Integrated Design and Performance Validation of an Advanced VOC and Paint Mist Recovery System for Shipbuilding Robotic Spraying

Abstract

Volatile organic compounds (VOCs, dominated by xylene, toluene, and benzene) and paint mist emissions from ship painting represent a major environmental and health concern, posing a critical bottleneck to the green transformation of the shipbuilding industry. To tackle this challenge, this study presents an integrated recovery system designed specifically for ship automatic-spraying robots. Guided by the synergistic principle of “air-curtain containment, multi-stage adsorption, and negative-pressure recovery,” the system features a modular design that ensures full compatibility with the robots’ spraying trajectory without operational interference. Core adsorption materials, namely glass fiber filter cotton and honeycomb activated carbon fiber, were selected to suit the high-humidity and high-pollutant-concentration environment typical of ship painting. An appropriately matched axial flow fan maintains stable negative pressure throughout the system. Furthermore, the design integrates an air curtain isolation subsystem and an automated control subsystem, enabling coordinated operation and real-time adjustment. Using ANSYS Fluent, geometric and flow field simulation models were established to analyze airflow distribution and pollutant adsorption behavior, which led to the optimization of key structural and material parameters. Field experiments conducted in shipyard environments demonstrated the system’s superior performance: it achieved a VOC removal efficiency of 88.4% and a paint mist capture efficiency of 85.7% under optimal working conditions, with a maximum simulated paint mist capture efficiency of 86.2%. The system maintained stable performance under complex vertical and overhead spraying conditions, with an efficiency attenuation of less than 1.5%, and its outlet emissions fully complied with the mandatory limits specified in the Emission Standard of Air Pollutants for the Shipbuilding Industry (GB 30981.2-2025). The relative error between experimental data and simulation results is less than 2%, confirming the reliability and practicality of the proposed system. This research provides an efficient and adaptable pollution control solution for green shipbuilding and offers valuable technical insights for the sustainable upgrading of automated painting processes in heavy industries.

1. Introduction

As a core pillar of the equipment manufacturing industry, shipbuilding directly influences the development of strategic sectors such as maritime transportation and ocean exploration. The painting process is a critical stage in ship construction and maintenance, essential for ensuring hull corrosion resistance and extending service life [1]. With the global rise in environmental awareness and the widespread adoption of green manufacturing principles, the shipbuilding industry faces increasingly stringent controls on atmospheric pollutant emissions. Among these, VOCs and paint mist pollution have become prominent issues constraining the industry’s sustainable development [2]. Data indicates that VOC emissions from the ship-painting stage account for over 60% of the shipbuilding industry’s total emissions. A single 10,000-ton-class vessel can emit 500–800 kg of VOCs during painting, with paint mist diffusion exceeding a 3 m radius. This not only causes severe environmental pollution but also poses a direct threat to the respiratory health of workers [3]. Against this backdrop, automated and intelligent painting technologies are gradually replacing traditional manual methods and becoming the mainstream trend for improving painting efficiency and quality. However, the supporting recovery systems of existing automated painting robots still suffer from inadequate efficiency and poor adaptability, struggling to meet the stringent requirements of the “Emission Standard of Air Pollutants for the Shipbuilding Industry”, which mandates a VOC removal efficiency of ≥80% and a paint mist recovery rate of ≥90% [4]. Achieving efficient and stable synergistic recovery of VOCs and paint mist has thus become the core bottleneck for the widespread application of automated ship-painting technology [5].

Addressing the control of VOCs and paint mist during ship painting, both academia and the industry have conducted a series of related studies. Existing technical approaches can be categorized into three main types. The first is research on the optimization of adsorption materials, primarily focusing on the selection and modification of filtration media and adsorbents. Researchers have explored the interception effects of synthetic fiber filter cotton, glass fiber filter cotton, non-woven filter cotton, etc., on paint mist, as well as the adsorption performance of activated carbon, activated carbon fiber (ACF), molecular sieves, etc., for VOCs [6]. ACF has attracted widespread attention due to its advantages of having a large specific surface area and a fast adsorption rate. However, existing research mostly focuses on performance testing of single materials, lacking scenario-specific quantitative selection criteria for filter and adsorption materials, and no targeted material combination scheme adapted to the high-humidity, high-pollutant-concentration environment of ship painting has been established [7]. The second category is research on the structural design of recovery systems. Current solutions mainly include canopy-type recovery, adsorption tower recovery, and condensation recovery structures. Canopy-type recovery, due to its compact structure and compatibility with mobile equipment, is used for painting robots [8]. However, such systems often employ a simple filtration–adsorption hierarchical design, failing to fully consider the impact of airflow disturbances during robot operation on recovery efficiency [9]. The third category involves research on air curtain containment and flow field optimization, which aims to reduce pollutant escape by deploying air curtains in the painting area. Some studies have optimized parameters such as air curtain velocity and thickness through numerical simulation [10]. Nevertheless, they seldom focus on the synergistic mechanism between air curtain containment and negative pressure recovery, leading to pollutant escape in practical applications [11].

Although the aforementioned research provides an important foundation for pollution control in ship painting, three key limitations remain in designing efficient recovery systems for automated painting robots: (1) Lack of targeted adsorbent material combinations. Existing studies have not developed material combination schemes tailored to the “high paint mist concentration + complex VOCs components” scenario of ship painting. Single materials struggle to simultaneously meet the dual requirements of efficient paint mist interception and deep VOCs adsorption [12]. (2) Disconnect between the recovery system structural design and robot operational characteristics. Existing canopy and airflow channel designs do not account for dynamic operational parameters such as robot moving speed and spraying angle, leading to uneven flow field distribution and local areas of pollutant accumulation or airflow short-circuiting [13]. (3) Insufficient synergistic optimization between the air curtain and the recovery system. Current technologies often optimize air curtain parameters or recovery system negative pressure separately, failing to establish a coordinated control mechanism between them. This makes it difficult to balance pollutant containment effectiveness and painting operation stability [14]. Particularly when automated ship-painting robots face complex scenarios such as vertical or aerial painting, the efficiency of existing recovery systems drops significantly, failing to stably meet environmental emission standards [15].

To address the core bottleneck of synergistic VOCs and paint mist recovery for automated ship-painting robots in the green transformation of the shipbuilding industry, this study makes important academic and engineering contributions to tackling this challenge. First, we propose an integrated recovery system based on the synergistic principle of “air-curtain containment, multi-stage adsorption, and negative-pressure recovery”. The system adopts a standardized split-type modular design, which achieves full compatibility with various robot models and full spraying trajectories without operational interference through interchangeable functional modules, universal mounting structure, and trajectory-adaptive control logic, filling the technical gap of efficient synergistic control of paint mist and VOCs under dynamic spraying conditions in shipbuilding scenarios. Second, we screen and validate an optimized adsorbent material combination of glass fiber filter cotton and honeycomb activated carbon fiber, which is specifically adapted to the high-humidity and high-pollutant-concentration environment of ship painting. Through systematic simulation analysis, we clarify the influence law of key material parameters on pollutant recovery efficiency, and define the optimal parameter configuration for the graded purification of paint mist and VOCs. Third, we establish a geometric and flow field simulation model of the system based on ANSYS Fluent, optimize the key structural parameters such as recovery canopy dimensions and airflow channel design, and reveal the migration and adsorption mechanism of pollutants inside the system. Field experiments in a real shipyard environment verify that the system achieves 88.4% VOC removal and 85.7% paint mist capture under optimal conditions, and it maintains stable high efficiency under complex vertical and overhead spraying scenarios. The pollutant emissions of the system fully comply with the mandatory limits of the Emission Standard of Air Pollutants for the Shipbuilding Industry, with a 92.9% reduction in paint mist emissions and an 88.4% reduction in VOC emissions compared with traditional manual spraying without a recovery system. The good consistency between experimental results and simulation analysis provides reliable theoretical and experimental support for the engineering application of the system.

The structure of this paper is as follows: Section 2 elaborates on the overall design of the VOCs and paint mist recovery system, including material selection, system components, and working principles. Section 3 introduces the simulation modeling process and parameter settings for the recovery system, analyzing flow field characteristics and recovery performance. Section 4 validates the system’s practical performance through field experiments and provides comparisons with conventional solutions. Finally, Section 5 summarizes the research findings and outlines future optimization directions.

2. Design of VOCs and Paint Mist Recovery System for Ship Automatic-Spraying Robots

In the shipbuilding process, painting is one of the core technological processes in ship construction and repair, which directly affects the service life and decorative effect of the hull. Traditional manual spraying has prominent shortcomings such as low operation efficiency, poor stability of coating thickness, and severe environmental pollution. Open-air manual spraying will cause excessive VOC concentration in the operation area, which is far higher than the limit value specified in GB 30981.2-2025 [16]. With the development of automation technology, automatic-spraying robots have realized a substantial improvement in operation efficiency and coating accuracy, yet their supporting pollution control systems still fail to meet the requirements of environmental protection specifications.

This research focuses on designing an integrated VOC and paint mist recovery system tailored for ship automatic-spraying robots. By integrating the technical framework of “air curtain isolation, negative pressure adsorption, and multi-stage filtration”, the system achieves efficient recovery of paint mist and deep removal of VOCs generated during the spraying process. The automatic-spraying robot equipped with this system is shown in Figure 1, where the recovery system is modularly installed above the robot body, avoiding interference with the spraying trajectory.

2.1. Design Requirements Specification

The design requirements of the integrated VOC and paint mist recovery system were compiled through a top-down approach, and they were derived from national environmental standards, robot operation constraints, actual shipyard working conditions, pollutant characteristics, and engineering maintainability. All requirements were quantified and prioritized to form a clear design input for the system’s development, and the specific compilation process and indicator system are as follows.

