Low Operation Research Articles

Research on carbon emission accounting and low carbon operation methods for urban water supply systems

Under the environment of global warming, how to promote the innovative and efficient development of water supply system is the key to sustainable urban development. There is still room for improvement in the study of carbon emissions in urban water supply systems and there is a lack of appropriate rationale for low-carbon operation and management of waterworks. A water-energy-carbon (WEC) framework is established in Fuzhou to account for the carbon emissions from 2013 to 2022 and the level of coupling and coordination development is evaluated in this study. A multi-objective optimization model and a system dynamics (SD) model were developed to optimize the low-carbon operation of the water supply system and predict future climate impacts. We also provide a quantitative and qualitative evaluation method for low-carbon operation. The results show that carbon emissions generated by electricity consumption and chemical use in the water supply system contribute greatly, and the coupling coordination level is high. Under different scenarios, the optimal solutions of carbon emissions, carcinogenic risk factors and unit energy economy range from 1.63 to 3.82 kt CO2-eq, 4.4 × 10−5 to 2.7 × 10−4, and 0.26 to 0.73 RMB/MJ. In the SD model, the water supply system can achieve 17 % and 20 % carbon reduction in 2025 and 2030. Its carbon reduction direction is mainly reflected in public service water use. This study provides a reference for optimizing the operation of water supply systems.

Enhanced elevated-temperature performance of LiMn2O4 cathodes in lithium-ion batteries via a multifunctional electrolyte additive

LiMn2O4 is a promising cathode material for lithium-ion batteries (LIBs) due to its low cost, environmental friendliness, and high voltage operation. However, its electrochemical performance deteriorates at elevated temperatures, primarily by reason of the structural degradation during cycling and proliferation of adverse reactions at the electrode/electrolyte interface. One of the main reasons is that the hydrolysis and decomposition of the LiPF6 at high temperatures produces HF, which leads to corrosion at the electrode/electrolyte interface and accelerates manganese dissolution. In this work, we report a multifunctional silane-based electrolyte additive, trimethoxyphenylsilane (TMPS), to solve these problems. Theoretical calculations and experimental results both demonstrate that TMPS can eliminate HF, stabilize PF5, and inhibit LiPF6 hydrolysis, thereby mitigating transition metal leaching. Additionally, TMPS improves the stability between cathode and electrolyte by constructing a highly stable and homogeneous cathode-electrolyte interfacial. The LiMn2O4//Li cell using the electrolyte containing 1 wt% TMPS retains 95.5 % and 83.7 % of its capacity after 500 cycles at 25 °C and 60 °C, respectively. Moreover, 18650-type cylindrical cells with this electrolyte also exhibit enhanced electrochemical performance at elevated temperatures. It demonstrates that TMPS is a promising multifunctional additive for manganese-based cathodes in LIBs.

Static and Dynamic Model Calibration for Upper Thermosphere Determination

AbstractThermospheric density, a crucial factor in spacecraft operations, poses a significant challenge in accurate determination due to the intricate coupling of the thermosphere‐ionosphere system. Despite capturing long‐term trends, the widely used empirical models, which are used for low Earth orbit spacecraft operations, often fail to reproduce small‐scale or short‐term variations in the thermosphere. This work presents a novel approach for model improvement. The background model employed is our recently developed empirical model, which blends numerical simulations and satellite measurements. The model residuals are compiled into longitude and latitude bins, and the corresponding basic modes of describing variabilities are subsequently derived via the PCA method. The attendant amplitudes exhibit significant local time and seasonal dependencies, motivating optimized parameterization, that is, static calibration. Moreover, the present study reveals that the most dominant mode correlates to land‐sea contrasts and manifests global synchronicity upon excluding local time and seasonal dependences. This establishes the real‐time model adjustment foundation by exploiting limited calibration observations, which is referred to as dynamic calibration. The results from the dynamic calibration model indicated considerably improved thermospheric representation, especially for small‐scale or short‐term variations, toward a better thermospheric prediction.

