect was the best. This reverse weed detection method addresses the challenges posed by weed diversity and complexities in image recognition modeling, thereby contributing to the enhancement of automated and intelligent weeding efficiency and quality. It also provides valuable technical support for precision weeding in farmland operations.

Response Characteristics of the Community Structure and Metabolic Genes of Oil-Recovery Bacteria after Targeted Activation of Petroleum Hydrocarbon-Degrading Bacteria in Low-Permeability Oil Reservoirs.

The microbial enhanced oil recovery (MEOR) process has been identified as a promising alternative to conventional enhanced oil recovery methods because it is eco-friendly and economically advantageous. However, the knowledge about the composition and diversity of microbial communities in artificially regulated reservoirs, especially after activating petroleum hydrocarbon-degrading bacteria (PHDB) by injecting exogenous nutrients, is still insufficient. This study utilized a combination of high-throughput sequencing and metagenomics technology to reveal the structural evolution characteristics of the indigenous microbial community in the reservoir during the PHDB activated for enhanced oil recovery, as well as the response relationship between the expression of its oil production functional genes and crude oil biodegradation. Results showed that Pseudomonas (>75%) gradually evolves into a stable dominant microbial community in the reservoir during the activation of PHDB. Besides, the gene expression and KEGG pathways after crude oil undergoes biodegradation by PHDB show that the number of genes related to petroleum hydrocarbon metabolism dominates the metabolism (21.98%). Meanwhile, a preliminary schematic diagram was drawn to illustrate the evolution mechanism of the EOR metabolic pathway after the targeted activation of PHDB. Additionally, it was found that the abundance of hydrocarbon-degrading enzymes increased significantly, and the activity of alcohol dehydrogenase was higher than that of aldehyde dehydrogenase and monooxygenase after PHDB activation. These research results not only filled in and expanded the theoretical knowledge of MEOR based on artificial interference or regulation of reservoir oil-recovery functional microbial community structure but also provided guidance for the future application of MEOR technology in oil field operations.

Robotics In Gynecology- A Review

This review article focuses on the development of robotics-based operations in the medical field especially in gynecology. The goal of the surgical robot is to improve surgical skills and compensate for human limitations. The robot's performance in performing duties correctly and consistently has been the key to its success. Tubal anastomosis was the first gynecologic surgery performed with Robotic assistance in 2000 followed by first Robotic Hysterectomy in 2002. In 2003 Da Vinci surgical system was developed which was FDA approved for gynecological surgery in 2005. It has gained popularity fast and is already playing a big part in many of the places where it is available. It consists of a high-resolution three-dimensional (3D) vision system adjacent to the patient on a cart with robotic branches. It provides unique technical advancement, with tremor filtration, improved ergonomics and lower muscular load as compared to both laparoscopic and open surgeries. Autonomous camera and energy instrument control, wrist articulation with 7 degree freedom, telestration and dual- console capabilities, overcoming the limitations of conventional laparoscopy, such as counterintuitive hand movements, 2 dimensional visualization, limited degree of motion and tremor amplification. Compared to laparoscopy, robotic assistance has a lower conversion rate to open procedures. Endo wrist movement of an automated machine during myomectomy surgery can perform proper and better suturing than traditional laparoscopy. The automated program is a noticeable improvement over laparoscopic surgery and, if price issues are resolved, can gain popularity among gynecological surgeons around the globe. Not unique to robotic system, the integration of indigo cyanine green fluorescence with the Da Vinci robotic system allows identification of lesions, and assessment of perfusion of bowel and ureter during deep infiltrating endometriosis resection.

Polarization-encoded photonic quantum-to-quantum Bernoulli factory based on a quantum dot source.

A Bernoulli factory is a randomness manipulation routine that takes as input a Bernoulli random variable, outputting another Bernoulli variable whose bias is a function of the input bias. Recently proposed quantum-to-quantum Bernoulli factory schemes encode both input and output variables in qubit amplitudes. This primitive could be used as a subroutine for more complex quantum algorithms involving Bayesian inference and Monte Carlo methods. Here, we report an experimental implementation of a polarization-encoded photonic quantum-to-quantum Bernoulli factory. We present and test three interferometric setups implementing the basic operations of an algebraic field (inversion, multiplication, and addition), which, chained together, allow for the implementation of a generic quantum-to-quantum Bernoulli factory. These in-bulk schemes are validated using a quantum dot-based single-photon source featuring high brightness and indistinguishability, paired with a time-to-spatial demultiplexing setup to prepare input resources of up to three single-photon states.