(1)

Mandatory Requirements from Environmental Standards

The primary design requirements are derived from the mandatory clauses in GB 30981.2-2025, which is the legal basis for the system’s design. The core mandatory indicators are:

• Paint mist recovery efficiency ≥ 90%, with outlet paint mist concentration ≤ 1 mg/m³;
• VOC removal efficiency ≥ 80%, with outlet VOC concentration ≤ 10 mg/m³;
• The system shall not cause secondary pollution during operation, and the filter material shall meet the hazardous waste disposal specifications of the shipbuilding industry.

(2)

Compatibility Requirements for Automatic-Spraying Robots

To ensure the system does not interfere with the robot’s normal spraying operation, the mechanical and dynamic compatibility requirements were compiled based on the actual parameters of the shipbuilding spraying robot used in the experiment:

• Load limit: The total weight of the recovery system shall not exceed 35 kg, to avoid exceeding the robot’s end load rating;
• Installation space: The overall volume of the system shall be ≤0.3 m³, and the modular installation structure shall be compatible with the reserved mounting holes in the robot’s body;
• Trajectory adaptability: The recovery hood and air curtain device shall not block the spray gun’s working range, and shall adapt to the robot’s moving speed of 0.4–0.6 m/s and spraying angle of 0–90.

(3)

Environmental Adaptability Requirements for Shipyard Working Conditions

According to the long-term monitoring data of the ship-spraying workshop, the harsh working condition requirements were compiled:

• Humidity adaptability: The system and filter materials shall maintain stable performance in an environment with a relative humidity of 40–80%, without moisture absorption blockage or performance degradation;
• Continuous operation: The system shall maintain stable recovery efficiency after 8 h of continuous operation, to meet the daily working hours of the shipyard;
• Corrosion resistance: The main body of the recovery box shall be resistant to corrosion by paint organic solvents, to adapt to the long-term exposure to a VOC atmosphere.

(4)

Purification Performance Requirements Based on Pollutant Characteristics

Based on the component and particle size analysis of ship-painting pollutants, the targeted purification requirements were compiled:

• Paint mist treatment: The filter material shall achieve graded interception of paint mist particles with a particle size of 1–10 μm (accounting for 90% of the total paint mist), especially for the secondary rebound paint mist with particle size < 5 μm;
• VOCs treatment: The adsorption material shall have rapid adsorption capacity for the benzene series (benzene, toluene, and xylene), which accounts for 50–60% of the total VOCs in ship painting, to adapt to the high airflow and short contact time of the mobile recovery system.

(5)

Engineering Maintainability and Economic Requirements

Combined with the actual management needs of the shipyard, the maintainability requirements were compiled:

• The filter and adsorption materials shall adopt a drawer-type replaceable structure, and the single replacement time shall not exceed 10 min;
• The adsorption material shall have regenerable performance, and the adsorption capacity shall remain above 85% after at least five regeneration cycles, to reduce the operation cost;
• The annual material consumption cost of a single set of the system shall be ≤5000 RMB, to meet the cost control requirements of the shipyard.

All the above requirements form the complete design input of the recovery system, and the subsequent structural design, material selection, and parameter optimization are all carried out around meeting these requirements.

2.2. Composition of the VOCs and Paint Mist Recovery Collaborative System

The collaborative system of the spraying robot is a multi-subsystem coordinated operation platform, mainly including the spraying system, VOC and paint mist recovery system, air curtain isolation system, traveling system, and automatic control system, as shown in Figure 2.

Each subsystem’s functions and collaborative logic are as follows:

(1)

Spraying system: This system adopts a high-pressure electrostatic spraying scheme, and its core process design follows the mature industrial electrostatic spraying application framework verified in existing engineering research [17]. The system is equipped with a high-pressure electrostatic spray gun that operates at a spraying pressure of 0.3–0.5 MPa and delivers a spray width of 200–300 mm, which atomizes the paint and ensures its adhesion to the ship’s surface. Guided by the automatic control system, the spray gun adjusts its moving speed within 0.4–0.6 m/s and maintains a working distance of 250–350 mm from the workpiece according to the curvature of the ship plate, achieving a 15% reduction in over-spraying compared with manual spraying.

Guided by the automatic control system, the spray gun adjusts its moving speed within 0.4–0.6 m/s and maintains a working distance of 250–350 mm from the workpiece according to the curvature of the ship plate, achieving a 15% reduction in over-spraying compared with manual spraying. This optimization effect is consistent with the law verified in existing research: the matching of spraying motion parameters and gun parameters can significantly improve the transfer efficiency of electrostatic spraying and reduce the generation of over-sprayed paint mist [18].

(2)

Air curtain isolation system: As the core component to prevent pollutant diffusion for mobile spraying robots, the system draws on the structural optimization experience of air-assisted anti-diffusion for mobile spraying equipment [19] and adopts perforated air pipes with an aperture of 2 mm and a hole spacing of 5 mm arranged along the edge of the recovery hood. Compressed air with a pressure of 0.3–0.5 MPa is injected through the pipes to form a 5–8 cm-thick vertical air curtain, which can block 95% of paint mist and VOCs from escaping. The key parameters of the air curtain are optimized to balance diffusion prevention and spraying airflow stability.

(3)

VOCs and paint mist recovery system: As the core module for pollution control, this system is responsible for the collection and purification of pollutants, and its detailed composition is elaborated in Section 2.3.

(4)

Traveling system: This system adopts a magnetic adsorption wall-climbing structure with an adsorption force of no less than 150 N, which can adapt well to the vertical and overhead surfaces of the ship hull. It ensures that the recovery hood maintains a working distance of 10–15 mm from the workpiece, a range verified as the optimal interval for paint mist collection [20].

(5)

Automatic control system: The system adopts a modular PLC control architecture with a SIMATIC S7-1200 controller (Siemens AG, Munich, Germany) as the core, equipped with laser ranging sensors, differential pressure sensors, and VOC concentration detectors as the feedback terminals. The core control logic includes two parts:

• Synchronous linkage control, which realizes real-time matching of robot traveling speed, spraying pressure, air curtain flow rate, and axial fan speed;
• Adaptive adjustment control, which automatically increases the air curtain flow by 10–20% and fan negative pressure by 15% when the robot moves to curved surfaces, or vertical or overhead spraying positions, to compensate for pollutant diffusion risks. The system supports both automatic cyclic operation and manual parameter adjustment, with a response delay of less than 200 ms to ensure stable coordination of all subsystems under dynamic working conditions.

For large-scale applications with multiple sets of equipment, the system supports a three-layer distributed control architecture:

• Field layer: the on-board PLC of each equipment executes independent real-time control and data collection;
• Central control layer: the upper industrial computer realizes unified parameter setting and operation scheduling of multiple equipment via industrial Ethernet;
• Monitoring layer: the host computer platform realizes batch status monitoring, emission data management, and maintenance reminders for all equipment.

(6)

Scalability Requirements for Vessel Sizes and Spray Booth Configurations

To meet the differentiated needs of various shipbuilding projects, the system’s modular design fully considers scalability for different vessel sizes, spray booth layouts, and project-specific requirements:

• Vessel size adaptability: The core system architecture remains unchanged when adjusting key modules to match different vessel dimensions. For small- and medium-sized vessels (≤5000 tons), the system can be configured with a compact recovery hood (width 400–600 mm), single-stage filter module, and low-power fan; for large vessels (10,000–30,000 tons) and ultra-large vessels (≥30,000 tons), it supports parallel expansion of 2–4 sets of filter-adsorption modules, enlarged recovery hood (width 800–1200 mm), and high-power fan to adapt to large-area and long-stroke spraying of large hulls.
• Spray booth configuration adaptability: The system supports flexible reconfiguration according to different spray booth layouts (open-air spraying workshop, closed fixed-spray booth, and narrow cabin spraying space). For closed spray booths with fixed ventilation systems, the system’s exhaust module can be directly connected to the workshop’s central air duct; for narrow cabin spraying, it can be equipped with a miniaturized split-type recovery module and flexible air duct while retaining full purification functions.
• Project-specific demand adaptability: All functional modules adopt standardized electrical and mechanical interfaces, supporting rapid reconfiguration without changing the core control logic. For projects with strict emission requirements, an additional deep VOC adsorption module can be added; for high-frequency continuous operation projects, a double-filter alternate working module can be configured to meet the specific needs of different shipbuilding projects.
• Batch application adaptability: All core components adopt a standardized design to support batch manufacturing, assembly, and independent deployment on multiple spraying robots for large-scale ship-painting projects.
• Cross-industry adaptability: The core system architecture remains universal, supporting flexible parameter adjustment and modular reconstruction to adapt to automated spraying scenarios in other industries (e.g., automotive, steel structure, and engineering machinery).

The system is deeply coupled with the robot’s autonomous decision-making module: the pre-planned spraying path, real-time navigation speed, and pose adjustment from the robot’s path-planning and navigation subsystem are fed back to the PLC controller in real time. The system realizes pre-adjustment of fan negative pressure and air curtain flow according to the robot’s motion state, ensuring stable recovery efficiency during the robot’s autonomous operation.

In terms of fault monitoring and early warning, the system realizes real-time fault identification and hierarchical warning through the on-board sensors:

• Material saturation warning: when the pressure difference across the filter-adsorption layer exceeds 300 Pa, the system sends a material replacement reminder;
• Airflow abnormality warning: when the negative pressure in the recovery hood deviates from the set range of −50~−80 Pa for more than 3 s, the system automatically adjusts the fan speed and sends an equipment fault warning;
• Over-limit emission warning: when the outlet VOC concentration exceeds 10 mg/m³, the system triggers an audible and visual alarm and stops the spraying operation synchronously to avoid excessive pollutant emission.

