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The multistage centrifugal pump is the critical component of mineral resources lifting in deep-sea mining. The reflux of nodules in the lifting pipe caused by the emergency pump stop can easily cause the pump to clog. In this paper, coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM co-simulations) are used to clarify the solid-liquid two-phase flow in two-stage centrifugal pumps under different particle sizes (10–20, 20–30, 30–40, 40–50 mm) with constant particle concentration. The movement and accumulation behaviour of particles in different flow fields (pipeline to pump, the first to the second pump stage) is investigated. Meanwhile, the effect of particle size and particle reflux velocity on the blockage of the flow channel in the pump was investigated. Particle accumulation in the pump was observed to determine the key factors affecting the pump’s reflux capacity. The residual mass of particles in the pump at different particle sizes was counted. Simultaneously, the percentage of residual mass of 10–20 mm particles in the pump was compared between the experiment and the simulation with an acceptable tolerance of within 10%. In addition, pressure changes in the blockage-prone section were also investigated. A comparison between experiments and simulations verifies the consistency of the trend on the pump inlet pressure when clogged with 50 mm particles. It was found that larger particles in the range of 10–30 mm can better ensure the pump’s reflux performance.

This work identifies spatial–temporal patterns of marine species biodiversity in the Norwegian, Greenland and Barents Seas and provides specific information in Norway for Environmental Impact Assessments and Statements about area‐based indices for biodiversity. The opening of the Norwegian Extended Continental Shelf for deep‐sea mining is a currently relevant topic for environmental management, as strategies to minimize mining impacts and delimit key zones for ecological preservation have been widely advised. A quality control procedure covering temporal and spatial scales on open‐source biodiversity data was applied, including the compilation of marine species from the archives of the Norwegian North‐Atlantic Expedition 1876–1878. Here, we present biodiversity patterns for 10,505,496 marine occurrences from 1876 to 2025 (149 years). Data occurrences were classified into two main datasets (shallow, < 500 m and deep ≥ 500 m) and two sub‐datasets for each (planktonic and benthic). 97% of the total were classified in the first main and 3% in the second main. On map view and out of 122,955 grid cells, 32,274 and 15,528 encompass data from the shallow and deep datasets, respectively, with different degrees of coverage inside; most frequently, grid cells with 1 to 10 occurrences. Data is mainly planktonic (20,098 grid cells for shallow‐planktonic and 3127 grid cells for deep‐planktonic). Peaks of species richness occur from southern to northern latitudes, even with evidently reduced values for species occurrences and abundances at certain latitudes. We conclude that knowledge gaps of benthic biodiversity in the Norwegian deep‐sea mining opening area are huge. The cumulative curve of species richness reveals that species identities, included in deep‐sea data, are not sufficient to quantify area‐based biodiversity indices in the species pool. Our findings are congruent with the need to contemplate data from deeper areas for decision‐making at different spatial–temporal windows, especially considering the granting of deep‐sea mining licenses. To identify spatial–temporal patterns of marine species biodiversity and to provide specific information in Norway for Environmental Impact Assessments and Statements, we present biodiversity patterns for marine occurrences from 1876 to 2025 (149 years). The knowledge gaps of benthic biodiversity in the Norwegian deep‐sea mining opening area are huge, and species identities included in deep‐sea data are not sufficient to quantify area‐based biodiversity indices in the species pool.

The objective of this study is to identify the feasibility of using rhamnolipid biosurfactant to remediate heavy metals contained in manganese nodules collected from the Clarion-Clipperton Fracture Zone, Pacific Ocean. Deep-sea manganese nodules may represent one of the most important future natural resources for heavy metals due to the depletion of resources on land. Since international marine environment guidelines for deep-sea mining will be set up by international organisations in the 2020s, remediation technologies are urgently required for deep-sea mining tailings. We show that rhamnolipid biosurfactant is an environmentally friendly substance and can be successfully used for the remediation of heavy metals in deep-sea mining tailings under various reaction conditions. Rhamnolipids therefore represent a useful extracting agent for heavy metals in deep-sea mining tailings. The removal of nickel (Ni), copper (Cu), and cadmium (Cd) would be enhanced in the presence of rhamnolipids with specific reaction times and concentrations. Future actual remediation technologies should be developed using rhamnolipid biosurfactant on the basis of these results.