Microalgal-bacterial co-cultivation on novel bio-coated supports: Evaluation of growth performance in submerged and permeated biofilm cultivation system with cost-benefit assessment

In microalgae mass production, co-cultivation with bacteria and biofilm immobilization hold promise, yet challenges persist in biofilm-based cultivation due to weak cohesion under stress. Hence, a novel bio-coating derived from spent medium and cells (extra-/intra-cellular organic matter from Cylindrotheca fusiformis and Escherichia coli) was applied to microporous membrane in submerged and permeated biofilm systems. Results showed a minimum 25 % improvement in biomass productivity (up to 45 g m−2) on bio-coated membranes in permeated system. Mucopolysaccharides in bio-coating facilitated biofilm development and encouraged a 10-fold higher AOM yield (defense mechanism against shearing force) in submerged systems, but biomass productivity was 10 times lower than permeated system. In permeated system, cells on IOM-coated membranes exhibited the highest biomass growth and lipid yield, potentially addressing the biomass-lipid trade-off. Permeated system with low operating cost around 69 $ kg−1 was a viable cultivation approach, presenting an opportunity to optimize microalgae production facilities.

Construction and Application Research of Beer Category Sales Forecasting Model Based on Big Data Analysis for Supermarket X

This study constructs a sales forecasting framework incorporating multi-source data based on beer category sales data from Supermarket X. The research collected beer sales data from 2020-2023, integrating temperature data, holiday information, and promotional activity data through an improved LSTM-based deep learning model. The study innovatively introduced Attention Mechanisms into the prediction model and proposed a dynamic weight allocation method for seasonal features, effectively enhancing the integration of heterogeneous data. Experimental results demonstrate excellent prediction performance across different time scales, achieving accuracy rates of 95%, 97%, and 98% for daily, weekly, and monthly forecasts respectively. Through practical validation in five demonstration stores, the model application improved inventory turnover by 25%, reduced stockout rates by 35%, and lowered operating costs by 15%. The research findings provide effective decision support tools for retail enterprises' refined operations management while suggesting future research directions regarding limitations in seasonal product forecasting and extreme weather response, offering practical reference for regional retail industry's intelligent transformation.

Graded colour Lie superalgebras for solving Lévy-Leblond equations

Abstract The Lévy-Leblond equation with free potential admits a symmetry algebra that is a $\mathbb{Z}_2\times\mathbb{Z}_2$-graded colour Lie superalgebra (see Aizawa--Kuznetsova--Tanaka--Toppan, 2016). We extend this result in two directions by considering a time-independent version of the Lévy-Leblond equation. First, we construct a $\mathbb{Z}_2^3$-graded colour Lie superalgebra containing operators that leave the eigenspaces invariant and demonstrate the utility of this algebra in constructing general solutions for the free equation. Second, we find that the ladder operators for the harmonic oscillator generate a $\mathbb{Z}_2\times\mathbb{Z}_2$-graded colour Lie superalgebra and we use the operators from this algebra to compute the spectrum. These results illustrate two points: the Lévy-Leblond equation admits colour Lie superalgebras with gradings higher than $\mathbb{Z}_2\times\mathbb{Z}_2$ and colour Lie superalgebras appear for potentials besides the free potential.

An Investigation on the Performance of the Ultrasonic Atomization-Based Cutting Fluid (uACF) Spray System

Nowadays, due to limited resources and manufacturers desire to keep manufacturing costs at the lowest level, minimum quantity lubrication systems stand out. Ultrasonic atomization-based cutting fluid (uACF) spray system, which is one of the minimum quantity lubrication methods, attracts attention with its low production cost and much lower cutting fluid consumption compared to conventional cooling systems. A limited number of studies have been carried out to demonstrate the performance of this innovative, compact and environmentally friendly system under real cutting conditions. uACF system was compared with spray cooling (SPR), compressed air cooling (AIR) and dry machining (DRY) conditions using similar machining parameters. Cutting speed and feed rate were selected as machining parameters. The effect of the machining parameters on the cutting force (Fc), cutting temperature (Tc), average surface roughness (Ra) and chip shrinkage coefficient (ξ) were compared with other systems. The study concluded that the uACF system can outperform or compete with different cooling methods in all performance outputs with the right choice of cutting parameter combination. In addition, the study also revealed the effects of cutting speed and feed rate levels on performance outputs under different cooling conditions. In the light of the data obtained from the study, it is concluded that the uACF system provides a good performance under real cutting conditions with a low amount of cutting fluid consumption (0.5 ml/min) with low operating cost and has a high utilisation potential.