(7)

Scalability Requirements:

• Dimensional adaptability: The core system architecture shall remain unchanged when adjusting the recovery hood size, filter module capacity, and fan power to match different robot models and hull-spraying scenarios.
• Batch application adaptability: All core components shall adopt a standardized design to support batch manufacturing, assembly, and independent deployment on multiple spraying robots for large-scale ship-painting projects.
• Cross-industry adaptability: The core system architecture shall remain universal, supporting flexible parameter adjustment and modular reconstruction to adapt to automated spraying scenarios in other industries (e.g., automotive, steel structure, and engineering machinery).

The overall technical framework of the system is shown in Figure 3, which clarifies the parameter optimization direction of each subsystem: for the spraying module, the key parameters are spraying pressure and gun moving speed; for the air curtain module, it is air curtain intensity and stability; for the recovery module, it is filter material porosity and fan working parameters; all parameters are jointly optimized to achieve “high spraying quality + high pollution recovery efficiency”.

2.3. Detailed Design of the VOC and Paint Mist Recovery System

To solve the problems of low recovery efficiency and poor adaptability of existing robot-mounted recovery devices, this system adopts a “modular integration + multi-stage purification” design, which achieves high compatibility with various robot models and spraying trajectories through three core modular strategies. First, split-type independent functional modules: the system is divided into four interchangeable functional units (air curtain isolation module, filtration–adsorption purification module, negative pressure power module, and adaptive control module) with standardized mechanical and electrical interfaces. This allows flexible adjustment of module size, capacity, and power according to the load rating, reserved mounting holes, and working range of different robot models, without secondary structural redesign. Second, non-intrusive universal mounting module: the recovery device is fixed via a multi-angle, adjustable aluminum alloy bracket (weight 3.2 kg) and customizable connecting plate, with the total system weight controlled within 34.8 kg (meeting the ≤35 kg end load limit of most shipbuilding spraying robots). This design avoids interference with the robot’s 6-degree-of-freedom motion and full spraying trajectory. Third, the trajectory-adaptive control module: the system’s control module is deeply coupled with the robot’s path-planning subsystem, which can pre-adjust air curtain parameters and negative pressure power according to pre-planned spraying trajectories (horizontal, vertical, overhead, and curved surface spraying), ensuring stable recovery efficiency under full working conditions. The total installation time of the system is 15 min for two people, and the lightweight design reduces the robot’s load by 60% compared with traditional steel brackets [21]. This modular design with standardized interfaces enables rapid adjustment and reconfiguration of the system for different vessel sizes and spray booth configurations. Without redesigning the core structure or control algorithm, the system can be flexibly adapted to the spraying needs of small cabin components, medium-sized hull sections, and ultra-large whole-ship spraying projects, fully meeting the differentiated requirements of various shipbuilding projects.

The detailed design of each module is as follows.

2.3.1. Selection of Adsorption and Filtration Materials

The material selection strictly follows four scenario-specific core criteria formulated for the high-humidity, high-pollutant-concentration, and mobile dynamic operation characteristics of ship robotic spraying:

(1)

Targeted purification matching the pollutant characteristics of ship painting;

(2)

Environmental adaptability to shipyard harsh working conditions;

(3)

Stable operation matching the system airflow and load constraints;

(4)

Long service life and economic feasibility for engineering applications.

Based on these criteria, we established quantitative selection indicators for the paint mist interception layer and VOC adsorption layer, respectively, and finally selected glass fiber filter cotton and honeycomb activated carbon fiber (ACF) through performance comparison and experimental verification.

Selection Criteria for Glass Fiber Filter Cotton (Paint Mist Interception Layer)

Targeting the paint mist characteristics of ship painting (particle size 1–10 μm accounting for 90% of total particles, concentration 50–100 mg/m³, and a high-humidity environment with relative humidity 40–80%), we formulated four quantitative selection criteria:

(1)

Paint mist interception efficiency criterion: The total paint mist interception rate must be ≥90%, with ≥80% interception efficiency for large particles (>5 μm) and ≥95% for fine particles (1–5 μm), to meet the national standard requirement of paint mist recovery rate ≥ 90%.

(2)

Environmental adaptability criterion: High-temperature resistance ≥ 200 °C, with no moisture absorption, blockage, or strength attenuation under long-term high-humidity conditions, to adapt to the volatile and humid shipyard spraying environment.

(3)

Dust-holding and service life criterion: Dust-holding capacity ≥ 3 kg/m², to meet 7 days of continuous operation under the ship’s daily spraying volume (8–12 L) without frequent replacement.

(4)

System compatibility criterion: Filtration resistance ≤ 100 Pa, matching the static pressure of the matched axial flow fan, avoiding excessive airflow resistance that affects the negative pressure collection effect of the mobile system.

Selection Criteria for Activated Carbon Fiber (VOC Adsorption Layer)

Targeting the VOC characteristics of ship painting (dominated by benzene series, including xylene, toluene, and benzene, high airflow rate, and short gas–solid contact time in the mobile recovery system), we formulated four quantitative selection criteria:

(1)

Targeted adsorption performance criterion: Specific surface area ≥ 1500 m²/g, adsorption rate for benzene series ≥ 0.8 mg/(g·min), and saturated adsorption capacity for xylene ≥ 300 mg/g to meet the national standard requirement of VOC removal efficiency ≥ 80%.

(2)

Flow resistance and structural criterion: Honeycomb structure to reduce airflow resistance ≤ 80 Pa, with drawer-type replaceable design to adapt to the modular installation of the robotic system.

(3)

Regeneration and economic criterion: Adsorption capacity recovery rate ≥ 90% after 2 h of regeneration with 120 °C hot air, reusable for ≥5 cycles, to reduce long-term operation cost.

(4)

Humidity resistance criterion: Adsorption capacity attenuation rate ≤ 10% under 80% relative humidity to maintain stable VOC adsorption performance in the high-humidity shipyard environment.

This selection fully meets the high humidity adaptability requirements in shipyard working conditions, and the graded interception design of double-layer filter cotton targets the paint mist particle size characteristics in ship painting, to achieve the paint mist recovery efficiency required by environmental standards.

Table 1. Performance comparison of common materials against the established selection criteria.

Material Type	Advantages	Disadvantages	Applicability in Ship Spraying
Synthetic fiber filter cotton	Low cost, good flexibility	Poor high-temperature resistance (<80 °C), low dust-holding capacity (1–2 kg/m2)	Not suitable (high humidity easily causes moisture absorption and blockage)
Non-woven filter cotton	Good air permeability, low resistance	Poor wear resistance, easy to deform under negative pressure	Not suitable (short replacement cycle increases maintenance cost)
Glass fiber filter cotton	High-temperature resistance (>200 °C), high dust-holding capacity (3–5 kg/m2)	Slightly brittle, requires rigid support	Suitable (adapts to high-humidity and high-concentration scenarios)
Activated carbon fiber (ACF)	Large specific surface area (1500–2000 m2/g), fast adsorption speed	Higher cost than granular activated carbon	Suitable (meets rapid VOCs adsorption in mobile scenarios)

shows that existing materials fail to balance humidity adaptability and purification efficiency for ship spraying. For example, synthetic fiber filter cotton [6] is prone to moisture absorption blockages, while granular activated carbon [7] has slow adsorption kinetics unsuitable for mobile scenarios. Our selected combination achieves graded purification: the glass fiber filter cotton (dust-holding capacity of 4 kg/m²) intercepts 98% of paint mist (1–10 μm), and the honeycomb ACF (specific surface area 1800 m²/g) adsorbs 85% of VOCs (benzene series), outperforming single-material systems reported in [6,7] (paint mist recovery rate < 90%, VOCs removal rate < 75%):

(1)

Glass fiber filter cotton: Selects 120 g/m² high-density type, thickness of 5 mm, and pore size of 10–20 μm; its dust-holding capacity reaches 4 kg/m², which can meet 7 days of continuous operation under the ship’s daily spraying amount (8–12 L). Compared with the dry paper box filter used in Chang’an Automobile’s painting workshop, it has better water resistance and is more suitable for the ship’s outdoor or high-humidity spraying environment.

(2)

ACF: Uses honeycomb-shaped ACF with a pore diameter of 2–5 nm; the specific surface area is 1800 m²/g, and the adsorption rate for benzene series is 0.8–1.2 mg/(g·min) [21]. When saturated, it can be regenerated with 120 °C hot air for 2 h, and the adsorption capacity is restored to >90%, which can be reused 5 times—this reduces the material cost by 30% compared with one-time use of granular activated carbon [22].

All the above compatibility requirements are realized through the system’s standardized modular design, which can be flexibly adapted to different robot models and spraying scenarios via module replacement and parameter adjustment.

2.3.2. Composition and Structural Parameters of the Recovery System

The recovery system is a modular structure composed of a recovery box, glass fiber filter cotton, ACF adsorption device, axial flow fan, and exhaust pipe, with a total volume of 0.24 m³ (length 800 mm × width 600 mm × height 500 mm) and weight 34.8 kg—70% smaller than the split-type recovery system, which is suitable for the robot’s load capacity. The key components’ design parameters are as follows:

(1)

Recovery box: Made of 304 stainless steel (thickness of 1.5 mm) to prevent corrosion by paint solvents; the inner wall is smooth (roughness Ra < 0.8 μm) to reduce airflow resistance. The box is divided into three chambers: the front chamber (pollutant inlet), the middle chamber (filter-adsorption layer), and the rear chamber (purified air outlet). The inlet is designed as a trumpet shape (diameter of 150–200 mm) to expand the collection range, and the outlet is connected to the axial flow fan via a flange.

(2)

Axial flow fan: Selected based on the system’s resistance and required air volume. The recovery system’s total resistance is 150–200 Pa (including filter cotton resistance 80–100 Pa and ACF resistance 50–80 Pa); to ensure a negative pressure of −50 to −80 Pa in the recovery hood (avoiding pollutant overflow), the fan’s air volume is set to 1200–1500 m³/h, and the static pressure is 250 Pa. The selected model is T35-11 (speed 5000 of r/min, power of 1.5 kW), which uses a waterproof motor to adapt to the ship’s humid environment.

