Deep reinforcement learning floating hydrogen production equipment layout optimization method under three-dimensional space constraint

Abstract

The invention belongs to the technical field of ocean engineering and floating hydrogen production platforms. In order to solve the problem that gravity center balance and safety spacing are difficult to consider in the layout of floating platform equipment, a deep reinforcement learning algorithm is adopted to construct a three-dimensional constraint matrix, and the layout is dynamically optimized in combination with a reward function. A virtual collision detection and center-of-gravity shift prediction cooperation mechanism is innovatively designed, and the position of key equipment is dynamically adjusted while it is guaranteed that the safety distance between the equipment is larger than or equal to 1.5 m. Through simulation verification, the space utilization rate of equipment is increased by 20%, the initial stability height of the platform is increased by 15%, the coverage area of a dangerous area is reduced by 30%, the resonance risk is remarkably reduced, and an efficient solution is provided for intensive design of the floating platform.

Description

Technical Field

[0001] The present invention belongs to the technical field of marine engineering and floating hydrogen production platforms, and in particular relates to a deep reinforcement learning floating hydrogen production equipment layout optimization method under three-dimensional space constraints. Background Art

[0002] As an important component of clean and renewable energy, offshore wind power is experiencing unprecedented development opportunities. Compared to onshore wind power, offshore wind power offers significant advantages, including abundant wind energy resources, minimal land occupation, and minimal environmental impact. Especially in deep-sea areas, its development potential is enormous. As key equipment for developing deep-sea wind energy resources, floating wind power platforms are able to overcome water depth limitations and adapt to complex sea conditions, becoming the primary development direction for future offshore wind power technology. As floating wind power technology matures, its integration with marine engineering, hydrogen production, and other fields is becoming increasingly in-depth. Constructing an integrated "offshore wind power-hydrogen production-storage-transportation" system has become an important path to achieving comprehensive offshore energy development.

[0003] However, large-scale development of offshore wind power faces significant challenges in power transmission and grid connection. Because offshore wind farms are typically located far from the coast in deep waters, traditional HVDC transmission technology presents challenges such as high construction costs, technical difficulties, and significant transmission losses. Furthermore, the intermittent and volatile nature of wind power output complicates grid scheduling and makes it difficult to ensure power quality. Furthermore, in some specific locations, such as islands and coastal industrial bases, weak grid infrastructure makes it difficult to directly accommodate large-scale offshore wind power. To address these challenges, a power storage and transportation technology approach centered around seawater electrolysis to produce hydrogen has emerged. By integrating hydrogen production equipment—including electrolyzers, compressors, and hydrogen storage tanks—on a floating platform, offshore wind power is converted into hydrogen for storage. This approach not only enables large-scale electricity storage but can also be transported to land via liquid hydrogen transport or pipelines, forming a "green electricity to green hydrogen" energy conversion chain. This technology approach not only overcomes grid access limitations but also provides a new avenue for the diversified utilization of offshore energy, aligning with the global trend of energy transition.

[0004] Currently, the design of floating offshore hydrogen production platforms faces numerous technical and theoretical challenges. Traditional approaches to optimizing equipment layouts struggle to account for the dynamic coupling effects of multi-dimensional constraints. On the one hand, floating platforms experience six degrees of freedom (DOF) motion under environmental loads such as waves and currents. Equipment layouts must meet stringent center-of-gravity balance requirements to ensure platform stability and safety. For example, the overall longitudinal center-of-gravity offset must be controlled within 3% of the ship's length, and the heeling restoring moment must exceed 15 MN m. On the other hand, hydrogen production equipment faces complex functional dependencies and safety constraints. For example, the spacing between the hydrogen production reactor and the proton exchange membrane must be precisely controlled within 10 m ± 0.3 m, and the equipment safety spacing must be no less than 1.5 m ± 0.05 m. Physical effects such as vibration coupling and thermal radiation must also be considered. Existing technologies mostly employ static layout methods, lacking dynamic analysis of platform motion, equipment dynamics, and multi-physics coupling. This can lead to layout solutions experiencing excessive center-of-gravity offsets, increased equipment collision risk, and reduced functional efficiency in real-world sea conditions. In addition, at the theoretical level, for multi-objective optimization problems under three-dimensional spatial constraints, traditional algorithms find it difficult to effectively handle high-dimensional constraint tensors and complex reward function systems, resulting in the optimization results being unable to simultaneously meet the requirements of space utilization, stability indicators, and equipment function coordination.

[0005] In summary, the traditional floating platform design method has the following major defects in technical implementation:

[0006] 1) Traditional optimization algorithms usually do not fully consider the interaction between devices and the deep impact of physical constraints. For example, the device layout process ignores

[0007] The interactive characteristics of fluid dynamic coupling and vibration transfer function amplitude are affected, which makes the layout scheme unable to adapt to the complex multi-body dynamic environment. 2) When dealing with multi-objective optimization problems such as space utilization, stability height, and pipeline bending radius, traditional methods have low search efficiency and large solution sets.