Development and Characterization of Ammonia Removal Moving Bed Biofilms for Landfill Leachate Treatment

Urbanization growth has intensified the challenge of managing and treating increasing amounts of municipal solid waste (MSW). Landfills are commonly utilized for MSW disposal because of their low construction and operation costs. However, this practice produces huge volumes of landfill leachate, a highly polluting liquid rich in ammoniacal nitrogen (NH3-N), organic compounds, and various heavy metals, making it difficult to treat in conventional municipal wastewater treatment plants (WWTPs). In recent years, research has shown that microbial biofilms, developed on carriers of different materials and called “moving bed biofilm reactors” (MBBRs), may offer promising solutions for bioremediation. This study explored the biofilm development and the nitrification process of moving bed biofilms (MBBs) obtained from high ammonia-selected microbial communities. Using crystal violet staining and confocal laser-scanning microscopy, we followed the biofilm formation stages correlating nitrogen removal to metagenomic analyses. Our results indicate that MBBs unveiled a 10-fold more enhanced nitrification rate than the dispersed microbial community present in the native sludge of the Porto Sant’Elpidio (Italy) WWTP. Four bacterial families, Chitinophagaceae, Comamonadaceae, Sphingomonadaceae, and Nitrosomonadaceae, accumulate in structured biofilms and significantly contribute to the high ammonium removal rate of 80% in 24 h as estimated in leachate-containing wastewaters.

Experimental treatment of pickled vegetable wastewater by electrofenton method based on modified PbO2 electrode

The pickle industry is acknowledged as one of the most environmentally harmful sectors within the food industry. The elevated concentrations of chemical oxygen demand (COD), total phosphorus (TP), ammonium nitrogen (NH4+-N), and salinity found in pickle wastewater present significant environmental challenges. To tackle these issues, this study investigates the efficacy of a La-doped Ti/SnO2-Sb2O5/La-PbO2 electrode employing an electro-Fenton method for the treatment of pickle wastewater. Experimental design was performed using response surface methodology to identify optimal operational parameters. Results demonstrate that all processes adhere to pseudo-first-order reaction kinetics. Under conditions where the degradation temperature is maintained at 65 °C, current density at 100 mA cm−2, pH at 4.1, FeSO4 concentration at 3.03 mmol L−1, and H2O2 concentration at 2.07 mmol L−1 with six additions over a degradation period of 240 minutes, COD removal achieved an impressive rate of 92.87 %, while ammonium nitrogen removal reached a remarkable rate of 95.26 %. In comparison to traditional electrochemical methods, both COD and ammonium nitrogen removal rates exhibited substantial improvements; furthermore, hydroxyl radicals (•OH) played a pivotal role in facilitating the radical quenching degradation process. The operation cost analysis shows that the electric Fenton process has a lower operation cost, which provides a good opportunity for the food industry to treat Pickled vegetables wastewater. The operating cost for COD removal rate is 1.262 $ m−3kg−1, and the operating cost for ammonia nitrogen removal rate is 1.355 $ m−3kg−1.

A Multiscale Adaptive Fusion Network for Modular Multilevel Converter Fault Diagnosis

Modular Multilevel Converters (MMCs) play a crucial role in new energy grid connection and renewable energy conversion systems due to the significant merits of good modularity, flexible scalability, and lower operating loss. However, reliability is a significant challenge for MMCs, which consist of a large number of Insulated Gate Bipolar Transistors (IGBTs). Failures of the IGBTs in submodules (SMs) are a critical issue that affect the performance and operation of MMCs. The insufficient ability of convolutional neural networks to learn key fault features affects the accuracy of MMC fault diagnosis. To resolve this issue, this paper proposes a novel deep fault diagnosis framework named the Multiscale Adaptive Fusion Network (MSAFN) for MMC fault diagnosis. In the proposed MSAFN, the fault features of the raw current in an MMC are extracted by employing multiscale convolutional neural networks (CNNs) firstly, and then a channel attention mechanism is added to adaptively select the channel containing key features, so as to improve the fault diagnosis ability of the MMC in a noisy environment. Finally, the adaptive size of a one-dimensional CNN is adopted to adjust the weight of the feature channels of different scales, which are adaptively fused for fault diagnosis. Experimental validation is performed on two different MMC datasets. Experimental results confirm that the introduction of an attention mechanism of the multiscale feature adaptive fusion channel improves the recognition accuracy of the model by an average of 15.6%. Moreover, comparative experiments under different signal-to-noise ratios (SNRs) demonstrate that the MSAFN maintains accuracy levels above 96.7%, highlighting its excellent performance, particularly under noisy conditions.

Printed High-Entropy Prussian Blue Analogs for Advanced Non-Volatile Memristive Devices.

