Optimization Ability Research Articles

Design optimization method of pipeline parameter based on improved artificial neural network

The rationality of pipeline design is directly related to its energy efficiency, reliability, and safety. Pipeline vibration may lead to negative effects such as mechanical loss and fatigue damage. Therefore, this study utilizes pipeline optimization design to mitigate these effects. Recently, neural networks have been widely used in structure design optimization. In the study, a backpropagation neural network (BP) combined with a variant slime mould algorithm (SMA) is utilized to solve the pipeline structure design optimization problem. Pipeline transport plays a crucial role in the efficient movement of various commodities, including but not limited to gas, oil, water, and other liquid substances. The interaction between liquid and pipeline can cause pipeline vibration and even damage. Therefore, based on the simulation model considering FSI (fluid-structure interaction), machine learning methods such as BP can predict vibration characteristics of fluid-conveying pipelines. However, existing research has shown that BP has insufficient parsing ability in structure mechanics problems, especially in solving the overall characteristics of complex structures (such as maximum structural strain). This study proposes an Arithmetic-based slime mould algorithm (ACSMA) with an adaptive decision strategy and a chaotic mapping strategy. A hybrid algorithm named ACSMA-BP is presented to promote the model's prediction ability. At last, to verify the effectiveness of the proposed pipeline structure design optimization approach, the ACSMA-BP model is utilized to complete a structure design optimization case for a simulated pipeline. The numerical results indicate that compared with AOA, CWOA, ESSAWOA, NGS_WOA, and RSA, the ACSMA has the best optimization ability.

Biocathode-anode cascade system in PRB: Efficient degradation of p-chloronitrobenzene in groundwater

The consistent presence of p-chloronitrobenzene (p-CNB) in groundwater has raised concerns regarding its potential harm. In this study, we developed a biocathode-anode cascade system in a permeable reactive barrier (BACP), integrating biological electrochemical system (BES) with permeable reactive barrier (PRB), to address the degradation of p-CNB in the groundwater. BACP efficiently accelerated the formation of biofilms on both the anode and cathode using the polar periodical reversal method, proving more conducive to biofilm development. Notably, BACP demonstrated a remarkable p-CNB removal efficiency of 94.76 % and a dechlorination efficiency of 64.22 % under a voltage of 0.5 V, surpassing the results achieved through traditional electrochemical and biological treatment processes. Cyclic voltammetric results highlighted the primary contributing factor as the synergistic effect between the bioanode and biocathode. It is speculated that this system primarily relies on bioelectrocatalytic reduction as the predominant process for p-CNB removal, followed by subsequent dechlorination. Furthermore, electrochemical and microbiological tests demonstrated that BACP exhibited optimal electron transfer efficiency and selective microbial enrichment ability under a voltage of 0.3–0.5 V. Additionally, we investigated the operational strategy for initiating BACP in engineering applications. The results showed that directly introducing BACP technology effectively enhanced microbial film formation and pollutant removal performance.

AWGC2023 cachexia consensus as a valuable tool for predicting prognosis and burden in Chinese patients with cancer.

The Asian Working Group for Cachexia (AWGC) proposed the first consensus report on diagnostic criteria for cachexia in Asians in 2023. However, the current consensus lacks cohort evidence to validate its effectiveness and practicality. We aimed to explore the value of the AWGC2023 criteria for predicting the prognosis and medical burden of patients with cancer through a retrospective post hoc cross-sectional analysis of the Investigation on Nutrition Status and its Clinical Outcome of Common Cancers (INSCOC) project in China. Cox regression analyses were performed to assess the independent association between cachexia and long-term survival. We utilized C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), inflammatory burden index (IBI), albumin (ALB) and Glasgow prognostic score (GPS) as diagnostic markers for cachexia, designating them as CRP-based cachexia, NLR-based cachexia, IBI-based cachexia, ALB-based cachexia and GPS-based cachexia, respectively. Additionally, we diagnosed cachexia using body mass index (BMI) cutoff values of 18.5, 20, 21 and 22kg/m2, respectively, and subsequently compared their prognostic predictive value through Harrell's concordance index (C-index). Logistic regression models were used to assess the association between cachexia and medical burden. A total of 5426 patients with cancer were enrolled in this study. Cox regression analysis confirmed that cachexia based on the AWGC2023 criteria was an independent predictor of long-term survival in patients with cancer. Patients with cachexia had significantly poorer long-term survival than patients without cachexia (66.4% vs. 49.7%, P<0.001). Inflammatory biomarker-based cachexia was as an independent predictor of prognosis in patients with cancer, with inflammatory burden index (IBI)-based cachexia demonstrating the optimal prognostic discriminatory ability. The C-index indicated that cachexia based on BMI cutoff values of 18.5, 20, and 22kg/m2 did not perform as well as a BMI cutoff value of 21kg/m2. Logistic regression models revealed that using the AWGC2023 criteria, patients with cachexia had a 16.6% higher risk of prolonged hospitalization and a 16.0% higher risk of high medical expenses than patients without cachexia. The AWGC2023 criteria represent a valuable tool for predicting survival and medical burden among Chinese patients with cancer. Encouragement for further validation in other Asian populations is warranted for the AWGC2023 criteria.