[0008] Due to the uneven distribution problem, it is difficult to find the optimal non-dominated solution set in high-dimensional space.

[0009] 3) Existing virtual verification cannot accurately simulate the coupling effect of rigid body dynamics and fluid dynamics, resulting in deviations in the reliability verification of layout schemes. Under extreme sea conditions, traditional methods cannot accurately calculate the impact of platform motion on equipment layout. Summary of the Invention

[0010] The present invention is made in light of the problems existing in the prior art. It addresses three core challenges faced by floating hydrogen production platforms in complex marine environments: equipment layout must simultaneously meet three-dimensional spatial constraints (safety spacing, prohibited areas), dynamic stability requirements (center of gravity offset, roll recovery), and functional coupling relationships (process pipeline connection, vibration interference avoidance); traditional layout methods rely on empirical trial and error, making it difficult to quantify multi-objective conflicts (space utilization vs. stability height vs. resonance risk); and existing optimization algorithms are insufficiently capable of handling nonlinear coupling constraints (geometry-physics-function), resulting in low convergence efficiency and a high proportion of suboptimal solutions.

[0011] This paper addresses the multi-objective optimization challenge of equipment layout for floating hydrogen production platforms in complex marine environments by proposing an innovative approach that integrates dynamic modeling, physical embedding algorithms, and multi-scale verification. Traditional approaches, which address geometric constraints, static stability, and functional requirements in isolation, struggle to address the risks of dynamic instability and resonance under wave-structure-equipment coupling. This approach deeply integrates the platform dynamics equations with equipment layout constraints by establishing a spatial model with six degrees of freedom motion coupling:

[0012] Mx¨+Cx˙+Kx=F env +∑ i＝1 n m i (g+a i )

[0013] Where M is the inertia matrix including the added mass effect, a i is the acceleration response of the i-th device under wave excitation. Based on this model, a 512×256×128-dimensional hierarchical constraint tensor is constructed to dynamically encode the safety distance (≥1.5m), regional load (≤20t / m 2 ) and process correlation (such as the distance between the hydrogen production reactor and the storage tank is ≤ 10m), and the octree indexing technology is used to achieve millisecond-level constraint updates. Compared with the traditional static grid method, this model improves the spatial resolution to 0.25m 3 , the dynamic stability error of the layout scheme is reduced from 12.7% to 3.5%.

[0014] This paper deeply embeds the physical conservation laws into the reinforcement learning architecture and designs an improved MADDPG-Pro algorithm. The policy network uses the graph attention mechanism to dynamically capture the interactions between devices. The node feature update formula is:

[0015]

[0016] where ξ ij Including physical interaction indicators such as fluid dynamic coupling and thermal radiation flux, γ = 1.2m -1 is the spatial attenuation coefficient. The Critic network introduces the physical information loss term The strategy output is forced to satisfy the continuum equilibrium equations, avoiding invalid layouts that violate stress distribution laws. Experiments show that this design accelerates algorithm convergence by 40% and increases the first-pass pass rate of layout solutions from 38% with traditional DRL to 82%.

[0017] In order to achieve multi-objective collaborative optimization, this solution constructs a three-level evaluation system of "micro-meso-macro": the Hertz contact model is used to quantify the equipment collision risk (1.5R micro =-k cmax (0,Fn-5kN) 1.5 ), the mesoscopic level suppresses regional resonance based on mechanical impedance theory (Z ratio =|Z equip / Z platform |<2), and at the macro level, the initial stability height (GM≥1.5+0.05B / √D) is optimized in combination with IMO stability rules. By integrating potential flow theory, finite element and CFD multi-field solvers on the digital twin platform, millisecond-level data synchronization is achieved under the OPC UA protocol, and the NSGA-III algorithm is used to screen the Pareto frontier solution set (hypervolume index ≥0.75). Engineering applications have shown that this solution improves equipment layout efficiency by 40%, reduces the resonant area to 8.7%, and shortens the verification cycle required for ABS certification by 70%, providing a full-chain theory-algorithm-verification solution for the intensive design of deep-sea hydrogen production platforms.

[0018] To achieve the above objectives, the technical solution of the present invention is a method for optimizing the layout of a floating hydrogen production facility using deep reinforcement learning under three-dimensional spatial constraints, which is characterized by comprising the following steps:

[0019] Step 1: Construct a three-dimensional equipment layout space based on the multi-modal coupled dynamic model of the floating platform. The input parameters are: the main dimensions of the platform, including overall length, width, and depth; basic equipment properties, including mass distribution, volume, and center of mass offset tolerance; parametric material constitutive relations based on the Johnson-Cook plasticity model; and a discretized spatial model of six-degree-of-freedom motion coupling using adaptive unstructured mesh generation technology.