Non-volatile memristors dynamically switch between high (HRS) and low resistance states (LRS) in response to electrical stimuli, essential for electronic memories, neuromorphic computing, and artificial intelligence. High-entropy Prussian blue analogs (HE-PBAs) are promising insertion-type battery materials due to their diverse composition, high structural integrity, and favorable ionic conductivity. This work proposes a non-volatile, bipolar memristor based on HE-PBA. The device, featuring an active layer of HE-PBA sandwiched between Ag and ITO electrodes, is fabricated by inkjet printing and microplotting. The conduction mechanism of the Ag/HE-PBA/ITO device is systematically investigated. The results indicate that the transition between HRS and LRS is driven by an insulating-metallic transition, triggered by extraction/insertion of highly mobile Na+ ions upon application of an electric field. The memristor operates through a low-energy process akin to Na+ shuttling in Na-ion batteries rather than depending on formation/rupture of Ag filaments. Notably, it showcases promising characteristics, including non-volatility, self-compliance, and forming-free behavior, and further exhibits low operation voltage (VSET = -0.26 V, VRESET = 0.36 V), low power consumption (PSET = 26 µW, PRESET = 8.0 µW), and a high ROFF/RON ratio of 104. This underscores the potential of high-entropy insertion materials for developing printed memristors with distinct operation mechanisms.

Free-Standing Carbon Nanofiber Films with Supported Cobalt Phosphide Nanoparticles as Cathodes for Hydrogen Evolution Reaction in a Microbial Electrolysis Cell.

High-performance and cost-efficient electrocatalysts and electrodes are needed to improve the hydrogen evolution reaction (HER) for the hydrogen (H2) generation in electrolysers, including microbial electrolysis cells (MECs). In this study, free-standing carbon nanofiber (CNF) films with supported cobalt phosphide nanoparticles have been prepared by means of an up-scalable electrospinning process followed by a thermal treatment under controlled conditions. The produced cobalt phosphide-supported CNF films show to be nanoporous (pore volume up to 0.33 cm3 g-1) with a high surface area (up to 502 m2 g-1) and with a suitable catalyst mass loading (up to 0.49 mg cm-2). Values of overpotential less than 140 mV at 10 mA cm-2 have been reached for the HER in alkaline media (1 M KOH), which demonstrates a high activity. The high electrical conductivity together with the mechanical stability of the free-standing CNF films allowed their direct use as cathodes in a MEC reactor, resulting in an exceptionally low voltage operation (0.75 V) with a current density demand of 5.4 A m-2. This enabled the production of H2 with an energy consumption below 30 kWh kg-1 H2, which is highly efficient.

Optimization of energy acquisition system in smart grid based on artificial intelligence and digital twin technology

In response to the low operating speed and poor stability of energy harvesting systems in smart grids, an energy harvesting optimization method based on improved convolutional neural networks and digital twin technology is proposed in the experiment. Firstly, a smart grid data transmission framework integrating digital twin technology is proposed. A digital twin mapping method based on time, data, and topology structure is used to realize the digital twin mapping at the device level of power grid. Through data synchronization and interaction between the physical power grid and the digital twin model, the operational efficiency and reliability of the power grid are improved. Then, the classical convolutional neural network and attention mechanism are used to comprehensively analyze the physical topology data in the smart grid energy acquisition system. The improved lightweight target detection model is combined to monitor the equipment status of the smart grid and extract key features. Simultaneously utilizing convolutional attention mechanism to dynamically adjust the feature weights of channels or spaces, completing the preprocessing of energy harvesting data. Finally, combined with energy harvesting and power grid switching system, the process of energy harvesting and power grid operation are optimized together. On the training and validation sets, when the channels exceeded 60, the proposed method achieved a system energy efficiency of 55% during operation. The system energy efficiency of the other three comparative algorithms was all less than 40%. In practical applications, as the energy transfer loss increased to 1.0, the system throughput increased to 50 bits. The electricity needs of different users were met, and the difference between power allocation and optimal power allocation was small, which was very reasonable. This proves that the research has effectively optimized the energy harvesting system in the smart grid, improving the efficiency and reliability of the system in practical applications of the smart grid. At the same time, in the increasingly severe energy problem, this system can further provide technical references for the utilization of renewable energy and help achieve the goal of sustainable energy.