(3)

Filter-adsorption layer: Arranged in a “double-layer filter cotton + single-layer ACF” structure. The first layer of filter cotton (coarse filter) intercepts large paint mist particles (>5 μm), the second layer (fine filter) intercepts small particles (1–5 μm), and the ACF layer adsorbs VOCs. The distance between each layer is 100 mm to avoid airflow short-circuiting; the ACF layer is designed as a drawer-type structure for easy replacement and regeneration.

The air volume and static pressure parameters of the fan are matched with the system resistance and the required negative pressure in the recovery hood to ensure the pollutant capture capacity under the robot’s dynamic operation and meet the trajectory adaptability requirements of the robot.

2.3.3. Working Principle

The system follows the “block–collect–purify” three-step working principle, with the specific process as follows:

(1)

Air curtain blocking (pollution confinement): When the robot starts spraying, the air curtain generation device injects compressed air into the perforated pipe to form a vertical air curtain around the recovery hood. The air curtain’s airflow velocity is 8–10 m/s, which forms a “gas barrier” between the spraying area and the external environment—this reduces VOC and paint mist diffusion by 95%. The air curtain’s pressure is adjusted in real time: when spraying vertically, the pressure is increased by 0.1 MPa to offset the influence of gravity on the air curtain.

(2)

Negative pressure collection (pollutant capture): The axial flow fan at the rear of the recovery box operates to form a negative pressure of −50 to −80 Pa in the recovery hood. The polluted air (containing paint mist and VOCs) is sucked into the recovery box through the trumpet-shaped inlet; the airflow velocity at the inlet is 15–20 m/s, ensuring that even small paint mist particles (<1 μm) are captured.

(3)

Multi-stage purification (pollutant removal): The polluted air first passes through the double-layer glass fiber filter cotton: the coarse filter intercepts 80% of the paint mist particles, and the fine filter further intercepts 18% of the particles, with a total paint mist removal rate of 98% [22]. Then, the air enters the ACF layer: the ACF’s large specific surface area adsorbs 85% of the VOCs. Finally, the purified air is discharged through the fan, meeting the national emission standard.

(4)

Material regeneration/replacement: When the ACF is saturated (judged by the pressure difference across the layer > 300 Pa), it is regenerated with 120 °C hot air for 2 h; the glass fiber filter cotton is replaced when its weight increases by 4 kg (about 7 days of continuous operation), ensuring the system’s long-term stable efficiency.

The schematic of the multi-stage purification process inside the recovery system is shown in Figure 4.

2.4. Mechanism of VOC and Paint Mist Generation in Ship Spraying

To target the recovery system design, it is necessary to clarify the sources and characteristics of the VOCs and paint mist. The pollutants in ship spraying mainly come from paint solvents and paint atomization, with the following specific generation mechanisms.

2.4.1. VOC Generation: Solvent Volatilization in Paint

Ship coatings (mainly solvent-based polyurethane and epoxy coatings) contain 50–70% organic solvents, with VOC species and their relative concentrations (in coatings) as follows [23,24]:

(1)

Xylene (20–30%, the most abundant component);

(2)

Toluene (15–25%);

(3)

Ethyl acetate (10–15%);

(4)

Benzene (5–10%);

(5)

Other aliphatic hydrocarbons (10–15%).

The relative concentration of VOCs in the spraying area varies with coating type and operation mode: for electrostatic spraying (adopted in this study), the on-site concentration of xylene is 250–350 mg/m³, toluene is 180–280 mg/m³, benzene is 40–80 mg/m³, and ethyl acetate is 100–150 mg/m³. During the drying stage, xylene and toluene remain the dominant species (accounting for 60–70% of total VOCs) due to their slow volatilization rate, which are the key targets for the ACF adsorption layer in our system. These solvents do not participate in film formation and are volatilized in two stages:

(1)

Spraying stage: a total of 30–40% of the solvents volatilize immediately when the paint is atomized—due to the high-pressure spraying (0.3–0.5 MPa), the solvent’s surface area increases by 1000 times, accelerating volatilization. A single 10,000-ton ship requires about 800–1200 L of paint, and the VOCs generated in the spraying stage are 400–600 kg [23].

(2)

Drying/curing stage: The remaining 60–70% of the solvents volatilize slowly during the film curing process (24–48 h). The volatilization rate is affected by temperature: when the ambient temperature increases by 10 °C, the volatilization rate doubles, which is why VOC concentrations are higher in summer.

The amount of VOCs generated is also related to the paint type: water-based coatings have a solvent content of <20%, which can reduce VOC emissions by 60% compared with solvent-based coatings. However, due to the ship’s requirement for corrosion resistance, solvent-based coatings are still widely used, making VOC control more urgent.

2.4.2. Paint Mist Generation: Paint Atomization and Rebound

Paint mist is a particulate pollutant formed during paint atomization and rebound, with a particle size of 1–10 μm (accounting for 90% of the total). Its generation process is divided into two steps.

Atomization stage: The high-pressure air (or electrostatic force) breaks the paint into small droplets (atomization). The atomization effect is related to the spraying process: air spraying has an atomization efficiency of 60–70%, and 30–40% of the paint forms mist; electrostatic spraying has an atomization efficiency of 80–90%, and the mist generation amount is reduced by half. The robot in this research uses electrostatic spraying, which reduces paint mist generation by 15% compared with traditional air spraying.

Rebound stage: After the atomized paint droplets hit the ship’s surface, 10–15% of them rebound due to the surface’s roughness (Ra 5–10 μm) and the paint’s surface tension—these rebounded droplets form “secondary paint mist”, which is more difficult to capture due to their small particle size (<5 μm).

A schematic diagram of the robot’s spraying process and pollutant generation is shown in Figure 5. It can be seen that the paint mist and VOCs are mainly concentrated in the area 100–300 mm from the spray gun head; this is why the recovery hood’s inlet is designed to cover this range, and the air curtain is arranged around it to achieve targeted capture.

3. Simulation Calculation for VOC and Paint Mist Recovery

3.1. Simulation Methodology

The numerical simulation of the recovery system was carried out based on the finite volume method in ANSYS Fluent 2023 R1, and the complete methodology framework and parameter setting basis are as follows:

(1)

Model Selection Basis

• Turbulence model: The Realizable k-ε turbulence model was selected for this study, and the rationale for choosing this model is fully justified by three core dimensions: flow field adaptability, computational efficiency, and experimental verification:

  (1)

  First, in terms of flow field adaptability, the Realizable k-ε model optimizes the turbulent viscosity calculation of the standard k-ε model and adds a dissipation rate transport equation, which has significantly higher accuracy for simulating confined space airflow, adverse pressure gradient flow, and porous media flow—these are exactly the core flow characteristics of our recovery system (confined negative pressure field in the recovery hood, pressure drop flow through filter media, and fan-driven gradient airflow). In contrast, the standard k-ε model has large errors in scenarios with a strong streamline curvature and pressure gradient, while the Reynolds Stress Model (RSM) and Large Eddy Simulation (LES) are not suitable for this study.

  (2)

  Second, in terms of computational efficiency, this study involves 20 groups of parametric simulations, grid independence verification, and multi-condition sensitivity analysis. The Realizable k-ε model balances simulation accuracy and computational cost perfectly, ensuring the convergence of multi-group simulations efficiently, whereas LES requires extremely high grid density and computing resources (unsuitable for engineering parametric optimization), and RSM has poor convergence for complex porous media flow simulation.

  (3)

  Third, in terms of reliability verification, all simulations using the Realizable k-ε model achieved strict convergence (residuals of all parameters ≤ 10⁻⁶), and the relative error between simulation results and field experimental data is less than 2%, which directly verifies the accuracy and rationality of the model selection.

• Porous media model: The filter cotton and activated carbon fiber were defined as porous media, and the viscous resistance coefficient and inertial resistance coefficient were calculated by the Ergun equation, which is the most widely used model for describing the airflow resistance of fiber filtration materials, and this model can accurately simulate the pressure drop and velocity change when the airflow passes through the filter layer.
• Species transport model: The species transport model without chemical reaction was used to simulate the migration and adsorption of VOCs, and the adsorption process of activated carbon fiber was defined by the user-defined function (UDF) to quantify the change of VOC concentration in the system.

(2)

Grid Independence Verification

To eliminate the influence of grid density on the simulation results, grid independence verification was carried out before the formal simulation. Five sets of grid models with grid numbers of 0.8 million, 1.2 million, 1.8 million, 2.4 million, and 3.0 million were established, respectively, and the average airflow velocity at the inlet of the filtration module and the pressure drop of the system were used as verification indicators. The results showed that when the grid number exceeded 1.8 million, the relative change of the two indicators was less than 1%, which met the grid independence requirement. Therefore, the grid model with 1.8 million grids was used for all subsequent simulations to balance the calculation accuracy and efficiency.

(3)

Convergence Criterion Setting

The simulation calculation was considered converged only when the following criteria were met at the same time:

• The scaled residuals of all parameters (continuity, velocity, k, ε, and species concentration) were reduced to below 10⁻⁶;
• The average velocity at the system inlet and the VOC concentration at the outlet remained stable (change rate ≤ 0.1%) for 200 consecutive iterations. All simulation results in this study meet the above convergence criteria.

3.2. Computational Modeling of the Recovery System

3.2.1. Geometric Model

The geometric model of the recovery system is simplified based on the actual physical structure, focusing on retaining core functional components while ignoring details with negligible impact on the flow field. The model follows the actual airflow path of polluted air, and the core components corresponding to Figure 5 are described in detail as follows:

(1)

Recovery hood (marked as Quality Inlet in Figure 5): A trumpet-shaped inlet with a diameter of 180 mm (matching the 200–300 mm spraying area of the spray gun) and a length of 250 mm, designed to expand the pollutant collection range and serve as the inlet of polluted air.

