Surveillance practice and automatic data algorithm of sniffing telemetry for SO2 emissions from ship exhaust in Tianjin Port

Abstract

Aiming at regulating SO2 emissions from ships, ship exhaust telemetry (SET) can be used as a low-cost and high-efficiency technology for the pre-detection of the S content of ship fuel. The sniffing method is the most accurate SET technology. In this study, eight SETs based on the sniffing method were built in Tianjin Port. The concentrations of CO2, SO2, and NOx in ship exhausts were monitored from 1 May to 31 August 2021. An automatic data algorithm was developed to identify peak signals of CO2, SO2, and NOx, calculate the S content of ship fuel, and trace emissions from ships. First, an automatic recognition algorithm for a gas-sniffing signal based on the incremental correlation threshold was established. Second, an automatic calculation algorithm for the S content of ship fuel based on the time series of SO2 peak was generated. Finally, the traceability-locking algorithm for emissions from ships was created by calculating the plume path integral. The recognition rate of the wave-crest signal was demonstrated to be more than 95%, and the positioning error of the ship was estimated at ∼100 m, which could satisfy the accuracy requirements of ship identification. Overall, the peak data of the curves of the gas concentrations monitored (n = 24,292) were obtained during the four-month operation of the SETs. The detection rate of ship exhaust was 62.2%, and the emission reduction of SO2 was 44.6 tons. The accuracy and validation of the algorithm were tested in surveillance practice in Tianjin Port. The SET and automatic data algorithm for SO2 emissions significantly promoted the efficiency of supervision and the emission reduction of SO2. Thus, a continuous online, unattended, and low-cost supervision strategy for the S content of ship fuel without boarding was established.