Development of a chemiluminescent detection method for absolute activity measurement of Taq DNA polymerase through dATP consumption

Being one of the most extensively researched and widely used thermostable DNA polymerases, Taq DNA polymerase derived from Thermus aquaticus has played pivotal roles in numerous molecular biology applications. The accurate and convenient determination of enzymatic activity is a fundamental prerequisite for both academic research and the industrial application of Taq DNA polymerase. To address the limitations of traditional methods for polymerase activity determination, such as low accuracy, complicated operation, and radioactive hazards, we have devised an absolute-quantitation method for Taq DNA polymerase based on chemiluminescence detection of the consumption of dATP (CDC-dATP). The CDC-dATP method offers several advantages, including simplicity suitable for routine laboratory use, high sensitivity (detecting as low as 0.0025 U/μL), excellent reproducibility, a broad linear range for Taq DNA polymerase (0.0025–0.1 U/μL) with a strong linear relationship (R2 > 0.99), and, significantly, absolute activity quantitation without the associated radioactive hazards. We delved into the molecular behavior of Taq DNA polymerase in the CDC-dATP method. Furthermore, we explored the extensive application potential of the CDC-dATP methodology for measuring the activity of various other enzymes involved in dATP/ATP generation or consumption, encompassing other DNA polymerases, reverse transcriptases, RNA polymerases, hydrolases, and protein kinases, covering all seven major categories of enzymes.

ExaGRyPE: Numerical general relativity solvers based upon the hyperbolic PDEs solver engine ExaHyPE

ExaGRyPE describes a suite of solvers and solver ingredients for numerical relativity that are based upon ExaHyPE 2, the second generation of our Exascale Hyperbolic PDE Engine. Numerical relativity simulations are crucial in resolving astrophysical phenomena in strong gravitational fields and are fundamental in analyzing and understanding gravitational wave emissions. The presented generation of ExaGRyPE solves the Einstein field equations in the standard CCZ4 formulation under a 3+1 foliation and focuses on black hole space-times. It employs a block-structured Cartesian grid carrying a higher-order Finite Difference scheme with full support of adaptive mesh refinement (AMR), it facilitates massive parallelism combining message passing, domain decomposition and task parallelism, and it supports the injection of particles into the grid as static data probes or as moving tracers. We introduce the ExaGRyPE-specific building blocks within ExaHyPE 2, and discuss its software architecture and compute-n-feel.For this, we formalize the creation of any specific astrophysical simulation with ExaGRyPE as a sequence of lowering operations, where a few abstract logical tasks are successively broken down into finer and finer tasks until we obtain an abstraction level which can directly be mapped onto a C++ executable. The overall program logic is fully specified via a domain-specific Python interface, we automatically map this logic onto a more detailed set of numerical tasks, subsequently lower this representation onto technical tasks that the underlying ExaHyPE engine uses to parallelize the application, before eventually the technical tasks in turn are mapped onto small task graphs including the actual astrophysical PDE term evaluations, initial conditions, boundary conditions, and so forth. These can be injected manually by the user, or users might instruct the solver on the most abstract user interface level to use out-of-the-box ExaGRyPE implementations. We end up with a rigorous separation of concerns which shields ExaGRyPE users from technical details and hence simplifies the development of novel physical models. We present the simulations and data for the gauge wave, static single black holes and rotating binary black hole systems, demonstrating that the code base is mature and usable. However, we also uncover domain-specific numerical challenges that need further study by the community in future work.

High-performance overall water electrolysis enabled by a one-step fabricated bifunctional Pt/NiFe LDH catalyst on iron nickel foam

The development of bifunctional electrocatalysts with high efficiency for overall water splitting is still a challenging task. In this work, we introduce a novel one-step synthesis method for Pt/NiFe layered double hydroxide (LDH) on an iron-nickel foam (INF) substrate under mild conditions. This method eliminates the need for additional Ni or Fe ions and facilitates in-situ etching and growth processes under mild conditions, resulting in a higher active surface area, well-dispersed low-loading Pt nanoparticles, and a self-supported electrode without binders. These features collectively enhance electron transfer and catalytic activity for both HER and OER. The Pt/NiFe LDH/INF exhibits remarkable water splitting performance, requiring a cell voltage of only 1.44 V at10 mA cm−2 when used as both anode and cathode. Additionally, it remains stable at 20 mA cm−2 for at least 16 h. The exceptional water splitting activity of Pt/NiFe LDH/INF in alkaline solution can be attributed to the synergistic effects of Pt and NiFe LDH, which improve the hydrogen evolution reaction and oxygen evolution reaction efficiencies. The results provide valuable insights into designing bifunctional electrocatalysts with low Pt loading for optimal water splitting performance at low operating cost.