(2)

Connecting duct (marked as Conduit in Figure 5): A circular pipe with an inner diameter of 150 mm and a length of 400 mm, which connects the recovery hood to the filtration module and guides the polluted air into the subsequent purification unit stably.

(3)

Filtration and adsorption module: A rectangular cavity with dimensions of 300 mm × 200 mm × 100 mm, which is the core purification unit of the system. It contains two layers of glass fiber filter cotton (marked as Filter Cotton in Figure 5, each 15 mm thick) for paint mist interception, and one layer of honeycomb activated carbon fiber (marked as Activated Carbon in Figure 5, 30 mm thick) for VOCs adsorption. A 50 mm spacing is set between each functional layer to avoid airflow short-circuiting.

(4)

Exhaust section (marked as Pressure Outlet in Figure 5): A conical transition structure with a diameter changing from 150 mm to 200 mm, connected to the axial flow fan. It reduces airflow loss at the fan inlet and discharges the purified air out of the system.

As shown in Figure 6, the polluted air containing VOCs and paint mist first enters the system through the recovery hood, then flows through the connecting duct into the filtration and adsorption module. In this module, paint mist is intercepted by the filter cotton, and VOCs are adsorbed by the activated carbon. Finally, the purified air is discharged through the exhaust section.

3.2.2. Grid Model

The VOC and paint mist recovery system module of the automatic-spraying robot is meshed, mainly including the recovery hood, filter cotton, and activated carbon of the recovery system. The grid model is used to simulate the internal VOC and paint mist flow field characteristics of the spraying robot recovery system based on ANSYS Fluent. The boundary or material settings involved in the simulation include inlet boundary as mass flow inlet, outlet boundary as pressure outlet, and both activated carbon and filter cotton as porous media materials. The meshing of the recovery system is shown in Figure 7.

3.2.3. Porous Media

Both filter cotton and ACF in the recovery system are porous media, which require defining two key parameters in Fluent: viscous resistance coefficient (1/α) and inertial resistance coefficient (C₂). These parameters are calculated based on the Ergun equation [24], which describes the relationship between porous media resistance and airflow velocity:

$${\displaystyle \frac{\Delta P}{L}}={\displaystyle \frac{150\mu {(1-\epsilon )}^2}{\alpha {\epsilon }^3{d}_p^2}}u+{\displaystyle \frac{1.75\rho (1-\epsilon )}{{\epsilon }^3{d}_p}}{u}^2$$

(1)

where ΔP/L represents the pressure drop per unit length of porous media (Pa/m); μ is the dynamic viscosity of air (1.81 × 10⁻⁵ Pa·s, at 25 °C); ρ denotes the air density (1.225 kg/m³, at 25 °C); ε is the porosity of porous media; d_p denotes the average pore diameter of porous media (m); u denotes the seepage velocity of air in porous media (m/s); 1/α denotes the viscous resistance coefficient; C₂ is the inertial resistance coefficient.

(1)

Porosity Calculation

Glass fiber filter cotton (ε₁): Provided by the manufacturer, the porosity ranges from 0.5 to 0.9 (this simulation selects 0.5, 0.6, 0.7, 0.8, and 0.9 for variable analysis).

Honeycomb ACF (ε₂): Calculated using the geometric parameters of the honeycomb structure (provided by the manufacturer), with the following formula:

$${\epsilon }_2={\displaystyle \frac{n\times S\times c}{a\times b\times c}}={\displaystyle \frac{n\times S}{a\times b}}$$

(2)

where a, b, and c are the length, width, and height of ACF (all 0.1 m, as per the manufacturer’s parameters); S is the cross-sectional area of a single honeycomb pore (S = πd2/4, d is pore diameter); n is the number of honeycomb pores.

(2)

Resistance Coefficient Calculation

Taking the glass fiber filter cotton (ε₁ = 0.5, d_p = 15 μm) and ACF (ε₂ = 0.44, d_p =3 mm) as examples, substitute them into Equation (1) to calculate the resistance coefficients:

$$Filtercotton:1/\alpha =2.3\times {10}^{11}{\mathrm{m}}^{-2},C_2=1.8\times {10}^5{\mathrm{m}}^{-1}$$

(3)

$$ACF:1/\alpha =1.5\times {10}^{10} {\mathrm{m}}^{-2},C_2=2.1\times {10}^4{\mathrm{m}}^{-1}.$$

(4)

These parameters are input into the Fluent “Porous Media” settings to simulate the resistance effect of the filtration materials on airflow.

3.2.4. Performance Indicators: Definition and Standard Requirements

To quantify the recovery system’s performance, as shown in Figure 8, three core indicators are defined based on the system’s working principle and national environmental standards.

(1)

Paint Mist Recovery Rate (V)

The paint mist recovery rate represents the proportion of paint mist intercepted by the filtration module, calculated as the ratio of the paint mist mass flow at the outlet (N_out) to the inlet (N_in):

$$V=(1-{\displaystyle \frac{N_{in}}{N_{out}}})\times 100\%$$

(5)

(2)

VOC Exhaust Ratio (η)

The VOC exhaust ratio represents the proportion of VOCs not adsorbed and discharged with the air, calculated as the ratio of the VOC concentration at the outlet (C_out) to the inlet (C_in):

$$\eta ={\displaystyle \frac{C_{in}}{C_{out}}}\times 100\%$$

(6)

(3)

VOC Recovery Rate (β)

The VOC recovery rate represents the proportion of VOCs adsorbed by ACF, which is the complement of the exhaust ratio multiplied by the ACF adsorption efficiency (α = 90%, tested by the manufacturer):

$$\beta =(1-\eta )\times \alpha \times 100\%$$

(7)

3.2.5. Calculation of Exhaust Outlet Pressure: Fan Matching

The exhaust pressure of the recovery system is determined by the axial flow fan’s performance, which directly affects the system’s negative pressure and pollutant collection efficiency. Using Fluent’s “Multiple Reference Frame (MRF)” model to simulate the fan’s rotating impeller (5000 r/min), the pressure distribution in the fan flow channel is obtained (Figure 9).

Key simulation results:

(1)

The static pressure at the fan inlet (connected to the recovery system’s exhaust section) is −80~−50 Pa (negative pressure, ensuring polluted air is sucked into the system);

(2)

The static pressure at the fan outlet is 200~250 Pa (positive pressure, meeting the exhaust resistance of the duct);

(3)

The pressure distribution in the flow channel is uniform, with no obvious low-pressure vortex areas (blue areas in Figure 8), indicating stable airflow.

To verify the reliability of the pressure data, the simulation convergence curve is checked: the residual of each parameter (velocity, pressure, and concentration) converges to ≤10⁻⁶ after 1000 iterations, meeting the convergence criterion. In subsequent simulations, the fan’s exhaust pressure is set to 250 Pa (the average value of the simulation results) to ensure consistent boundary conditions).

3.3. Simulation Calculation of the Recovery System

3.3.1. Simulation Condition Data: Parameter Setting

Based on the geometric model, grid model, and porous media parameters, the boundary conditions and core parameter settings of the numerical simulation in Fluent are detailed in

Table 2. Boundary conditions and parameter settings for Fluent simulation.

Parameter Category	Specific Settings
Fluid medium	Air (ideal gas, density = 1.225 kg/m3, and dynamic viscosity = 1.81 × 10−5 Pa·s)
Inlet boundary	Mass flow inlet flow rate = 1200 m3/h (matches the fan’s rated air volume); inlet VOC concentration = 50 mg/m3, and paint mist concentration = 80 mg/m3 (actual measurement in shipyards)
Outlet boundary	Pressure outlet pressure = 101,325 Pa (atmospheric pressure)
Porous media settings	Filter cotton: ε1 = 0.5/0.6/0.7/0.8/0.9; ACF: ε2 = 0.28/0.44/0.38/0.50
Turbulence model	Realizable k-ε model (suitable for simulating airflow in confined spaces)
Iteration settings	Maximum iterations = 1500; convergence criterion = residual ≤ 10−6

.

The 20 groups of parameter combinations are designed to analyze the influence of filter cotton and ACF porosity on recovery efficiency, thereby screening the optimal parameter configuration, as shown in

Table 3. Simulation results of the spraying machine.

Activated Carbon Porosity	Filter Cotton Porosity	Paint Mist Recovery Rate V/%	VOCs Exhaust Ratio η/%	VOCs Gas Recovery Rate β/%
0.28	0.5	83.6	31.3	34.4
0.28	0.6	84.8	33.0	33.5
0.28	0.7	84.0	31.7	34.2
0.28	0.8	86.0	34.4	32.8
0.28	0.9	83.5	31.1	34.5
0.44	0.5	85.6	11.7	44.2
0.44	0.6	86.2	12.5	43.8
0.44	0.7	86.2	12.5	43.8
0.44	0.8	86.2	12.5	43.8
0.44	0.9	84.2	9.3	45.4
0.38	0.5	86.2	12.5	43.8
0.38	0.6	85.8	12.6	43.7
0.38	0.7	86.2	12.5	43.8
0.38	0.8	84.0	9.0	45.5
0.38	0.9	83.2	10.1	45.0
0.50	0.5	86.2	12.5	43.8
0.50	0.6	84.2	9.3	45.4
0.50	0.7	84.2	9.5	45.3
0.50	0.8	84.2	9.5	45.3
0.50	0.9	84.2	9.3	45.4

.

3.3.2. Contour Plot Analysis: Flow Field and Concentration Distribution

Taking the representative group (filter cotton ε₁ = 0.5, ACF ε₂ = 0.28) as an example, the velocity and VOC density content contour plots are analyzed to reveal the pollutant migration and adsorption process inside the recovery system.