[0020] Step 2: Construct a hierarchical constraint tensor. The geometric constraint layer is set to equipment safety distance ≥1.5m±0.05m, equipment and structure gap ≥0.8m±0.02m; the physical constraint layer is set to regional load density ≤20t / m 2±5%, overall longitudinal center of gravity offset ≤3% Lpp ± 0.1%, heel restoring moment ≥ 15MN·m; the functional constraint layer is set to the frequency difference between the compressor vibration sensitive axis and the ambient excitation ≥ 3Hz, the tensor dimension is 512×256×128, the spatial resolution is 0.25m×0.25m×0.25m, and the octree encoding technology with level 6 LOD layer is used to realize dynamic loading of constraint conditions, with an update delay of ≤10ms;

[0021] Step 3: Design an improved multi-agent deep deterministic policy gradient algorithm. The policy network adopts the graph attention mechanism; the critic network introduces the physical information neural network architecture; the experience pool implements spatiotemporal correlation sampling, and the parameters are set as follows: learning rate Actor 0.0001 / Critic 0.0003, target network update coefficient τ = 0.008, and policy noise Ornstein-Uhlenbeck process θ = 0.15, σ = 0.2;

[0022] Step 4: Establish a multi-scale reward function system. At the micro scale, set the device-level contact force penalty: Press F when the contact force is ≥ 5kN. 1.2 ×(–0.8); at the mesoscopic scale, a regional resonance risk indicator is set: when the modal participation factor in the 6–8 Hz frequency band is greater than 0.15, a penalty of –1.2 / 0.1 is applied; at the macroscopic scale, a platform-level stability indicator is set: a reward of +0.5 is applied for every 0.1m increase in the GM value, and a penalty of –1.5 / Hz is applied when the matching error between the natural period of roll and the peak frequency of the wave energy spectrum is greater than 8%. The total reward function adopts hierarchical weighting, and the weight distribution entropy regularization coefficient λ=0.05;

[0023] Step 5: Implement high-precision virtual collision detection, using a convergence threshold of 1e –6 m, an improved GJK-EPA hybrid algorithm with a maximum number of iterations of 100, combined with the kinematic chain modeling of the device with 4 to 6 joint degrees of freedom and a maximum angular velocity of 0.1 rad / s, generates a collision potential field gradient map in real time, implements pre-action suppression on potential collision areas, and the penalty coefficient varies with the collision probability P c Piecewise function: P c <0.3 –0.5, 0.3≤P c <0.7–1.2, P c ≥0.7 –3.0;

[0024] Step 6: Perform nonlinear center of gravity prediction and compensation. Use 20-node hexahedral hybrid element, incremental finite element method with material nonlinear iteration step size of 0.01, and combine with platform damage stability criterion to calculate the center of gravity correction value ΔK G =Σ(m i Δz i ) / Σm i, set the mass sensitivity coefficient k m =1.5, when predicting G M When the value is <2.0m±0.05m, the adaptive adjustment strategy based on Lyapunov stability is triggered, with convergence time ≤90s and overshoot ≤2%;

[0025] Step 7: Using the mixed reality-digital twin verification system, with the help of the NVIDIA Omniverse physics engine, the rigid body solution accuracy is 0.001m, the fluid coupling time step is 0.01s, and the real-time data exchange software protocol based on OPC UA, the verification conditions need to include a 100-year wave H s =14m, spectral peak period T p = 16s extreme sea condition, the performance index requires that the righting moment of the layout scheme at a heel of 30° be ≥ 120% of the minimum allowable value;

[0026] In step 8, multi-objective Pareto frontier screening is implemented in the dynamic optimization stage. The optimization objective function is defined as f1 = space utilization ≥ 85%, f2 = stability height ≥ 2.5 m, and f3 = pipeline bending radius ≥ 3 times the pipe diameter. The NSGA-III algorithm is used with a population size of 200 and 3000 iterations to output a non-dominated solution set. The final solution must meet the safety requirements of CCS Rules for Classification of Offshore Floating Facilities (2023).

[0027] It is further defined that the constitutive relationship of the material described in step 1 is verified by using the Hopkinson pressure bar experimental data, combined with digital image correlation technology, and the Johnson-Cook model parameters C (strain rate sensitivity coefficient) = 0.012 and m (temperature softening index) = 1.03 are modified. The material failure criterion adopts the improved Bai-Wierzbicki model, in which the stress triaxiality η = –0.5~1.0 and the Lode parameter θ = –1~1.

[0028] Further definition, the device interaction features defined in the graph attention mechanism described in step 3 include: fluid dynamic coupling, thermal radiation influence factor, vibration transfer function amplitude, and the adjacency weight calculation uses the adaptive kernel function k = exp(–||x i –x j || 2 / (2σ 2 )), bandwidth σ=1.2m±0.1m.

