The application and impact analysis of intelligent sensor technology in the detection of ocean high salt spray particles

Marine high-salt spray particles significantly accelerate vessel corrosion, with chloride ions being the primary corrosive component. This study presents a funnel-shaped intelligent sensor system integrating ion-selective electrode technology with enhanced neural network algorithms. The proposed design employs an improved Sparrow Search Algorithm-optimized Back Propagation Neural Network for temperature compensation, addressing the critical challenge of thermal drift in marine environments. Experimental results demonstrate the system's superior performance: maximum relative error of 1.786% across 0.001–1 mol/L chloride concentrations, with average error reduced to 0.972%–59.3% lower than conventional compensation methods. The sensor maintains near-theoretical sensitivity while achieving 0.02 mV/°C temperature coefficient through the proposed compensation mechanism. This advancement enables precise real-time monitoring of salt spray corrosion factors, providing a technical foundation for extending marine vessel service life through proactive maintenance strategies. A new funnel-shaped intelligent sensor is developed, which combines ion-selective electrode technology with an improved neural network algorithm. By introducing the sparrow search algorithm for optimization, the detection accuracy of chloride ions and the temperature compensation capability are enhanced, providing an efficient solution for the real-time monitoring of Marine high salt spray particles.