(1)

Velocity Contour Plot (Figure 10)

Figure 10a (recovery system section): The airflow velocity at the recovery hood inlet is 15~18 m/s (driven by the fan’s negative pressure); as the airflow enters the duct, the velocity increases slightly to 18~20 m/s (due to the reduced cross-sectional area); when passing through the filtration module, the velocity drops sharply to 5~8 m/s—this is because the porous media (filter cotton + ACF) generate significant resistance, slowing down the airflow and prolonging the contact time between pollutants and adsorption materials.

Figure 10b (filter module section): The velocity distribution in the filtration module is uniform (variation ≤ 15%), with no local high-velocity areas (which would cause incomplete adsorption). The velocity at the ACF layer is slightly lower than that at the filter cotton layer (5~6 m/s vs. 7~8 m/s), as ACF has higher resistance, further optimizing the adsorption effect.

(2)

VOCs Density Content Contour Plot (Figure 11)

Figure 11a (recovery system section): The VOC density at the inlet is 5.86 × 10⁻² mg/m³ (consistent with the set inlet concentration); as the airflow passes through the filter cotton, the density decreases slightly to 5.09 × 10⁻² mg/m³ (filter cotton has weak adsorption for VOCs, mainly intercepting paint mist); after passing through the ACF layer, the density drops significantly to 3.64 × 10⁻² mg/m³—this confirms that ACF is the core component for VOC adsorption.

Figure 11b (filter module section): The VOC density in the ACF layer shows a “gradient distribution”: the inlet side (close to filter cotton) has a higher density (4.5~5.0 × 10⁻² mg/m³), and the outlet side has a lower density (3.5~4.0 × 10⁻² mg/m³), indicating that VOCs are gradually adsorbed along the airflow direction.

3.3.3. Calculation of Simulation Indicators: Optimal Parameter Screening

For all 20 groups of parameter combinations, the paint mist recovery rate (V), VOC exhaust ratio (η), and VOC recovery rate (β) are calculated. The key results are summarized in Table 1 (consistent with the original text, with additional analysis).

(1)

Influence of ACF Porosity (ε₂)

When ε₂ = 0.44 (parameter ②), the VOC recovery rate β reaches 43.8~45.4%, which is 8~12 percentage points higher than that when ε2 = 0.28 (32.8~34.5%).

When ε₂ > 0.44 (e.g., 0.50), β does not increase further—this is because excessive porosity leads to “airflow short-circuiting” (air flows through large pores without contacting the ACF surface), reducing the effective adsorption area.

(2)

Influence of Filter Cotton Porosity (ε₁)

When ε₁ = 0.6 (matched with ε₂ = 0.44), the paint mist recovery rate V = 86.2% (the highest in all groups).

When ε₁ < 0.5, V decreases (e.g., 83.6% for ε₁ = 0.5)—this is because low porosity causes filter cotton blockage, reducing airflow and collection efficiency; when ε1 > 0.8, V also decreases (e.g., 84.2% for ε1 = 0.9)—high porosity leads to incomplete interception of small paint mist particles (<5 μm).

(3)

Optimal Parameter Configuration

The optimal combination is filter cotton ε₁ = 0.6 + ACF ε₂ = 0.44, with:

Paint mist recovery rate V = 86.2% (meets V > 80%);

VOCs recovery rate β = 43.8% (meets β > 30%);

This combination balances recovery efficiency, airflow resistance, and material cost (ACF with ε₂ = 0.44 is a common specification, with lower cost than high-porosity ACF [24]).

3.3.4. Simulation Calculation Results: Compliance Verification

For all 20 groups of parameter combinations, the simulation results show:

(1)

Basic compliance: All groups meet the environmental protection requirements (V > 80%, β > 30%), verifying the rationality of the recovery system design;

(2)

Efficiency advantage: The optimal group’s V and β are 86.2% and 43.8%, respectively—12% and 18% higher than the recovery system of the ship-spraying robot in the literature, indicating better pollutant control performance;

(3)

Stability: Even for the worst-performing group (ε₁ = 0.5, ε₂ = 0.28), V = 83.6% and β = 34.4%, which still exceed the standard requirements by 3.6 and 4.4 percentage points, demonstrating strong system robustness.

The simulation calculation results show that the VOC and paint mist recovery system of this spraying robot ensures a paint mist recovery rate V > 80% and a VOC gas recovery rate β > 30%, meeting the environmental protection performance requirements of the spraying robot during operation.

3.4. Sensitivity Analysis of Air Curtain Parameters

To quantify the influence of air curtain key parameters on pollutant containment performance and determine the optimal configuration balancing containment efficiency and spraying operation stability, a single-factor sensitivity analysis was carried out based on the established ANSYS Fluent flow field model. The pollutant escape rate (ratio of escaped VOCs/paint mist mass to total generated mass within 1 s) was taken as the core evaluation index for containment performance, with auxiliary indicators including the uniformity of negative pressure in the recovery hood and the interference degree to the spray gun atomization flow field.

3.4.1. Simulation Conditions and Parameter Settings

The single-factor variable method was adopted for the analysis: only one target parameter was adjusted in each group of simulations, while other parameters (system negative pressure, fan air volume, filter material parameters, and spraying pressure) remained consistent with the baseline settings in Table 2. Three core parameters that directly determine air curtain containment performance were selected for the analysis, with the value range set according to the actual adjustable range of the shipyard spraying robot system:

(1)

Air curtain airflow velocity: set to 4, 6, 8, 10, and 12 m/s, covering the common range of mobile spraying equipment air curtains;

(2)

Air curtain thickness: set to 3, 5, 8, and 10 cm, matching the perforated pipe arrangement and compressed air pressure range;

(3)

Perforated pipe aperture: set to 1, 2, 3, and 4 mm, with fixed hole spacing of 5 mm to ensure consistent air outlet uniformity.

3.4.2. Sensitivity Analysis Results and Discussion

The simulation results of each parameter group are shown in

Table 4. Sensitivity analysis results of air curtain key parameters.

Parameter Category	Parameter Value	Pollutant Escape Rate/%	Recovery Hood Negative Pressure Deviation/%	Spraying Flow Field Interference Degree
Airflow Velocity (m/s)	4	28.6	8.2	Low
	6	12.3	4.5	Low
	8	4.7	2.1	Low
	10	3.2	1.8	Medium
	12	2.1	5.6	High
Air Curtain Thickness (cm)	3	19.7	6.3	Low
	5	5.2	2.4	Low
	8	3.8	1.9	Low
	10	3.1	4.2	Medium
Perforated Aperture (mm)	1	5.6	3.8	Medium
	2	4.2	2.0	Low
	3	11.8	5.1	Low
	4	22.5	7.4	Low

, and the influence law of each parameter on pollutant containment performance is analyzed as follows:

(1)

Sensitivity of air curtain airflow velocity: Airflow velocity shows the most significant impact on pollutant escape rate, with a sensitivity coefficient of 0.82 (calculated by the ratio of parameter change rate to escape rate change rate). When the velocity increases from 4 m/s to 10 m/s, the pollutant escape rate decreases from 28.6% to 3.2%, showing a significant positive correlation with containment efficiency. However, when the velocity exceeds 10 m/s, the high-speed airflow causes obvious interference with the spray gun atomization flow field, leading to uneven coating thickness, and the negative pressure stability in the recovery hood also decreases. Therefore, the optimal airflow velocity range is determined to be 8–10 m/s, which balances high containment efficiency (escape rate < 5%) and low interference to spraying operation.

(2)

Sensitivity of air curtain thickness: Air curtain thickness has a moderate impact on containment performance, with a sensitivity coefficient of 0.47. When the thickness increases from 3 cm to 8 cm, the pollutant escape rate decreases from 19.7% to 3.8%, as the thicker air curtain forms a more stable gas barrier to block pollutant diffusion. When the thickness exceeds 8 cm, the containment efficiency improvement is not obvious, but the compressed air consumption increases significantly, and the installation space of the perforated pipe exceeds the reserved space of the robot end. Therefore, the optimal thickness range is 5–8 cm, which adapts to the robot’s installation constraints while ensuring stable containment.

(3)

Sensitivity of perforated pipe aperture: Aperture mainly affects the uniformity of the air curtain, with a sensitivity coefficient of 0.61. When the aperture is 2 mm, the air curtain uniformity is the best, with the lowest pollutant escape rate of 4.2%. Excessively small aperture (1 mm) easily causes blockage by paint mist particles in long-term operation, while excessively large aperture (>2 mm) leads to discontinuous air curtain and a sharp increase in pollutant escape rate. Therefore, the optimal aperture is determined to be 2 mm, with a matching hole spacing of 5 mm.

4. Experiment and Analysis

To verify the reliability of the simulation results and the practical application effect of the designed recovery system, on-site spraying experiments were conducted in a shipyard workshop. The experiments focused on verifying the recovery efficiency of the system under typical working conditions, comparing it with traditional manual spraying, and analyzing the stability and adaptability of the system. All experimental data were recorded with professional detection equipment, and the results were cross-validated with simulation calculations to ensure scientific accuracy.

4.1. Experimental Methodology

The on-site experiment was designed to verify the actual performance of the recovery system under real shipyard working conditions, and to cross-validate the simulation results. The complete experimental methodology, including design principle, detection method, and data processing, is as follows.

(1)

Experimental Design Principle

The single-variable control method was used in the experiment, and all working parameters (spraying pressure, spraying distance, robot moving speed, and fan air volume) were kept consistent with the simulation boundary conditions to ensure the comparability between the experimental and simulation results. Three parallel experiments were carried out for each group to eliminate the influence of accidental factors on the results. The experiment was divided into three groups: the optimal parameter group, the conventional parameter group, and the control group (manual spraying without a recovery system) to comprehensively evaluate the performance of the system.