[0029] It is further defined that the improved GJK-EPA hybrid algorithm described in step 5 includes the following enhanced modules: a fast repulsion stage based on conformal geometric algebra; a curvature adaptive subdivision strategy is introduced in the EPA stage; and the Hertz–Mindlin nonlinear contact model is used for contact force calculation.

[0030] It is further defined that the following nonlinear effects are considered in the incremental finite element analysis described in step 5: material rate-dependent plasticity; large deformation geometric nonlinearity; and fluid-structure interaction boundary conditions.

[0031] Further defined, a constraint handling mechanism is defined in the multi-objective optimization described in step 8: a death penalty strategy is adopted for solutions that violate hard constraints; soft constraint violations are converted into additional objective functions; and dynamic constraint relaxation is implemented.

[0032] It is further specified that the final layout solution described in Step 8 must pass three levels of verification marks, namely, CFD-based deck wave analysis; explosion shock wave overpressure simulation; and full-life fatigue analysis.

[0033] The present invention is beneficial in that:

[0034] (1) Through dynamic constraint fusion and physical embedding algorithms, the equipment layout optimization cycle is shortened by more than 40%, and the initial stability of the platform is improved by 15%-25%, meeting international standards and reducing the risk of offshore operations.

[0035] (2) An innovative multi-scale assessment system enables space utilization to reach over 85%, compresses the resonance risk area to 8.7% of the deck area, and optimizes the pipeline bending radius by 30% to 50%, taking into account both functional efficiency and structural reliability.

[0036] (3) The digital twin verification system reduces the number of physical prototype iterations by 70%, and the fatigue life of the layout scheme is increased to 1.3 times the design life. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0038] Figure 1 This is a schematic diagram of the three-dimensional structure of a floating hydrogen production platform. The reference numerals are explained as follows:

[0039] Floating platform shell (1), simulated power supply equipment (2), water supply and desalination tank (3), electrolytic water tank (4), gas-liquid balancer (5), compressor (6), hydrogen transmission pipeline (7), hydrogen storage tank (8), BOP system (9).

[0040] Figure 2 It is a technical flow chart of the present invention.

[0041] Figure 3 This is a framework diagram of the deep reinforcement learning algorithm.

[0042] Figure 4This is a working principle diagram of the standard assessment system.

[0043] Figure 5 Validate the system architecture diagram for the digital twin. DETAILED DESCRIPTION

[0044] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0045] As an embodiment of the present invention, a deep reinforcement learning floating hydrogen production equipment layout optimization method under three-dimensional space constraints, wherein the three-dimensional structure of the floating hydrogen production platform is as shown in the attached Figure 1 The general process is shown in the attached Figure 2 As shown, the specific steps include:

[0046] Step 1: Construct a three-dimensional equipment layout space based on the multi-modal coupling dynamic model of the floating platform. The input parameters are: the main dimensions of the platform, including the total length, width, and depth; the basic properties of the equipment, including mass distribution, volume, and center of mass offset tolerance; the parametric material constitutive relationship based on the Johnson-Cook plasticity model; and the use of adaptive unstructured grid generation technology to establish a discretized space model of six-degree-of-freedom motion coupling. The material constitutive relationship is verified using Hopkinson pressure bar experimental data, combined with digital image correlation technology, to modify the Johnson-Cook model parameters C (strain rate sensitivity coefficient) = 0.012 and m (temperature softening index) = 1.03. The material failure criterion adopts the improved Bai-Wierzbicki model (stress triaxiality η = -0.5 to 1.0, Lode parameter θ = -1 to 1).

[0047] Step 2: Construct a hierarchical constraint tensor. The geometric constraint layer is set to equipment safety distance ≥1.5m±0.05m, equipment and structure gap ≥0.8m±0.02m; the physical constraint layer is set to regional load density ≤20t / m 2 ±5%, overall longitudinal center of gravity offset ≤3% Lpp ± 0.1%, heel restoring moment ≥ 15MN·m; the functional constraint layer is set to the frequency difference between the compressor vibration sensitive axis and the ambient excitation ≥ 3Hz, the tensor dimension is 512×256×128, the spatial resolution is 0.25m×0.25m×0.25m, and the octree encoding technology with level 6 LOD layer is used to realize dynamic loading of constraint conditions, with an update delay of ≤10ms;

[0048] Step 3: Design an improved multi-agent deep deterministic policy gradient algorithm. The policy network adopts a graph attention mechanism. The Critic network introduces a physical information neural network architecture. The experience pool implements spatiotemporal correlation sampling. The parameters are set as follows: learning rate Actor 0.0001 / Critic 0.0003, target network update coefficient τ = 0.008, strategy noise Ornstein-Uhlenbeck process θ = 0.15, σ = 0.2. The device interaction features defined in the graph attention mechanism include: fluid dynamic coupling, thermal radiation influence factor, vibration transfer function amplitude, and the adjacency weight calculation adopts the adaptive kernel function k = exp(–||x i –x j ||2 / (2σ 2 )), bandwidth σ=1.2m±0.1m; the framework diagram of the above deep enhancement algorithm is as shown in the attached Figure 3 shown.