A Self-Learning Hyper-Heuristic Algorithm Based on a Genetic Algorithm: A Case Study on Prefabricated Modular Cabin Unit Logistics Scheduling in a Cruise Ship Manufacturer.

Hyper-heuristic algorithms are known for their flexibility and efficiency, making them suitable for solving engineering optimization problems with complex constraints. This paper introduces a self-learning hyper-heuristic algorithm based on a genetic algorithm (GA-SLHH) designed to tackle the logistics scheduling problem of prefabricated modular cabin units (PMCUs) in cruise ships. This problem can be regarded as a multi-objective fuzzy logistics collaborative scheduling problem. Hyper-heuristic algorithms effectively avoid the extensive evaluation and repair of infeasible solutions during the iterative process, which is a common issue in meta-heuristic algorithms. The GA-SLHH employs a genetic algorithm combined with a self-learning strategy as its high-level strategy (HLS), optimizing low-level heuristics (LLHs) while uncovering potential relationships between adjacent decision-making stages. LLHs utilize classic scheduling rules as solution support. Multiple sets of numerical experiments demonstrate that the GA-SLHH exhibits a stronger comprehensive optimization ability and stability when solving this problem. Finally, the validity of the GA-SLHH in addressing real-world decision-making issues in cruise ship manufacturing companies is validated through practical enterprise cases. The results of a practical enterprise case show that the scheme solved using the proposed GA-SLHH can reduce the transportation time by up to 37%.

基于势场蚁群算法的农业仓库物流搬运机器人路径优化研究

In the layout of modern agricultural warehouse logistics handling industry, it is an inevitable way to realize industrial upgrading by replacing people with mobile robots. Aiming at the problems that the existing obstacle avoidance control algorithm of agricultural handling robot is easy to fall into local optimal solution, and the operation process of agricultural warehouse logistics handling robot is prone to collision, the obstacle avoidance control of agricultural warehouse logistics handling robot is studied, and a control algorithm based on improved potential field ant colony is proposed. The moving trajectory of the agricultural warehouse logistics handling robot during the handling process is studied, and the spatial kinematics equation of the robot is given. The ant colony algorithm is used to optimize the classical artificial potential field algorithm to improve the global optimization ability and balance the interaction between gravity and repulsion. In the aspect of local area obstacle avoidance of agricultural storage and handling robots, the artificial potential field is optimized twice based on the strategy gradient algorithm. By analyzing the probability of the next action command, the randomness of the travel path selection when multiple robots work at the same time is improved. After testing, the path of the proposed control algorithm is the shortest, and under the condition of complex path planning, the number of collisions between robots is also significantly less than that of the traditional obstacle avoidance control algorithm. The practical application can meet the needs of improving the efficiency of warehouse logistics management.

MOF derived hierarchical α-Bi2O3-BiVO4-CuFe2O4 multijunction heterostructure with conjugated S-scheme charge mobilization: Photocatalytic decontamination study, toxicity assessment and mechanistic elucidation

A series of hierarchical α-Bi2O3-BiVO4-CuFe2O4 multijunction heterostructure was designed by integrating one-pot MOF derived Bi2O3-BiVO4 microrods with CuFe2O4 nanosheets. The MOF-derived route afforded Bi2O3-BiVO4 with interconnecting porous architecture. Comprehensive investigations revealed preservation of crystalline phases, optimal light harvesting ability, higher lifetime, large electrochemically active surface area and improved charge dynamics. The heterostructure efficiently performed the photo-degradation of potentially toxic and mutagenic mesotrione (MTE) herbicide with rates 6–12 times greater than the parent semiconductors. The photo-degraded end products displayed profoundly less acute toxicity, bioaccumulation factor and mutagenic nature than parent MTE as analyzed by QSAR protocol. The heterostructure was equally effective for complete photo-inactivation of E. coli bacteria within 60 min of irradiation. SEM, AFM height profile and confocal microscopic investigation provided crucial information about the photo-inactivation process. A conjugated S-scheme electron transfer mechanism was proposed based on detailed band structure analysis to elucidate the improved activity of the multijunction photocatalyst.