Vacuum Loss Detection of PTC in CSP Plants via Temperature-Sensors

The efficient operation of a solar field is an essential factor for the commercial operation of a concentrating solar power (CSP) plant. In addition to predictive control for the highest possible constant outlet temperature at high mass flow, efficient operation also includes early detection of defective components and heat losses. This work presents a method for non-invasive heat loss detection as a strong indication for vacuum losses, based on measured operational data of Andasol III, an operating 50 MW parabolic trough collector (PTC) plant located in southern Spain. To detect vacuum losses via this method, mass flow rate and temperature reduction are determined separately for each individual loop via the analysis of a short-term temperature rise of the heat transfer fluid (HTF) during preheating. While the temperature reduction was measured directly, the mass flow was determined via the thermal time-of-flight (ToF) method using the same installed temperature sensors. By measuring thermal step responses during the preheating of the solar field at nighttime operation, the influence of fluctuating direct normal irradiance (DNI), misalignment of the absorber tubes and an offset in collector focus was circumvented. In the scope of the presented work, single loops were detected, which show a higher heat loss at lower mass flow rate and therefore have an increased probability of a higher vacuum loss. Better localization and early detection of these vacuum losses would allow the corresponding absorber tubes to be renewed at the economically and environmentally best time, improving the efficiency of the solar field and thus the entire CSP plant.

Investigation of Unresolved Interface "Rag Layer" in Athabasca Oil Sand Bitumen In Situ Recovery.

Steam-assisted gravity drainage (SAGD), the leading commercial in situ bitumen recovery process, involves the underground injection of steam and produces at the well head a hot fluid containing water, hydrocarbons, and sand. This fluid is subjected to separation by diluent addition and gravity in several parallel treaters. Occasionally, the separation may be disrupted in one or few treaters by the occurrence of an unresolved interface or "rag layer" while continuing without disruption in the rest of the treaters. In the current study, we investigate "rag layer" occurrence based on the quantification of laboratory-scale and SAGD field tests and imaging of the "rag layer" morphology. The quantification results show that the formation and volume of the "rag layer" are affected by solids, mixing speed, and solvent addition. The microscopic images demonstrate the presence of both water-in-oil or oil-in water emulsions with a distinct transition between the continuous phases. The visual detection boundaries of the "rag layer" are defined as the threshold between the agglomerated and individual droplet layers. The extent of agglomeration increases in the proximity to the oil-water interface. The contribution of hydrophobic fine inorganic solids (less than 10 μm) to forming a "rag layer" is supported by their accumulation observed at the treaters' oil-water interface, compared to the feed. In well-controlled field operations, the perceived randomness of "rag layer" occurrence could be associated with the fluctuation of fine solid contents in the feed.

A fault diagnosis method of hot spots for photovoltaic clusters based on model parameters

AbstractUtilizing the direct current‐side electrical data resources of the photovoltaic power generation system on the FusionSolar platform, this study investigates the impact of hot spot faults on the output characteristics of photovoltaic strings and proposes a hot spot fault diagnosis method based on time series waveform characteristics. By analyzing the mechanisms of hot spot generation and evolution, as well as the characteristic differences in I–V curves and time series compared to other types of faults, the waveform variation patterns of hot spots in current and voltage time series are obtained. A function form suitable for hot spot fault waveform characteristics in time series graphs is constructed, and fault diagnosis feature vectors are extracted. Combining field operation and maintenance experience, a fuzzy reasoning fault diagnosis system is established to determine the causes and estimate the severity of hot spot faults. Experimental results indicate that hot spot faults have unique and corresponding variations in the current/voltage time series waveforms of the string output. The constructed function form can clearly represent the waveform variation patterns, and the established fuzzy reasoning system can achieve effective and reliable diagnosis of hot spot faults.