[0049] Step 4: Establish a multi-scale reward function system. At the micro scale, set the device-level contact force penalty: Press F when the contact force is ≥ 5kN. 1.2 ×(–0.8) calculation; at the mesoscopic scale, set the regional resonance risk index: when the modal participation factor of the 6–8 Hz frequency band is greater than 0.15, a penalty of –1.2 / 0.1 is imposed; at the macroscopic scale, set the platform stability index: a reward of +0.5 is imposed for every 0.1m increase in the GM value, and a penalty of –1.5 / Hz is imposed when the matching error between the natural period of roll and the peak frequency of the wave energy spectrum is greater than 8%. The total reward function adopts hierarchical weighting, and the weight distribution entropy regularization coefficient λ=0.05; Appendix Figure 4 Working principle diagram of the standard assessment system

[0050] Step 5: Implement high-precision virtual collision detection, using a convergence threshold of 1e –6 m, an improved GJK-EPA hybrid algorithm with a maximum number of iterations of 100, combined with the kinematic chain modeling of the device with 4 to 6 joint degrees of freedom and a maximum angular velocity of 0.1 rad / s, generates a collision potential field gradient map in real time, implements pre-action suppression on potential collision areas, and the penalty coefficient varies with the collision probability P c Piecewise function: P c <0.3 –0.5, 0.3≤P c <0.7–1.2, P c ≥0.7–3.0; the improved GJK-EPA hybrid algorithm includes the following enhancements: a fast repulsion phase based on conformal geometric algebra; a curvature-adaptive subdivision strategy introduced in the EPA phase; and a Hertz–Mindlin nonlinear contact model for contact force calculation. The incremental finite element analysis considers the following nonlinear effects: material rate-dependent plasticity; large deformation geometric nonlinearity; and fluid-structure interaction boundary conditions.

[0051] Step 6: Perform nonlinear center of gravity prediction and compensation. Use 20-node hexahedral hybrid element, incremental finite element method with material nonlinear iteration step size of 0.01, and combine with platform damage stability criterion to calculate the center of gravity correction value ΔK G =Σ(m i Δz i ) / Σm i , set the mass sensitivity coefficient k m =1.5, when predicting G M When the value is <2.0m±0.05m, the adaptive adjustment strategy based on Lyapunov stability is triggered, with convergence time ≤90s and overshoot ≤2%;

[0052] Step 7: Using the mixed reality-digital twin verification system, with the help of the NVIDIA Omniverse physics engine, the rigid body solution accuracy is 0.001m, the fluid coupling time step is 0.01s, and the real-time data exchange software protocol based on OPC UA, the verification conditions need to include a 100-year wave H s =14m, spectral peak period T p = 16s extreme sea condition, the performance index requires that the righting moment of the layout scheme at a heel of 30° should be ≥ 120% of the minimum allowable value; Figure 5 Architectural diagram of the digital twin verification system.

[0053] In step 8, during the dynamic optimization phase, multi-objective Pareto frontier screening was implemented. The optimization objective functions were defined as f1 = space utilization ≥ 85%, f2 = stability height ≥ 2.5 m, and f3 = pipeline bending radius ≥ 3 times the pipe diameter. The NSGA-III algorithm was used, with a population size of 200 and 3,000 iterations to output a non-dominated solution set. The final solution was required to meet the safety requirements of the CCS Rules for Classification of Offshore Floating Installations (2023). A constraint handling mechanism was defined in the multi-objective optimization: solutions violating hard constraints were penalized; soft constraint violations were converted into additional objective functions; and dynamic constraint relaxation was implemented. The final layout solution was required to pass three levels of verification: CFD-based deck wave analysis; explosion shock wave overpressure simulation; and lifecycle fatigue analysis.

[0054] The following further describes the implementation of the present invention in conjunction with specific examples.

[0055] Example 1

[0056] This example optimizes the layout of a hydrogen production system within a single 20-foot standard container (effective internal dimensions: 5.8m × 2.3m × 2.2m). It includes five core components: ① PEM electrolyzer (1.8m × 1.2m × 1.5m, mass: 2.8t, operating temperature: 80°C ± 5°C); ② Hydrogen storage tanks (three groups, each 0.8m × 0.8m × 2.0m, pressure: 35MPa); ③ BOP auxiliary system (1.5m × 1.0m × 1.6m, including a circulation pump and cooling device); ④ Power module (0.6m × 0.6m × 1.8m, output power: 2MW); and ⑤ Safety monitoring unit (0.5m × 0.5m × 0.5m). Prior to optimization, a traditional manual layout was used, with equipment spacing of only 0.8-1.2m. This resulted in excessive heat radiation between the electrolyzer and hydrogen storage tanks (surface temperature difference: 65°C) and blocked emergency access routes.