PSAO: An enhanced Aquila Optimizer with particle swarm mechanism for engineering design and UAV path planning problems

Metaheuristic algorithms have become increasingly significant in solving complex optimization problems. To address the limitations of the original Aquila Optimizer (AO), such as insufficient local exploitation ability, low optimization precision, and slow convergence rate, an enhanced Aquila Optimizer (PSAO) for global optimization has been proposed. PSAO uses a better ergodic good point set to initialize the Aquila population and modifies the search method by employing the golden sine operator and the mechanism of self-learning and social learning based on particle swarm, followed by designing a nonlinear balance factor γ as the switching condition of the algorithm. The simulation experiments on benchmark functions and CEC2017 functions have verified that the PSAO has better global optimization ability and stronger robustness compared with other intelligent algorithms. Meanwhile, the contribution of each component that belongs to PSAO has been validated by ablation experiments for the CEC2022 test functions. To further illustrate the practical application potential of PSAO, PSAO is successfully applied to four typical engineering design problems, three of which reach the best fitness values. In addition, utilizing PSAO to solve the UAV trajectory planning problem by considering the objectives of trajectory length, altitude and corner of the flight process, the total flight cost is reduced by 60.22 %, 27.94 %, and 22.41 % compared with AO, PSO and Gold-SA, respectively, which proves its applicability and superiority in solving the real optimization problems.

K-Means text clustering method based on Decision Grey Wolf Optimization

Aiming at the problem that the traditional algorithm is easy to fall into local optimum in the process of text clustering, which leads to inaccurate text clustering results, a text clustering method based on decision grey wolf optimization K-Means is proposed to cluster the text data set and the standard UCI data set respectively. Afterword segmentation, stop words removal, feature extraction, and text vectorization of text data, the powerful optimization ability of the Decision Gray Wolf Optimization (DGWO) algorithm is used for global optimization, and the clustering center in K-Means algorithm is replaced by the location of wolves. The position of the wolf group is updated by iterative optimization to obtain the optimal clustering center, to perform text clustering. The experimental results show that compared with the traditional method, the precision, recall, and F-Measure of the text data clustering are improved by 49.22%, 51.15%, and 48.98% respectively. The precision, recall, and F-Measure of UCI data clustering are increased by 23.92%, 25.40%, and 24.70% respectively, and the text clustering results are more reliable.

A hybrid neural combinatorial optimization framework assisted by automated algorithm design

In recent years, the application of Neural Combinatorial Optimization (NCO) techniques in Combinatorial Optimization (CO) has emerged as a popular and promising research direction. Currently, there are mainly two types of NCO, namely, the Constructive Neural Combinatorial Optimization (CNCO) and the Perturbative Neural Combinatorial Optimization (PNCO). The CNCO generally trains an encoder-decoder model via supervised learning to construct solutions from scratch. It exhibits high speed in construction process, however, it lacks the ability for sustained optimization due to the one-shot mapping, which bounds its potential for application. Instead, the PNCO generally trains neural network models via deep reinforcement learning (DRL) to intelligently select appropriate human-designed heuristics to improve existing solutions. It can achieve high-quality solutions but at the cost of high computational demand. To leverage the strengths of both approaches, we propose to hybrid the CNCO and PNCO by designing a hybrid framework comprising two stages, in which the CNCO is the first stage and the PNCO is the second. Specifically, in the first stage, we utilize the attention model to generate preliminary solutions for given CO instances. In the second stage, we employ DRL to intelligently select and combine appropriate algorithmic components from improvement pool, perturbation pool, and prediction pool to continuously optimize the obtained solutions. Experimental results on synthetic and real Capacitated Vehicle Routing Problems (CVRPs) and Traveling Salesman Problems(TSPs) demonstrate the effectiveness of the proposed hybrid framework with the assistance of automated algorithm design.

