Comparison of H2S Gas Sensors: A Sensor Management Procedure for Sewer Monitoring

Hydrogen sulphide (H2S) emissions are one of the major problems associated with sewer networks. This gas, with its characteristic smell of rotten eggs is highly toxic and leads to the corrosion of sewer infrastructures. To protect cities and ensure the safety of sewer workers, sewers are commonly monitored using H2S gas sensors. In this work, three commercial H2S gas sensors for air quality monitoring were compared at two different sites in Berlin, Germany. Two of the sensors provide online access to data, while the other one is a data logger. Moreover, based on statistical measures (RMSE, MAE, MB, and a graphical analysis), we evaluated whether a rotation/exchange between data logger (reference) and online sensors is possible without significant differences in the gas measurements. Experimental evaluation revealed that measurement differences are dependent on the H2S concentration range. The deviation between sensors increases as the H2S concentration rises. Therefore, the interchange between reference and online sensors depends on the application site and the H2S levels. At lower ranges (0–10 ppm) there were no observed problems. Finally, to support practitioners on-site, a management procedure in the form of a decision-making tool is proposed for assessing whether gas sensors should be exchanged/rotated.

On the Use of Copula for Quality Control Based on an AR(1) Model

Manufacturing for a multitude of continuous processing applications in the era of automation and ‘Industry 4.0′ is focused on rapid throughput while producing products of acceptable quality that meet customer specifications. Monitoring the stability or statistical control of key process parameters using data acquired from online sensors is fundamental to successful automation in manufacturing applications. This study addresses the significant problem of positive autocorrelation in data collected from online sensors, which may impair assessment of statistical control. Sensor data collected at short time intervals typically have significant autocorrelation, and traditional statistical process control (SPC) techniques cannot be deployed. There is a plethora of literature on techniques for SPC in the presence of positive autocorrelation. This paper contributes to this area of study by investigating the performance of ‘Copula’ based control charts by assessing the average run length (ARL) when the subsequent observations are correlated and follow the AR(1) model. The conditional distribution of yt given yt−1 is used in deriving the control chart limits for three different categories of Copulas: Gaussian, Clayton, and Farlie-Gumbel-Morgenstern Copulas. Preliminary results suggest that the overall performance of the Clayton Copula and Farlie-Gumbel-Morgenstern Copula is better compared to other Archimedean Copulas. The Clayton Copula is the more robust with respect to changes in the process standard deviation as the correlation coefficient increases.

Comparison of Online Sensors for Liquid Phase Hydrogen Sulphide Monitoring in Sewer Systems

Hydrogen sulfide (H2S) related to wastewater in sewer systems is known for causing significant problems of corrosion and odor nuisance. Sewer systems severely affected by H2S typically rely on online H2S gas sensors for monitoring and control. However, these H2S gas sensors only provide information about the H2S emission potential at the point being monitored, which is sometimes inadequate to design control measures. In this study, a comparison of three market-ready online sensors capable of liquid-phase H2S detection in sewer systems was assessed and compared. Two of the three sensors are based on UV/Vis spectrophotometry, while the other adapted the design and principles of a Clark-type electrochemical microsensor. The H2S measurements of the sensors were statistically compared to a standard laboratory method at first. Following that, the performance of the online sensors was evaluated under realistic sewer conditions using the Berlin Water Company (BWB) research sewer pilot plant. Test applications representing scenarios of typical H2S concentrations found in sulfide-affected sewers and during control measures were simulated. The UV/Vis spectrometers showed that the performance of the sensors was highly dependent on the calibration type and measurements used for deriving the calibration function. The electrochemical sensor showed high sensitivity by responding to alternating anaerobic/anoxic conditions simulated during nitrate dosing. All sensors were prone to measurement disturbances due to high amounts of sanitary solids in wastewater at the study site and required continuous maintenance for reliable measurements. Finally, a summary of the key attributes and limitations of the sensors compared for liquid phase H2S detection is outlined.

Improve the performance of full-scale continuous treatment of municipal wastewater by combining a numerical model and online sensors

Mathematical models based on instant environmental inputs are increasingly applied to optimize the operation of wastewater treatment plants (WWTPs) for improving treatment efficiency. This study established a numerical model consisting of the activated sludge module ASM3 and EAWAG bio-P module, and calibrated the model using data from a full-scale experiment conducted in a WWTP in Nanjing, China. The calibrated model was combined with online sensors for water temperature, chemical oxygen demand, -N and -P to optimize and dynamically adjust the operation of the WWTP. The results showed that, compared to the original default operation mode, the effluent water quality was significantly improved after optimization even without supplementation of external carbon or alkalinity, and the required aeration rate in spring, summer, autumn, and winter was reduced by 15, 41, 33 and 11%, respectively. The study indicated that there was the potential for application of closed-loop automatic control to regulate operating parameters to improve wastewater treatment processes through the integration of data on influent characteristics and environmental conditions from sensors, and results from simulation models.

Online condition monitoring of misaligned meshing gears using wear debris and oil quality sensors

Purpose This paper aims to investigate the effect of misalignment on wear of spur gears and on oil degradation using online sensors. Design/methodology/approach The misalignment effect on gears is created through a self-alignment bearing, and is measured using laser alignment system. Several online sensors such as Fe-concentration sensor, moisture sensor, oil condition sensor, oil temperature sensor and metallic particle sensor are installed in the gear test rig to monitor lubricant quality and wear debris in real time to assess gearbox failure. Findings Offset and angular misalignments are detected in both vertical and horizontal planes. The failure of misaligned gear is observed at both the ends and on both the surfaces of the gear teeth. Larger-size ferrous and non-ferrous particles are traced by metallic particle sensor due to gear and seal wear caused by misalignment. Scanning electron microscope (SEM) images examine chuck, spherical and flat platelet particles, and confirm the presence of fatigue (pitting) and adhesion (scuffing) wear mechanism. Energy-dispersive X-ray spectroscopy analysis of SEM particles traces carbon (C) and iron (Fe) elements due to gear failure. Originality/value Gear misalignment is one of the major causes of gearbox failure and the lubricant analysis is as important as wear debris analysis. A reliable online gearbox condition monitoring system is developed by integrating wear and oil analyses for misaligned spur gear pair in contact.