Three-dimensional path planning is instrumental in path decision making and obstacle avoidance for deep-sea mining vehicles (DSMV). However, conventional particle swarm algorithms have been prone to trapping in local optima and have slow convergence rates when applied to underwater robot path planning. In order to secure a safe and economical three-dimensional path for the DSMV from the mining area to the storage base in connection with innovative mining system, this paper proposes a multi-objective optimization algorithm based on improved particle swarm optimization (IPSO) path planning. Firstly, we construct an unstructured seabed mining area terrain model with hazardous obstacles. Consequently, by considering optimization objectives such as the path length, terrain undulation, comprehensive energy consumption, and crawler slippage rate, we convert the path planning problem into a multi-objective optimization problem, constructing a multi-objective optimization mathematical model. Following that, we propose an IPSO algorithm to tackle the multi-objective non-linear optimization problem, which enables global optimization for DSMV path planning. Finally, we conduct a comprehensive set of experiments using the MATLAB simulation platform and compare the proposed method with existing advanced methods. Experimental results indicate that the path planned by the IPSO exhibits superior performance in terms of path length, terrain undulation, energy consumption, and safety.

Abyssal plains of the Clarion Clipperton Fracture Zone (CCZ) in the NE Pacific Ocean probably harbour one of the world’s most diverse ecosystems. Gaining a basic understanding of the mechanisms underlying the evolution and persistence of CCZ biodiversity in terms of biogeography and connectivity has both scientific merit and informs the development of policy related to potential future deep-sea mining of mineral resources at an early stage in the process. Existing archives of polychaetes and isopods were sorted using a combined molecular and morphological approach, which uses nucleotide sequences (cytochrome c oxidase subunit I (COI)) and morphological information to identify appropriate sample sets for further investigations. Basic patterns of genetic diversity, divergence and demographic history of five polychaete and five isopod species were investigated. Polychaete populations were found to be genetically diverse. Pronounced long- and short-distance dispersal produces large populations that are continuously distributed over large geographic scales. Although analyses of isopod species suggest the same, spatial genetic structuring of populations do imply weak barriers to gene flow. Mining-related, large-scale habitat destruction has the potential to impact the continuity of both isopod and polychaete populations as well as their long-term dispersal patterns, as ecosystem recovery after major impacts is predicted to occur slowly at evolutionary time scales.

Deep-sea mining vehicles (DSMVs) are highly prone to sinking and slippage when traveling on extremely soft seafloor sediments. In addition, DSMVs can be vulnerable to dangerous situations such as overturning due to the non-homogeneous characteristic of the seafloor sediments, the heavy loads carried by DSMVs, and the complex and varied topography of the seafloor. When the terrain is uneven, four-tracked DSMVs can show excellent traveling abilities and safety performances compared with conventional dual-tracked vehicles, thus having a broad range of applications. Consequently, modeling and simulation of a four-tracked DSMV are essential for the study of DSMV traveling performance. To enhance adaptability to uneven terrain, the tracks are designed to be rotatable. First, a multi-body dynamics model is built in the Recurdyn software based on the actual structural properties of a specially designed four-tracked DSMV prototype. Then, the model’s forces are modified to reflect the actual circumstances of seafloor travel. Applying a more accurate shear model, a user subroutine is written to modify the track–soil force. Moreover, internal resistance and water resistance are considered and applied to the model in the form of external loads. Then, based on the multi-pass effect, the track–soil force to the rear track is modified. Moreover, considering the relationship between soil forces and velocity, a velocity coefficient is summarized and added to the resistance estimation equation. Consequently, a more realistic dynamic model of the mining vehicle has been developed. On this basis, simulations of straight-line travel on flat ground are performed. In addition, to investigate the effects of rotatable tracks, a straight-line travel simulation with tracks fixed is also performed. By analyzing the simulation results, the motion features and dynamic characteristics of a four-tracked DSMV with rotatable tracks when traveling in a straight line on flat ground can be studied.