NIGA: A Novel Method for Investigating the Attacker–Defender Model within Critical Infrastructure Networks

The field of infrastructure security has garnered significant research attention. By integrating complex network theory with game theory, researchers have proposed many methods for studying the interactions between the attacker and the defender from a macroscopic viewpoint. We constructed a game model of infrastructure networks to analyze attacker-defender confrontations. To address the challenge of finding the Nash equilibrium, we developed a novel algorithm—node-incremental greedy algorithm (NIGA)—which uses less strategy space to solve the problem. The experiments performed further showed that NIGA has better optimization ability than other traditional algorithms. The optimal defense strategies under different conditions of initial strategy ratios and attacker-defender resources were analyzed in this study. Using intelligent computing to solve the Nash equilibrium is a new approach by which for researchers to analyze attacker-defender confrontations.

Medical image segmentation approach based on hybrid adaptive differential evolution and crayfish optimizer

Image segmentation plays a pivotal role in medical image analysis, particularly for accurately isolating tumors and lesions. Effective segmentation improves diagnostic precision and facilitates quantitative analysis, which is vital for medical professionals. However, traditional segmentation methods often struggle with multilevel thresholding due to the associated computational complexity. Therefore, determining the optimal threshold set is an NP-hard problem, highlighting the pressing need for efficient optimization strategies to overcome these challenges. This paper introduces a multi-threshold image segmentation (MTIS) method that integrates a hybrid approach combining Differential Evolution (DE) and the Crayfish Optimization Algorithm (COA), known as HADECO. Utilizing two-dimensional (2D) Kapur’s entropy and a 2D histogram, this method aims to enhance the efficiency and accuracy of subsequent image analysis and diagnosis. HADECO is a hybrid algorithm that combines DE and COA by exchanging information based on predefined rules, leveraging the strengths of both for superior optimization results. It employs Latin Hypercube Sampling (LHS) to generate a high-quality initial population. HADECO introduces an improved DE algorithm (IDE) with adaptive and dynamic adjustments to key DE parameters and new mutation strategies to enhance its search capability. In addition, it incorporates an adaptive COA (ACOA) with dynamic adjustments to the switching probability parameter, effectively balancing exploration and exploitation. To evaluate the effectiveness of HADECO, its performance is initially assessed using CEC’22 benchmark functions. HADECO is evaluated against several contemporary algorithms using the Wilcoxon signed rank test (WSRT) and the Friedman test (FT) to integrate the results. The findings highlight HADECO’s superior optimization abilities, demonstrated by its lowest average Friedman ranking of 1.08. Furthermore, the HADECO-based MTIS method is evaluated using MRI images for knee and CT scans for brain intracranial hemorrhage (ICH). Quantitative results in brain hemorrhage image segmentation show that the proposed method achieves a superior average peak signal-to-noise ratio (PSNR) and feature similarity index (FSIM) of 1.5 and 1.7 at the 6-level threshold. In knee image segmentation, it attains an average PSNR and FSIM of 1.3 and 1.2 at the 5-level threshold, demonstrating the method’s effectiveness in solving image segmentation problems.

Optimization of Bayesian algorithms for multi-threshold image segmentation

The Bayesian optimization algorithm uses Bayesian networks as the probability model of its solution space. Although the research on this algorithm has steadily developed, there are still some problems in its application process, such as excessive computational complexity. To solve various problems in Bayesian algorithm, reduce its computational complexity, and enable it to better achieve image segmentation. The study chooses to improve the Bayesian algorithm on the basis of immune algorithm, and solves the problem of computational complexity by reducing the number of Bayesian network construction times, thereby improving the individual fitness of the population. Through simulation experiments, it has been shown that the average number of times the improved Bayesian algorithm reaches the optimal value is 30, which is higher than the traditional algorithm’s 20 times. Its excellent optimization ability searches for the optimal threshold to complete image segmentation. The improved Bayesian optimization algorithm based on immune algorithm can effectively reduce computational complexity, shorten computational time, and improve convergence. And applying Bayesian algorithm to image segmentation has broadened the application field of the algorithm and found new exploration directions for image segmentation.