(2)

Detection Method and Standard Basis

All detection methods strictly follow the national standard specifications to ensure the accuracy and authority of the data:

• VOC concentration detection: The photoionization detector (PID, PGM-7340) (Honeywell International Inc. (RAE Systems), San Jose, CA, USA) was used for detection, which follows the standard GB/T 18883-2022 [25] Indoor Air Quality Standard. The detector was calibrated with standard gas before each experiment, and the sampling frequency was 1 time/min.
• Paint mist concentration detection: The laser dust detector (LD-5C) (Qingdao Lubo Environmental Protection Technology Co., Ltd., Qingdao, China) was used for detection, which follows the standard GB/T 16157-1996 [26] Determination of Particulate Matter and Gaseous Pollutants Emitted from Stationary Pollution Sources.
• Recovery rate calculation: The paint mist recovery rate was calculated using a weighing method: the filter cotton was weighed before and after the experiment with a precision electronic balance (accuracy ±0.01 g), and the actual intercepted paint mist quality was obtained; the VOC recovery rate was calculated by the inlet and outlet concentration method, which is consistent with the calculation formula in the simulation.

(3)

Data Processing and Uncertainty Analysis

The experimental data were processed as follows: (1) The abnormal data caused by equipment fluctuation were eliminated by the 3σ criterion; (2) the average value and standard deviation of three parallel experiments were calculated as the final result; and (3) an uncertainty analysis of the experimental results was carried out, considering the uncertainty of the detection equipment, the repeatability of the experiment, and the environmental fluctuation. The results showed that the relative expanded uncertainty of all test indicators was less than 5%, which meets the requirements of engineering test accuracy.

4.2. Experimental Design

4.2.1. Experimental Conditions and Equipment

(1)

Experimental Site: Spraying workshop of Zhoushan COSCO Shipping Heavy Industry Co., Ltd. (Zhoushan, China) (area 50 m × 30 m, ambient temperature 25 ± 2 °C, humidity 55 ± 5%, and no obvious air disturbance).

(2)

Test Piece: 10 m × 2 m ship hull steel plate (surface roughness Ra = 8 μm, consistent with actual shipbuilding materials).

(3)

Experimental Equipment:

(1)

Automatic-spraying robot with the designed recovery system (equipped with T35-11 axial flow fan (Dezhou Asia-Pacific Group Co., Ltd., Dezhou, China), glass fiber filter cotton (ε₁ = 0.6) (Suzhou Filter Material Co., Ltd., Suzhou, China), and honeycomb ACF (ε₂ = 0.44)); the control system parameters are set to be consistent with the above design, and the whole experimental process is controlled by the upper computer to ensure the consistency of working conditions.

(2)

VOC detector (model PGM-7340, range of 0–1000 ppm, and accuracy of ±2%) (Honeywell International Inc. (RAE Systems), San Jose, CA, USA);

(3)

Paint mist concentration detector (model LD-5C, range of 0–100 mg/m³, accuracy of ±5%);

(4)

Pressure difference sensor (range of 0–1000 Pa, accuracy of ±1 Pa) for monitoring filter material saturation;

(5)

Control variables: Spraying pressure of 0.4 MPa, spraying distance of 30 cm, robot moving speed of 0.5 m/s, and fan air volume of 1400 m³/h (consistent with simulation boundary conditions).

4.2.2. Experimental Grouping

The experiment was divided into three groups to comprehensively evaluate the system performance:

(1)

Group A (optimal parameter group): Filter cotton ε₁ = 0.6 + ACF ε₂ = 0.44 (simulation-derived optimal combination);

(2)

Group B (common parameter group): Filter cotton ε₁ = 0.5 + ACF ε₂ = 0.28 (conventional parameter combination in industry);

(3)

Control group: Traditional manual open spraying (no recovery system, and same paint and spraying parameters as experimental groups).

Each group was repeated three times, with each experiment lasting 30 min, and data were recorded every 5 min to avoid accidental errors.

4.3. Experimental Process

(1)

Preparatory stage: Clean the test piece surface, calibrate detection equipment, and preheat the robot system to ensure stable operation.

(2)

Spraying and data collection: Start the robot (or manual spraying), activate the recovery system (for experimental groups), and place detectors at the recovery system outlet (for experimental groups) and 1.5 m away from the spraying area (for control group) to record VOC concentration, paint mist concentration, and system pressure difference.

(3)

Post-experiment treatment: Collect filter cotton and ACF, weigh the intercepted paint mist quality, and calculate the actual recovery rate; regenerate ACF with 120 °C hot air to test regeneration efficiency.

The on-site spraying operation of the robot under complex working conditions is shown in Figure 12. Figure 12a shows the system’s operation under vertical hull surface spraying conditions, and Figure 12b shows the system’s operation under overhead (top-down) spraying conditions, which are the most common and challenging scenarios in ship hull painting. To compensate for the pollutant diffusion risk caused by gravity in these two scenarios, the system automatically increases the air curtain pressure by 0.1 MPa and the fan negative pressure by 15% through the built-in adaptive control logic. The experimental results show that the system’s recovery efficiency decreases by only 1.2% and 1.5% under vertical and overhead spraying conditions, respectively, compared with horizontal spraying, which intuitively verifies the strong adaptability of the system to the complex working conditions of ship painting.

4.4. Experimental Results and Analysis

4.4.1. Recovery Efficiency Comparison

The key experimental results are summarized in

Table 5. Experimental and simulation results comparison.

Group	Paint Mist Recovery Rate V/%		VOCs Recovery Rate β/%		Outlet VOCs Concentration (mg/m3)	Outlet Paint Mist Concentration (mg/m3)
	Experimental Value	Simulation Value	Experimental Value	Simulation Value
Group A	85.7 ± 0.8	86.2	43.2 ± 1.1	43.8	9.8 ± 0.5	0.9 ± 0.1
Group B	82.9 ± 1.0	83.6	33.8 ± 0.9	34.4	18.3 ± 0.8	1.5 ± 0.2
Control Group	19.5 ± 2.3	-	4.8 ± 0.6	-	84.7 ± 3.2	12.6 ± 1.0

, which compares the recovery efficiency and emission indicators of each group with simulation results for cross-validation:

(1)

Recovery efficiency and emission reduction performance: Group A (optimal parameters) achieves the highest purification performance, with a paint mist capture efficiency of 85.7 ± 0.8% and a VOC removal efficiency of 88.4% (calculated by the inlet–outlet concentration difference method, based on the on-site background VOC concentration of 84.7 mg/m³ in the shipyard). The experimental values are highly consistent with the simulation results, with a relative error of less than 1%, which is attributed to minor air leakage in the actual recovery hood and uneven paint mist distribution in the field. Compared with the control group (manual spraying without a recovery system), the proposed system achieves a 92.9% reduction in paint mist emissions and an 88.4% reduction in VOC emissions.

(2)

Emission indicators: The outlet VOC concentration of Group A is 9.8 mg/m³, and paint mist concentration is 0.9 mg/m³, both meeting the limits specified in GB 30981.2-2025 (VOCs ≤ 10 mg/m³, paint mist ≤ 1 mg/m³); Group B and control group exceed the standard to varying degrees, especially the control group with severe pollution.

4.4.2. Stability and Adaptability Analysis

(1)

Stability: Continuous operation test of Group A shows that the paint mist recovery rate decreases from 85.7% to 84.1% after 8 h, and the VOC recovery rate decreases from 43.2% to 41.5%, with a pressure difference increase of 85 Pa—indicating slow saturation of filter materials, which can meet the demand of daily continuous operation (6–8 h) in shipyards.

(2)

Adaptability: Parallel control experiments were carried out under vertical and overhead spraying conditions (Figure 11a,b) with the same control variables as the horizontal spraying experiment, and each working condition was repeated three times to ensure data reliability. The results show that under vertical spraying conditions, the average paint mist recovery rate of the system is 84.7 ± 0.7%, which is only 1.2% lower than that of horizontal spraying; under overhead spraying conditions, the average paint mist recovery rate is 84.4 ± 0.9%, which is 1.5% lower than that of horizontal spraying. The VOC recovery rate under the two complex working conditions also maintains a stable level, with a decrease of less than 2% compared with the horizontal condition. This result demonstrates the strong adaptability of the system to complex ship-painting scenarios, which is attributed to the real-time adjustment of air curtain pressure and fan negative pressure by the automatic control system, effectively compensating for pollutant diffusion caused by gravity.

Unlike existing recovery systems that operate independently of robots [8,13], our system is deeply coupled with the robot’s path-planning and navigation subsystem. The PLC controller pre-adjusts fan negative pressure and air curtain flow based on the robot’s real-time moving speed and pose, ensuring stable recovery efficiency during dynamic spraying. This solves the efficiency drop (by 30–40% in [15]) of conventional systems in vertical/overhead scenarios.

4.4.3. Full-Dimensional Cost–Benefit Analysis

(1)

Direct Material Cost Savings

After five regeneration cycles of ACF in Group A, the VOC adsorption capacity remains above 88% of the initial value. Compared with the one-time granular activated carbon scheme widely used in shipyards, the annual material consumption cost of a single system is only ~3200 RMB, a 62% reduction in annual material expenditure. Meanwhile, the system cuts VOC emissions by 88.4% and paint mist emissions by 92.9% per 100 m² spraying area, saving 150,000–200,000 RMB per year for a single shipyard in environmental treatment, pollutant monitoring, and non-compliance penalty costs.

(2)

Labor Cost Reduction

Traditional manual spraying requires three to four workers per operation, with an average labor cost of 450 RMB per person per day. In contrast, the robotic spraying system equipped with this recovery device only needs one worker for monitoring and auxiliary operation, reducing labor input by 75% and saving 280,000–350,000 RMB in annual labor costs per production line. In addition, the drawer-type replaceable design of filter materials shortens single replacement time to less than 10 min, cutting equipment maintenance labor time by 80% compared with traditional fixed filtration devices.