The discharge of sediment plumes, which occurs mainly in the two depth zones, has a critical impact on assessing the deep-sea environment. Therefore, it is necessary to establish the corresponding physical oceanography for the evolution of these sediment plumes. For a more accurate evolution estimation of the plumes, the model in this research is concerned with the dynamic interaction between the deep-sea mining vehicle (DSMV) and the sediment plumes on small scales (t ≤ 2 s), contributing to a focus on the vital physical mechanics of controlling the extent of these plumes. The sediment concentration and particle trajectories of the plume emissions were determined using the Lagrangian discrete phase model (DPM). The results show that (1) the wake structure of the DSMV wraps the plume vortex discharged from the rear of the vehicle and inhibits the lateral diffusion of the plume, (2) the length of the entire wake (Lw) increases exponentially as the relative discharge velocity of the plume (U*) increases, where U* is defined as the dimensionless difference between the traveling velocity of the DSMV and the discharge velocity of the plume, and (3) at the same traveling speed of the DSMV and U* less than 0.75, the dispersion of the sediment particles in the early discharge stage of the plume does not vary with the plume discharge rate. This will be beneficial for the more accurate monitoring of ecological changes in deep-sea mining activities and provide theoretical guidance for the green design of DSMVs.

The vortex-induced vibration (VIV) dynamics of commercial-scale deep-sea mining risers with complex component arrangements (pumps, buffer stations, buoyancy modules) remain insufficiently explored, especially for 6000 m systems with nonlinear tension. This study investigates VIV control strategy by adjusting tension for a nonlinear riser system using the Iwan-Blevins wake oscillator model integrated with Morison equation-based analysis. An analytical model incorporating four typical current profiles was established to quantify the dynamic response under different flow velocities, internal flow density, and structural parameters. Increased buffer station mass effectively suppressed drift distance (over 35% reduction under specific conditions) by regulating axial tension. Dynamic comparisons demonstrated distinct VIV energy distribution patterns under different current conditions. Spectral analysis revealed that the vibration follows Strouhal vortex shedding lock-in principles. Spatial modal differentiation was observed due to nonlinear variations in velocity profiles, pipe diameters, and axial tension, accompanied by multi-frequency resonance, coexistence of standing and traveling waves, and broadband resonance with amplitude surges under critical velocities (1.75 m/s in Current-B). This study proposes to control the VIV amplitude by adjusting internal flow density and buffer mass, which is proved effective for reducing vibrations in upper (0–2000 m) risers. It validates vibration amplitude and frequency control through current velocity, buffer mass and slurry density regulation in a nonlinear riser system.

Ensuring the safe landing of deep-sea mining vehicles (DSMVs) on soft seabed sediments is critical for the stability and operational reliability of subsea mineral extraction. However, deep-sea sediments, particularly in polymetallic nodule regions, are characterized by low shear strength, high compressibility, and rate-dependent behavior, posing significant challenges for full-scale experimental investigation and predictive modeling. To address these limitations, this study develops a high-fidelity finite element simulation framework based on the Coupled Eulerian–Lagrangian (CEL) method to model the landing and penetration process of full-scale DSMVs under various geotechnical conditions. To overcome the high computational cost of FEM simulations, a data-driven surrogate model using the random forest algorithm is constructed to predict the normalized penetration depth based on key soil and operational parameters. The proposed hybrid FEM–ML approach enables efficient multiparameter analysis and provides actionable insights into the complex soil–structure interactions involved in DSMV landings. This methodology offers a practical foundation for engineering design, safety assessment, and descent planning in deep-sea mining operations.

In order to improve the hydraulic performance of a deep-sea mining pump, this research proposed a multi-objective optimization strategy based on the computational fluid dynamics (CFD) numerical simulation, genetic algorithm back propagation (GABP) neural network, and non-dominated sorting genetic algorithm-III (NSGA-III). Significance analysis of the impeller and diffuser parameters was conducted using the Plackett–Burman experiment to filter out the design variables. The optimum Latin hypercube sampling method was used to produce sixty sample cases. The GABP neural network was then utilized to establish an approximate model between the pump’s hydraulic performance and design variables. Finally, the NSGA-III was utilized to solve the approximation model to determine the optimum parameters for the impeller and diffuser. The results demonstrate that the GABP neural network can accurately forecast the deep-sea mining pump’s hydraulic performance, and the NSGA-III global optimization is effective. On the rated clear water conditions, the optimized pump has a 14.65% decrease in shaft power and a 6.04% increase in efficiency while still meeting the design requirements for the head. Under rated solid-liquid two-phase flow conditions, the head still meets the design requirements, the shaft power is decreased by 15.64%, and the efficiency is increased by 6.00%.