Artificial bee colony optimization algorithm with full dimensional updating strategy and its application

For the low accuracy and slow convergence speed of artificial bee colony(ABC) algorithm in solving complex optimization problems, an improved artificial bee colony(ABC) algorithm based on the new full dimensional updating ABC/best /1 strategy, namely FNABC was proposed in this paper. In the FNABC, for the low efficiency of one-dimensional search, the full dimensional updating search strategy and ABC/best /1 strategy were combined to design a new full dimensional updating ABC/best/1 strategy, which expanded the search space, improved the mining ability and search efficiency. Then, a new evolutionary phase is designed to balance the global search ability and local mining ability to avoid falling into local optimum and improve the convergence accuracy. Finally, the FNABC is compared with eight state-of-the-art ABC variants such as AABC, iqABC, MEABC, ABCVSS, GBABC, DFSABC, MABC-NS, MGABC in solving 12 complex functions. All functions have obtained the best optimal values among 9 algorithms. Additionally, FNABC is applied to solve a real-world train operation adjustment problem. The experiment results indicate that the FNABC has better optimization ability, scalability and robustness. It obtains the ideal train operation adjustment results.

MARes-Net: multi-scale attention residual network for jaw cyst image segmentation.

Jaw cyst is a fluid-containing cystic lesion that can occur in any part of the jaw and cause facial swelling, dental lesions, jaw fractures, and other associated issues. Due to the diversity and complexity of jaw images, existing deep-learning methods still have challenges in segmentation. To this end, we propose MARes-Net, an innovative multi-scale attentional residual network architecture. Firstly, the residual connection is used to optimize the encoder-decoder process, which effectively solves the gradient disappearance problem and improves the training efficiency and optimization ability. Secondly, the scale-aware feature extraction module (SFEM) significantly enhances the network's perceptual abilities by extending its receptive field across various scales, spaces, and channel dimensions. Thirdly, the multi-scale compression excitation module (MCEM) compresses and excites the feature map, and combines it with contextual information to obtain better model performance capabilities. Furthermore, the introduction of the attention gate module marks a significant advancement in refining the feature map output. Finally, rigorous experimentation conducted on the original jaw cyst dataset provided by Quzhou People's Hospital to verify the validity of MARes-Net architecture. The experimental data showed that precision, recall, IoU and F1-score of MARes-Net reached 93.84%, 93.70%, 86.17%, and 93.21%, respectively. Compared with existing models, our MARes-Net shows its unparalleled capabilities in accurately delineating and localizing anatomical structures in the jaw cyst image segmentation.

Self-Adaptive Resonance Technology for Wireless Power Transfer Systems to Eliminate Impedance Mismatches

Impedance mismatching can severely decrease the power transmission capacity or even invalid the control logic of wireless power transfer (WPT) systems. To eliminate the impedance mismatch caused by various factors, such as resonant parameter drift, coupling structure misalignment and control strategy, a self-adaptive mistuning correction circuit and corresponding parameter design rules are proposed to ensure the WPT system consistently operates under resonant state, accompanying the optimal power transfer ability. Moreover, a reactive compensation circuit that can create an auxiliary voltage source by the absorbed reactive power that is introduced by impedance mismatching is designed. This source can generate a new current on the coil to bring the mismatched input current back to the resonant state, according to the current superposition principle. Unlike passive impedance network that require high parameter precision and active impedance matching strategies equipped with detection and control circuits, the proposed approach can self-adaptively eliminate either undesired capacitive or inductive reactance with two additional switches and an energy-storage capacitor, improving impedance adjusting rate and providing better system robustness. The simulations and experiments are in good agreement with the theoretical analysis. This study has potential applications in harsh environments where the system impedance and coupling strength are continuously disturbed.

2D/2D Bi2WO6/C3N5 S-scheme heterojunction for highly selective production of CH4 by photocatalytic CO2 reduction under visible light

Photocatalytic CO2 reduction is a practical solution to the energy dilemma and environmental damage caused by greenhouse gases, and it is very important to explore high-efficiency photocatalysts. In this study, a novel Bi2WO6/C3N5 step(S)-scheme heterojunction was successfully constructed and applied for CO2 photoreduction. The 2D/2D structure showed excellent photocatalytic properties, with methane production reaching 1.976 μmol·g−1·h−1 and selectivity reaching 100 % under 5 hours of visible light irradiation. The result indicates that combining Bi2WO6 and C3N5 can promote interfacial charge separation and maintain the optimal reducing ability of photogenerated electrons. Based on experimental and theoretical calculations, we characterized the reaction mechanism and heterojunction formation mechanism. This study offers a novel approach to improve the selectivity and photocatalytic efficiency of CO2 reduction products.