[0057] According to the implementation plan of the present invention, the specific implementation process includes: establishing a millimeter-level 3D model, reconstructing the point cloud using laser scanning (accuracy ±1mm), setting safety constraint parameters: the minimum distance between devices is 1.2m (in compliance with ISO 22734:20206.3.2), the temperature rise within 2m around the electrolyzer is ≤15°C (according to NFPA 2-2023 hydrogen safety regulations); deep reinforcement learning parameter configuration: Actor network layer structure 128-256-512, Critic network input includes temperature field gradient (sampling rate 10Hz), hydrogen concentration distribution (0-100%LEL); experience pool storage 10 5 The training batch size was 256. The virtual collision detection algorithm used an accurate OBB bounding box (with an error of ±2 mm) and set the electrolyzer vibration displacement threshold to ±3 mm (corresponding to a frequency of 8-12 Hz). The dynamic optimization algorithm generated a layout plan after 1500 iterations, increasing the distance between the electrolyzer and the hydrogen storage tank to 2.3 m. The power module and the BOP system were arranged in an upper and lower staggered layout (with a vertical spacing of 0.8 m).

[0058] After adopting this method, the space utilization rate increased from 58% to 81% (effective use volume 19.2m 3 Increased to 26.8m 3 ); The range of thermal radiation on the surface of the electrolytic cell is reduced by 72% (the area of ​​the high temperature zone is reduced from 4.8m 2 Down to 1.3m 2 ); the width of the emergency passage was increased from 0.6m to 1.5m (to meet OSHA 1910.106 requirements); the system resonant frequency was shifted from 9.8Hz to 14.2Hz (to avoid the BOP pump natural frequency of 10-12Hz); Safety certification time is shortened by 40% (the original 320h test is reduced to 192h).

[0059] Example 3

[0060] A 40-foot container (12.0m×2.4m×2.3m) deployed in a tropical sea area (ambient temperature 32°C, relative humidity 85%) integrates 8 devices: ① Alkaline electrolyzer (3.0m×1.8m×2.0m, hydrogen production capacity 120Nm 3 / h); ② a two-stage cooling system (temperature difference ΔT = 25°C); ③ a hydrogen purification unit (dew point –70°C); and ④ an explosion-proof control cabinet (compliant with IEC 60079). The original layout resulted in an average of 1.2 electrical equipment failures per month due to condensate accumulation.

[0061] The present invention first implements environmental modeling, adding a humidity diffusion model (discrete solution of Fick's law, 50mm grid size) to the three-dimensional constraint matrix, and setting the humidity around the electrical equipment to ≤70% RH. Environmental data is collected and improved through reinforcement learning: a new humidity gradient tensor (resolution 0.1% RH / m) is input to the critic network, and a humidity penalty term is set in the reward function (a penalty of -0.5 for every 10% RH in the over-limit area). Dynamic dehumidification path planning is then optimized to form a 0.8m / s directional airflow between the cooling system outlet and the electrical equipment (CFD verification speed deviation is ±0.05m / s).

[0062] After optimization, the condensation water area on the equipment surface was reduced by 89% (from 2.4m 2 Down to 0.26m 2 ); humidity around the electrical cabinet dropped from 82% RH to 65% RH; equipment failure interval extended from 28 days to 152 days; hydrogen purity increased from 99.95% to 99.997% (dew point -72°C); system energy efficiency ratio increased from 58% to 63% (calculated according to ISO 22734:2020).

[0063] Example 4

[0064] The hydrogen production system, consisting of three 40-foot containers (total space 36m × 2.4m × 2.3m), includes 12 pieces of equipment: ① three electrolyzers (staggered); ② compressed energy storage units (operating pressure 45MPa); ③ three-stage cooling towers; and ④ a central control station. Cross-container pipeline connections (total length limited to ≤15m) and thermal stress accumulation must be addressed.

[0065] According to the implementation method of the present invention, first, a cross-box constraint matrix is ​​established: the width of the inter-box connection channel is ≥1.0m (ISO 3874 standard), and the pipeline bending radius is ≥4 times the pipe diameter (ASME B31.3 specification); the temperature field (FLIR A655sc infrared data, accuracy of ±1°C) and the pressure pulsation spectrum (0-50Hz, resolution 0.5Hz) are embedded in the reinforcement learning state space to perform thermodynamic coupling modeling; the total pipeline length (weight 0.7) and thermal stress accumulation (weight 0.3) are minimized simultaneously, and the NSGA-II algorithm is used to screen the Pareto solution set for multi-objective optimization; the thermal displacement is calculated using ANSYS Mechanical APDL (maximum expansion 8.2mm), and the pipeline compensator layout is optimized for dynamic verification.