(3)

Spraying Efficiency Improvement

Compared with traditional manual spraying, the automatic-spraying robot with this system increases spraying operation efficiency by 2.5 times, with the single-day spraying area rising from 200 m² (manual) to 500 m². Meanwhile, the system’s air curtain containment and negative pressure recovery eliminate secondary cleaning of over-sprayed paint mist on-site, reducing post-spraying cleaning time by 90% and further improving the overall efficiency of the painting process.

(4)

Potential Revenue Growth from Faster Project Turnover

The above efficiency improvements shorten the painting cycle of a single 10,000-ton-class ship from 12–15 days to 5–7 days, increasing the annual shipbuilding capacity of a single dock from 24 ships to 50–60 ships. For shipyards, this not only reduces the construction cost of a single ship but also increases annual order capacity and project turnover rate, bringing an estimated 15–20% potential annual revenue growth. In addition, the system ensures full compliance with national emission standards, avoiding production suspension risks due to non-compliance and guaranteeing stable project progress.

4.4.4. VOC Removal Efficiency

The composition and concentration of VOCs at the system inlet and outlet were detected by gas chromatography–mass spectrometry (GC-MS, Agilent 7890A, Agilent Technologies, Inc., Santa Clara, CA, USA). The results show that xylene (280 mg/m³) and toluene (220 mg/m³) are the main inlet VOCs (accounting for 65% of total VOCs), followed by ethyl acetate (120 mg/m³) and benzene (60 mg/m³). After purification, the outlet concentrations of xylene, toluene, benzene, and ethyl acetate are 32 mg/m³, 25 mg/m³, 7 mg/m³, and 18 mg/m³, respectively—all meeting the emission standard. This confirms the targeted adsorption effect of the honeycomb ACF on the dominant VOC species in shipbuilding.

4.5. Experimental–Simulation Consistency Verification

The experimental results are highly consistent with the simulation calculations, with the maximum relative error of key indicators (paint mist recovery rate, VOC recovery rate) less than 2%. This verifies the rationality of the geometric model, grid division, and porous media parameter setting in the simulation, and confirms that the designed recovery system can stably achieve efficient pollutant recovery in practical applications.

The main reason for the minor difference is the idealized assumption in the simulation (e.g., uniform paint mist distribution, no air leakage), while the actual working conditions are affected by environmental airflow and equipment installation accuracy—providing direction for subsequent system optimization (e.g., optimizing recovery hood sealing structure).

The consistency between the experimental and simulation results was quantitatively evaluated by the relative error and the Pearson correlation coefficient. The maximum relative error of the core indicators (paint mist recovery rate, VOC recovery rate) between the experiment and simulation is less than 2%, and the Pearson correlation coefficient of the two sets of data is 0.996 (p < 0.01), which indicates a significant linear correlation between the experimental and simulation results. This high consistency verifies the correctness of the simulation model and the reliability of the system design method.

The high consistency between experimental and simulation results further confirms that the Realizable k-ε turbulence model can accurately describe the flow field and pollutant migration characteristics of the recovery system, and the model selection is fully reasonable.

4.6. Discussion

This study addresses the core bottleneck of synergistic VOCs and paint mist recovery for shipbuilding automatic-spraying robots, and the performance of the proposed system is systematically compared with the existing related literature to clarify its innovation and advantages. In terms of adsorption material selection, most existing studies focus on the performance test of single filter or adsorption materials, lacking targeted material combination schemes for the high-humidity and high-pollutant-concentration ship-painting scenario; in contrast, the composite material system of glass fiber filter cotton and honeycomb activated carbon fiber proposed in this study achieves graded interception of paint mist and deep adsorption of VOCs, with a paint mist recovery rate of 85.7% and VOC recovery rate of 43.2%, which are 12% and 18% higher than the conventional single-material recovery scheme for ship-spraying robots in the literature. For the system structure design, the existing canopy-type recovery systems mostly adopt a simple filtration–adsorption hierarchical design, which rarely considers the airflow disturbance caused by the robot’s dynamic operation, resulting in a significant efficiency drop (more than 5%) under complex working conditions such as vertical and overhead spraying; while our system integrates air curtain containment and negative pressure recovery with a deep coupling control logic, the recovery efficiency drop is only 1.2–1.5% under the same complex working conditions, showing stronger operational adaptability. In addition, unlike existing studies that optimize air curtain parameters or recovery system negative pressure separately, the coordinated control mechanism established in this study balances pollutant containment effectiveness and spraying operation stability, and the system can stably meet the emission limits of GB 30981.2-2025, which is difficult to achieve for most existing robot-mounted recovery systems. It should be noted that this study mainly focuses on the shipbuilding spraying scenario, and the parameter optimization for cross-industry applications, such as automotive spraying, needs to be further verified in subsequent research, which is also the core direction of our future work.

This study still has some limitations and practical challenges in long-term operation and maintenance under real shipyard conditions, which need to be addressed in subsequent research.

First, for long-term stability, real shipyard workshops are characterized by long-term high humidity (often over 80% in coastal areas), salt spray corrosion, and high paint dust concentration. Long-term continuous operation will cause gradual corrosion of the recovery box, blockage of air curtain perforated pipes by paint mist, and performance attenuation of the axial flow fan due to dust accumulation, which may reduce the system’s pollutant control efficiency over time. Periodic vibration from the robot’s frequent posture adjustment may also cause loosening of the mounting bracket and pipeline air leakage, affecting the stability of the system’s negative pressure environment.

Second, for on-site maintenance, the material replacement cycle set in standard tests is difficult to adapt to the large fluctuation of actual shipyard spraying load (peak 12 h daily operation during busy periods), which will accelerate the saturation of filter materials and increase maintenance frequency. Meanwhile, most shipyards lack on-site professional regeneration equipment for activated carbon fiber (ACF), and off-site regeneration will increase maintenance costs and system downtime. In addition, the current fault monitoring relies on on-board sensors, lacking remote real-time early warning functions for large-scale batch applications in shipyards, which further increases daily maintenance difficulty.

In addition, the current system verification is mainly oriented to the shipbuilding spraying scenario, and systematic parameter optimization for cross-industry applications has not been carried out. The system’s adaptive adjustment is based on pre-set rules, and fully autonomous collaborative optimization with the robot’s motion control has not been realized.

5. Conclusions and Future Outlook

5.1. Experimental–Simulation Consistency Verification

This study focuses on the design of a VOC and paint mist recovery system for ship automatic-spraying robots, forming a complete technical solution integrating “air curtain isolation–multi-stage adsorption–negative pressure recovery”. Key research results are as follows:

(1)

The system adopts a modular design, selecting glass fiber filter cotton (ε₁ = 0.6) and honeycomb activated carbon fiber (ACF, ε₂ = 0.44) (Ningbo Jianfeng Carbon Fiber Co., Ltd., Ningbo, China) as core adsorption materials, and matching with a T35-11 axial flow fan. The integrated structure ensures stable operation without interfering with the robot’s spraying trajectory.

(2)

Simulation calculations based on ANSYS Fluent verify that the system’s paint mist recovery rate reaches > 86% and VOC recovery rate > 43% under optimal parameters, meeting the requirements of GB 30981.2-2025. The flow field analysis shows uniform velocity distribution in the filtration module, avoiding local adsorption defects.

(3)

On-site shipyard experiments confirm that the system achieves a paint mist capture efficiency of 85.7 ± 0.8% and a VOC removal efficiency of 88.4% under actual working conditions, with outlet concentrations of both pollutants fully complying with the mandatory limits of GB 30981.2-2025. The experimental results are highly consistent with simulation data (relative error < 2%), verifying the reliability and stability of the proposed system.

(4)

The system exhibits strong adaptability to vertical and overhead spraying scenarios, with continuous operation stability for 8 h and low material consumption costs, providing an effective environmental protection solution for green shipbuilding.

(5)

Long-term stability and maintenance optimization: Optimize the anti-corrosion and anti-blocking design of the system structure to adapt to the harsh shipyard environment; develop portable on-site ACF regeneration equipment and build a remote monitoring and early warning platform to reduce the maintenance difficulty and operation cost of the system in long-term batch applications.

In addition, the universal modular design of the system endows it with good cross-industry scalability, which can be adapted to the green spraying needs of other industries represented by automotive manufacturing, a key scenario for Industry 5.0 sustainable development.

5.2. Future Outlook

To further enhance the system’s comprehensive performance and application value, future work will focus on four core directions:

(1)

Material performance optimization: Develop composite filter materials loaded with nano-modifiers to improve the interception efficiency of fine paint mist particles (<1 μm), and explore microwave regeneration technology to shorten ACF regeneration time to within 30 min.

(2)

Intelligent control upgrade: Integrate multi-sensor monitoring (VOC concentration, particle size, and pressure difference) and machine learning algorithms to realize real-time adjustment of fan air volume and air curtain pressure under dynamic working conditions, and integrate the recovery system performance optimization into the robot’s autonomous decision-making framework to realize collaborative optimization of spraying path-planning, autonomous navigation, and pollution recovery efficiency.

(3)

Miniaturization and scene expansion: Develop a modular miniaturized system (volume reduced by 40%) for small- and medium-sized ships and offshore platforms, adjust parameters to adapt to steel structure and marine engineering equipment spraying fields, and further optimize the system parameter configuration for the automotive spraying industry to expand its application in the green and sustainable manufacturing of Industry 5.0.

(4)

Low-carbon integration: Adopt frequency conversion fans and green energy supply to reduce system energy consumption by 15–20%, and explore catalytic combustion technology for resource utilization of captured VOCs, forming a “treatment-recycling” closed loop.