Photovoltaic MPPT based on improved Cuckoo algorithm

Abstract Photovoltaic power generation is susceptible to external factors such as light, resulting in a reduction in the actual efficiency of photovoltaic power generation. However, the traditional maximum power point tracking technology has the characteristics of slow convergence speed and poor accuracy. In order to improve the above problems, a new photovoltaic maximum power control algorithm with an improved cuckoo algorithm is proposed. By adjusting the iterative step size in the cuckoo algorithm and changing the probability of finding the bird nest, the iterative convergence speed is accelerated, and the global optimization ability is increased. Finally, the algorithm is applied to the maximum power tracking, and the simulation model is built on the Matlab/Simulink platform. After comparing the standard Cuckoo algorithm with the improved Cuckoo algorithm under static and dynamic conditions, the simulation results show that the improved Cuckoo algorithm has more advantages in maximum power tracking speed and accuracy.

Multi-Adaptive Strategies-Based Higher-Order Quantum Genetic Algorithm for Agile Remote Sensing Satellite Scheduling Problem.

The agile remote sensing satellite scheduling problem (ARSSSP) for large-scale tasks needs to simultaneously address the difficulties of complex constraints and a huge solution space. Taking inspiration from the quantum genetic algorithm (QGA), a multi-adaptive strategies-based higher-order quantum genetic algorithm (MAS-HOQGA) is proposed for solving the agile remote sensing satellites scheduling problem in this paper. In order to adapt to the requirements of engineering applications, this study combines the total task number and the total task priority as the optimization goal of the scheduling scheme. Firstly, we comprehensively considered the time-dependent characteristics of agile remote sensing satellites, attitude maneuverability, energy balance, and data storage constraints and established a satellite scheduling model that integrates multiple constraints. Then, quantum register operators, adaptive evolution operations, and adaptive mutation transfer operations were introduced to ensure global optimization while reducing time consumption. Finally, this paper demonstrated, through computational experiments, that the MAS-HOQGA exhibits high computational efficiency and excellent global optimization ability in the scheduling process of agile remote sensing satellites for large-scale tasks, while effectively avoiding the problem that the traditional QGA has, namely low solution efficiency and the tendency to easily fall into local optima. This method can be considered for application to the engineering practice of agile remote sensing satellite scheduling for large-scale tasks.

Optimization research on multi-trip distribution of reverse logistics terminal for automobile scrap parts under the background of sustainable development strategy

To effectively solve the reverse logistics distribution problem caused by the increasing number of scrapped parts in the automotive market, this study constructs a multi-trip green vehicle routing problem model with time windows by comprehensively considering the coordination between carbon dioxide emissions and cost efficiency. A hybrid adaptive genetic algorithm is proposed to solve this problem, featuring innovative improvements in the nearest neighbor rule based on minimum cost, adaptive strategies, bin packing algorithm based on the transfer-of-state equation, and large-scale neighborhood search. Additionally, to efficiently obtain location data for supplier factory sites in the distribution network, a coordinate extraction method based on image recognition technology is proposed. Finally, the scientific validity of this study is verified based on the actual case data, and the robust optimization ability of the algorithm is verified by numerical calculations of different examples. This research not only enriches the study of green vehicle routing problems but also provides valuable insights for the industry to achieve cost reduction, efficiency enhancement, and sustainable development in reverse logistics.

Disclosing the influence mechanism of facet-dependent pyrite photo-activation and photo-dissolution processes on the reduction of Cr(VI)

The photo-activation and photo-dissolution processes of pyrite (FeS2) can affect the environmental behavior of the co-existing hexavalent chromium (Cr(VI)). But the photochemical performance of FeS2 is intimately dependent on its exposed facets. Herein, FeS2 nanosheets (FeS2 NS) and FeS2 nanocubes (FeS2 NC) with the dominant exposed facets of (001) and (210)/(100) respectively are prepared. The more Fe3+, Fe2+, and SO42− are released in the FeS2 NS system than the other system due to its more excellent generation ability of photogenerated electrons and reactive oxygen species. The higher surface energy on (001) facet leads to the faster dissolution rate of FeS2 NS. Due to the optimal production ability of photogenerated electrons and Fe2+ of (001) facet, the much higher Cr(VI) elimination efficiency in the FeS2 NS system is observed than that in the FeS2 NC (72.8%) system within 120 min. This work could help to unveil the influence of FeS2 on the fate of Cr(VI) in surface environment, and offer a theoretical support to clarify the influence of heavy metal ions on the iron sulfide minerals.