[0066] After optimization, the total length of cross-box pipelines was shortened from 23.4m to 14.7m (the number of elbows was reduced by 6); the peak thermal stress was reduced from 138MPa to 82MPa (lower than the ASME SA-240 316L allowable stress of 115MPa); the system startup time was shortened from 45min to 28min (preheating energy consumption was reduced by 37%); all safety clauses of the CE certification (EN 62282-3-100:2023) were passed; and the modular expansion cost was reduced by 55% (the standard interface matching rate reached 92%).

[0067] Example 5

[0068] When an existing hydrogen production container (with internal equipment fixedly installed) encounters a hydrogen leak (concentration>4%VOL), the present invention activates the emergency mode and generates an equipment emergency shutdown sequence and personnel evacuation path within 120 seconds.

[0069] After optimization by the present invention, the emergency response time is shortened from 210s in the traditional solution to 97s; the leakage isolation efficiency is improved by 68% (the diffusion speed in the hazardous area is reduced to 0.3m / s); the safety of personnel evacuation routes is improved to SIL 2 level (IEC 61508 standard); the equipment emergency shutdown sequence is optimized to reduce hydrogen release by 41%; and the system has passed the dynamic leakage control certification of Article 7.3.2 of NFPA 2-2023.

[0070] Example 6

[0071] For a 20-foot hydrogen production container (with fixed equipment layout) that has been in operation for 5 years, without changing the main structure, the present invention achieves the following: 1. adding a new membrane separation device (size 0.9m×0.7m×1.5m); 2. optimizing the original BOP system layout.

[0072] The specific implementation method of the present invention includes: 3D scanning and reconstruction: using a FARO Focus Premium laser scanner (accuracy ±1mm) to obtain existing layout point cloud data; historical data fusion: importing the equipment vibration spectrum (peak frequency 8.2Hz, 12.5Hz) from the past three years as constraints; incremental reinforcement learning: additional training (500 iterations) based on the original strategy network, with new constraints including: membrane component operating temperature ≤50°C, vibration acceleration ≤0.3g; topology optimization: using the variable density method (SIMP model, penalty factor p=3) to redesign the support frame, reducing the mass by 18%.

[0073] After technical optimization, the system volume increased by only 7% after the integration of the new equipment, while hydrogen production increased by 22%. The vibration amplitude of the original BOP system was reduced from 0.8mm to 0.35mm (ISO 10816-3 Class B compliance). The renovation period was shortened from an estimated 14 days to 6 days. Overall energy efficiency increased from 61% to 67%, and all updated terms of the EAC certification (TP TC 032 / 2013) were passed.

[0074] Standardized comparison table

[0075] Indicator Example 2 Example 3 Example 4 Example 5 Example 6 Improved space utilization +23％ +18％ +29％ N / A +7％ Safety standards compliance ISO 22734 IEC 60079 EN 62282 NFPA 2 TP TC 032 Core parameter optimization Temperature field -72% Humidity field - 65% Heat stress - 41% Response time - 54% Vibration - 56% Shortened certification cycle 40％ 35％ 50％ 60％ 45％ Improvement of energy consumption indicators +5％ +5％ +8％ N / A +6％

[0076] The specific embodiments described above are only used to specifically illustrate the spirit of the present invention, and the scope of protection of the present invention is not limited thereto. For those skilled in the art, it is of course possible to easily make other embodiments by changing, replacing or modifying the technical contents disclosed in this specification, and these other embodiments should all be included in the scope of protection of the present invention.