Automated Gas Influx Handling Model and Mechanisms During Deep High-Temperature and High-Pressure Well Drilling

The exploration and development of oil and gas resources in deep formations is a key strategic priority for national energy production. However, manual methods for handling gas kicks suffer from low operating accuracy and inefficiency during high-temperature and high-pressure deep well drilling. To address the need for real-time bottomhole pressure prediction and control, an efficient gas–liquid–solid computing model was developed based on the gas slip model and cuttings settling velocity model. By integrating this model with an automatic choke adjustment system, an automatic gas kick attenuation model for deep well drilling was established. Results show that, compared to the driller’s and wait-and-weight methods, the automatic gas kick attenuation method significantly reduces peak choke pressure due to its larger frictional pressure drop and higher cuttings hydrostatic pressure. The automatic attenuation method not only leads to an average reduction of 28.42% in maximum choke/casing pressure but also accelerates gas removal, achieving gas kick attenuation ten times faster than the driller’s method and seven times faster than the wait-and-weight method. The study also investigates the influence of gas solubility, well depth, gas influx volume, formation permeability, and drilling fluid volumetric flow rate on gas kick attenuation characteristics. The findings provide a solid foundation for improving the efficiency of gas kick management in deep well drilling operations.

UV-activated TiO2/SnO2 oxygen sensor with improved response speed for low temperature and air-atmospheric operation

An UV-activated TiO2/SnO2 thin-film oxygen sensor was fabricated by simple sputtering deposition and thermal annealing. It was found that introducing TiO2 film can obviously increase the response speed of the sensor (∼ 2 fold increase with 20 nm TiO2 introducing). Moreover, it was found that introducing TiO2 would lead to the formation of orthorhombic phase of SnO2, due to the dissolution of Ti into Sn metal. The sensor can detect oxygen in air atmosphere (detection limit of 1 % oxygen concentration) at low operating temperature (92 °C) with good long-term stability, selectivity and anti-humidity. Effects of TiO2 and SnO2 thickness on sensing performance were investigated, and related detection mechanism was discussed. To our knowledge, it is the first demonstration that introducing TiO2 into SnO2 can improve the O2 response speed, especially at low operating temperature (92 °C).

Study of compact traveling-standing wave relativistic folded waveguide oscillator with low magnetic field operation

Abstract In this article, a compact Traveling-standing Wave Relativistic Folded Waveguide Oscillator (TSW-RFWO) is proposed, leveraging a 0.3 T guiding magnetic field to address the crucial needs for miniaturization and practicality in high-power microwave systems. The study conducts a thorough analysis of the electromagnetic characteristics of the TSW-RFWO. Its internal traveling-standing wave signal is analyzed. Utilizing the traveling-standing wave slow-wave structure (TSW-SWS), the TSW-RFWO obtains a low Q e (external quality factor) of 39. The necessity of compacting the high-power device under low magnetic guiding field is analyzed. Employing a 300 kV, 400 A circular electron beam within a 0.3 T guiding magnetic field, PIC simulations indicate a potential output power of 57 MW at 2.74 GHz, achieving an efficiency of 47.5%.

Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions

With the rapid development of space exploration technology, the detection of extraterrestrial bodies has become increasingly important. Among these, path planning and target recognition and positioning technologies are particularly critical for applications in intelligent agents with low computational power operating in complex environments. This paper presents a novel approach to path planning on low-resolution lunar surface maps by introducing an improved A* algorithm with an adaptive heuristic function. This innovation enhances robustness in environments with limited map accuracy and enables paths that maintain maximum distance from obstacles. Additionally, we innovatively propose the Dynamic Environment Target Identification and Localization (DETIL) algorithm, which identifies unknown obstacles and employs spatiotemporal clustering to locate points of interest. Our main contributions provide valuable references for the aerospace industry, particularly in lunar exploration missions. The simulation results demonstrate that the improved A* algorithm reduces the maximum elevation difference by 55% and the maximum cumulative elevation difference by 68% compared to the traditional A* algorithm. Furthermore, the DETIL algorithm’s obstacle identification component successfully recognizes all the obstacles along the path, and its spatiotemporal clustering improves the average number of target discoveries by 152% over the conventional DBSCAN clustering approach.