Metaverse‐based remote support system with smooth combination of free viewpoint observation and hand gesture instruction

AbstractIn field operations with large machines, onsite large task spaces have been typically required. In these cases, remote skilled workers have to provide a step‐by‐step guide by observing the onsite situation around the large task spaces in three dimensions, instructing onsite unskilled workers on how to perform the tasks with the correct hand gestures at the right position, and switching between observation and instruction frequently during the remote support. In this study, we developed a Metaverse‐based remote support system with a seamless user interface for switching between free viewpoint observation and hand gesture instruction. The proposed system enables remote skilled workers to observe the onsite field from any viewpoint, transfer hand gesture instruction to onsite workers, and seamlessly switch between free viewpoint observation and free hand gesture instruction without having to change devices. We compared the time efficiency of the proposed system and a conventional system through experiments with 28 users and found that our system improved the observation time efficiency by 65.7%, the instruction time efficiency by 27.9%, and the switching time efficiency between observation and instruction by 14.6%. These results indicate that the proposed system enables remote skilled workers to support onsite workers quickly and efficiently.

Multi-constraint improved RS path planning method for unmanned rice direct seeding machine

Path planning is one of the key technologies that determines the efficiency and quality of field operations using autonomous agricultural machinery. To date, there has been extensive research on global coverage path planning for unmanned agricultural machinery within the working area; however, the issue of “encircling and edging” left by the machinery during headland turns is often overlooked. This study focuses on the operational conditions of unmanned agricultural machinery in the rice fields of southern China. The path planning problem is abstracted under physical constraints, such as the space at the field headland, the agricultural environment (e.g., types of field ridges), the turning characteristics of the machinery, turning radius, working width, and minimum safety distance, into mathematical constraints. This yields an optimized path planning model and sequence combination. Based on the improved Reeds–Shepp curve, an en-circling and edging path planning algorithm is designed. The planned path for the unmanned agricultural machinery from start to finish consists only of arcs and straight lines, suitable for the right-angle turning, reversing, and 180° U-turn operations required for rice field machinery, thus significantly improving the coverage of field edges and missed areas at the headland. A rice direct-seeding machine was used as a test subject for encircling and edging field trials, with tests conducted in rectangular, trapezoidal, and irregular quadrilateral rice fields. The results show that, for rectangular plots, the full-field coverage rate of rice direct-seeding operations can reach 96.44%; for trapezoidal plots, the coverage rate is 95.47%; and for irregular quadrilateral fields, the coverage rate is 95.69%. These results are promising, indicating that the encircling and edging path planning based on the improved Reeds–Shepp curve can effectively increase the full-field coverage rate of unmanned agricultural machinery, improving work quality and meeting the needs of modernized, intelligent agricultural machinery operations.

The State of Health of Electrical Connectors

For modern machines, factories and electric and autonomous vehicles, the importance of vreliable electrical connectors cannot be overstated. With an increasing number of connectors being used in machines, factories and vehicles, ensuring their reliability is crucial for comfort and safety alike. One of the key indicators of reliability is the lifetime of connectors. To evaluate the lifetime of electrical connectors, a testing method and a model for calculating their lifetime based on the test data were developed. The results from these tests were compared to failure analysis data from long-term field operations. The findings indicate that the laboratory tests can accurately reproduce the main failures observed in the field. However, such lifetime tests can be time- and labor-intensive. To address this challenge, a data-driven method is proposed that predicts the lifetime of electrical connectors using statistical analysis of electrical contact resistance data collected from short-term tests. The predictions from this method were compared to actual results obtained from long-term tests. A strong correlation was observed between the contact resistance development in short-term tests and the number of failures in later stages of testing. Thus, apart from predicting the lifetime of connectors, this method can also be applied for failure prognosis in real-time operations.