Claims

1. A deep reinforcement learning floating hydrogen production equipment layout optimization method under three-dimensional space constraints, characterized by The method comprises the following steps: Step 1: Construct a three-dimensional equipment layout space based on the multi-modal coupled dynamic model of the floating platform. The input parameters are: the main dimensions of the platform, including overall length, width, and depth; basic equipment properties, including mass distribution, volume, and center of mass offset tolerance; parametric material constitutive relations based on the Johnson-Cook plasticity model; and a discretized spatial model of six-degree-of-freedom motion coupling using adaptive unstructured mesh generation technology. Step 2: Construct a hierarchical constraint tensor. The geometric constraint layer is set to equipment safety distance ≥1.5m±0.05m, equipment and structure gap ≥0.8m±0.02m; the physical constraint layer is set to regional load density ≤20t / m 2 ±5%, overall longitudinal center of gravity offset ≤3% Lpp ± 0.1%, heel restoring moment ≥ 15MN·m; the functional constraint layer is set to the frequency difference between the compressor vibration sensitive axis and the ambient excitation ≥ 3Hz, the tensor dimension is 512×256×128, the spatial resolution is 0.25m×0.25m×0.25m, and the octree encoding technology with level 6 LOD layer is used to realize dynamic loading of constraint conditions, with an update delay of ≤10ms; Step 3: Design an improved multi-agent deep deterministic policy gradient algorithm. The policy network adopts the graph attention mechanism; the critic network introduces the physical information neural network architecture; the experience pool implements spatiotemporal correlation sampling, and the parameters are set as follows: learning rate Actor 0.0001 / Critic 0.0003, target network update coefficient τ = 0.008, and policy noise Ornstein-Uhlenbeck process θ = 0.15, σ = 0.2; Step 4: Establish a multi-scale reward function system. At the micro scale, set the device-level contact force penalty: Press F when the contact force is ≥ 5kN. 1.2 ×(–0.8); at the mesoscopic scale, a regional resonance risk indicator is set: when the modal participation factor in the 6–8 Hz frequency band is greater than 0.15, a penalty of –1.2 / 0.1 is applied; at the macroscopic scale, a platform-level stability indicator is set: a reward of +0.5 is applied for every 0.1m increase in the GM value, and a penalty of –1.5 / Hz is applied when the matching error between the natural period of roll and the peak frequency of the wave energy spectrum is greater than 8%. The total reward function adopts hierarchical weighting, and the weight distribution entropy regularization coefficient λ=0.05; Step 5: Implement high-precision virtual collision detection, using a convergence threshold of 1e –6 m, an improved GJK-EPA hybrid algorithm with a maximum number of iterations of 100, combined with the kinematic chain modeling of the device with 4 to 6 joint degrees of freedom and a maximum angular velocity of 0.1 rad / s, generates a collision potential field gradient map in real time, implements pre-action suppression on potential collision areas, and the penalty coefficient varies with the collision probability P c Piecewise function: P c <0.3 –0.5, 0.3≤P c <0.7–1.2, P c ≥0.7 –3.0; Step 6: Perform nonlinear center of gravity prediction and compensation. Use 20-node hexahedral hybrid element, incremental finite element method with material nonlinear iteration step size of 0.01, and combine with platform damage stability criterion to calculate the center of gravity correction value ΔK G =Σ(m i Δz i ) / Σm i , set the mass sensitivity coefficient k m =1.5, when predicting G M When the value is <2.0m±0.05m, the adaptive adjustment strategy based on Lyapunov stability is triggered, with convergence time ≤90s and overshoot ≤2%; Step 7: Using the mixed reality-digital twin verification system, with the help of the NVIDIA Omniverse physics engine, the rigid body solution accuracy is 0.001m, the fluid coupling time step is 0.01s, and the real-time data exchange software protocol based on OPC UA, the verification conditions need to include a 100-year wave H s =14m, spectral peak period T p = 16s extreme sea condition, the performance index requires that the righting moment of the layout scheme at a heel of 30° be ≥ 120% of the minimum allowable value; In step 8, multi-objective Pareto frontier screening is implemented in the dynamic optimization stage. The optimization objective function is defined as f1 = space utilization ≥ 85%, f2 = stability height ≥ 2.5 m, and f3 = pipeline bending radius ≥ 3 times the pipe diameter. The NSGA-III algorithm is used with a population size of 200 and 3000 iterations to output a non-dominated solution set. The final solution must meet the safety requirements of CCS Rules for Classification of Offshore Floating Facilities (2023).

2. The method according to claim 1, characterized in that The material constitutive relationship described in step 1 is verified using Hopkinson pressure bar experimental data. Combined with digital image correlation technology, the Johnson-Cook model parameters C (strain rate sensitivity coefficient) = 0.012 and m (temperature softening index) = 1.03 are modified. The material failure criterion adopts the improved Bai-Wierzbicki model, where the stress triaxiality η = –0.5~1.0 and the Lode parameter θ = –1~1.

3. The method according to claim 1, characterized in that The device interaction features defined in the graph attention mechanism described in step 3 include: fluid dynamic coupling, thermal radiation impact factor, vibration transfer function amplitude, and the adjacency weight calculation uses an adaptive kernel function k=exp(–||x i –x j || 2 / (2σ 2 )), bandwidth σ=1.2m±0.1m.

4. The method according to claim 1, characterized in that The improved GJK-EPA hybrid algorithm described in step 5 includes the following enhanced modules: a fast repulsion stage based on conformal geometric algebra; a curvature adaptive subdivision strategy is introduced in the EPA stage; and the Hertz–Mindlin nonlinear contact model is used for contact force calculation.

5. The method according to claim 1, characterized in that The following nonlinear effects are considered in the incremental finite element analysis described in step 5: material rate-dependent plasticity; large deformation geometric nonlinearity; and fluid-structure interaction boundary conditions.

6. The method according to claim 1, characterized in that The constraint handling mechanism is defined in the multi-objective optimization described in step 8: solutions that violate hard constraints are penalized with a death penalty strategy; soft constraint violations are converted into additional objective functions; and dynamic constraint relaxation is implemented.

7. The method according to claim 1, characterized in that The final layout solution described in Step 8 needs to pass three levels of verification marks, namely, CFD-based deck wave analysis; explosion shock wave overpressure simulation; and life-cycle fatigue analysis.

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