Analysis of Vibration Characteristics of Tractor–Rotary Cultivator Combination Based on Time Domain and Frequency Domain

A good planting bed is a prerequisite for improving planting quality, while complex ground excitation often leads to machine bouncing and operation vibration, which then affects the operation effect. In order to improve the quality of rotary tillage operations, it is necessary to study the effects of various vibration excitations on the unit during tractor rotary tillage operations and analyze the vibration interaction relationship among the tractor, the three-point suspension mechanism, and the rotary tiller. For this purpose, multiple three-way acceleration sensors were installed at different positions on the rotary tiller unit of a Lexing LS1004 tractor(Lexing Agricultural Equipment Co. Ltd., Qingdao, China) to collect vibration data at different operating speeds and conduct vibration characteristic analysis between different components. The test results showed that when the unit moved forward at 2.1 km/h, 3.6 km/h, and 4.5 km/h, respectively, the vibration acceleration of the tractor, the three-point suspension mechanism, and the rotary tiller increased with the increase in speed, and there was indeed interaction between them. The vertical acceleration change during the test in the three-point suspension mechanism was the most significant (5.914 m/s2) and was related to the increase in the speed of the vehicle and the vibration transfer of the rotary tiller. Meanwhile, the vertical vibration acceleration of the tractor’s symmetrical structure was not similar, suggesting the existence of structural assembly problems. From the perspective of frequency domain analysis, the resonant frequency at the cab of the tractor was reduced in a vertical vibration environment, with relatively low frequencies (0~80 Hz) and small magnitudes, which might be beneficial to the driver’s health. The rotary tillage group resonated around 350 Hz, and this characteristic can be used to appropriately increase the vibration of the rotary tiller to reduce resistance. The tractor cab resonated around 280 Hz, which must be avoided during field operations to ensure driver health and reduce machine wear. The research results can provide a reference for reducing vibration and resistance during tractor rotary tillage operations, as well as optimizing and improving the structure of rotary tillers and tractors.

Научно-методический подход к разработке проектов опытно-промышленных испытаний открытой разработки месторождений

Methodology of designing pilot tests in open-pit mining is a complex and insufficiently developed issue in the theory and practice of designing mining systems, which, as a rule, fails to take into account the mining and geological features of the deposits. The complex development of mineral deposits requires new methods of open-pit designing. Thus, the theoretical justification and development of the methods to determine the optimal parameters and indicators of open-pits during the design of pilot tests with the establishment of scales, deadlines, tasks and test conditions is an urgent research problem. The authors of the study have systematized the research trends in developing the basis for designing pilot tests in open-pit geotechnology. The level of geological inaccuracies in case of insufficiently reliable prospecting results leads to a decrease in the expected technical and economic indicators of the mining project ranging from 5-20% to several folds. When information on the deposit is insufficient, pilot test projects are prepared. This is done in order to obtain additional input data for preparation of the feasibility study of permanent conditions or at the stage of the field development to verify the created technologies and means of their mechanisation in order to avoid revision of the mining system parameters in the course of the field operations due to discrepancies between the geomechanical and hydrogeological representations given in the detailed exploration materials and the real conditions.

Comparison of simulation tools for optimizing borehole heat exchanger field operation

Model predictive control (MPC) is a promising approach for optimizing the performance of borehole heat exchangers (BHEs) in ground-source heat pump systems. The central element of MPC is the forward model that predicts the thermal dynamics in the ground. In this work, we validate the prediction accuracy of four BHE modeling approaches against real-world measurement data across various operational events and timescales. We simulate the fluid temperature leaving a BHE using a fully discretized 3-D numerical model, a resistance–capacitance model, a g-function model, and a hybrid model. The simulated temperatures are compared to measured temperatures using three validation metrics that quantify temperature offset, noise, and accuracy. The main reason for a mismatch between measured and modeled temperatures is a temperature offset of the simulated temperature. To remove this effect, the models were calibrated for their most sensitive parameter, the ground temperature, and their prediction accuracy over 4 years was evaluated. Thereby, model calibration seems to be a viable solution to account for an unknown load history. The results show that the resistance–capacitance model provides decent predictions in the short term and the g-function model in the long term. However, both models are strongly dependent on accurate calibration. The hybrid model provides the most accurate short and long-term predictions and is less dependent on calibration. Still, its integration into optimization syntax poses challenges compared to the other models. Although not yet applied in model predictive control, the hybrid model stands out as a promising choice for optimizing BHE field operations across various timescales.