Studying the formation of H2S in wastewater would actually help ease the maintenance costs of wastewater treatment systems. I would also like to thank engineer Salem Hegazy for his exceptional help and support during the laboratory work. I would like to thank my family, Muhammed Sherief, Sabitha Sherief and Waseem Sherief for their sacrifices and support throughout my life.
I would also like to thank my husband, Hisham Ashraf, for his support, and my in-laws; Ashraf Puthusseri and Naseema Vediyil. I would like to express my gratitude to Mohammed Asad and Abdul Mannan Zafar for their continued guidance in the completion of my work.
Introduction
- Overview
- Statement of the Problem
- Scope of the Research
- Summary of Paper
It also includes the modeling of H2S emission at the inlet of the wastewater treatment plant. H2S generation and release in a wastewater treatment plant has been identified as the main cause of odor problems and concrete corrosion. The effect of different water chemistry parameters and wastewater treatment plant design on gaseous H2S concentration.
This study highlighted that a neural model can be a valuable tool in H2S management in the wastewater treatment plant. The effect of wastewater components and flow characteristics on H2S production must be analyzed to accurately predict the generation of H2S in wastewater treatment networks and in turn control concrete corrosion.
Literature Review
- Formation and Generation of H 2 S
- Impact of H 2 S on the Integrity of Wastewater Systems
- Factors Affecting H 2 S Generation
- Management Techniques of H 2 S
The formation of ettringite increases the internal pressure in the cement structure, leading to the formation of cracks. They also found that the variation of H2S emission depending on temperature was also related to variations in the rate of sulfide formation and turbulence in the wastewater treatment system [39]. The formation of H2S and its emission can be controlled by increasing the Oxidation Reduction Potential (ORP) in the wastewater.
Nitrate in wastewater prevents the formation of H2S, as the presence of nitrate increased the redox potential [66]. Some models developed to predict H2S generation included organic matter concentration using BOD and COD in wastewater.
![Figure 1: Schematic representation of the biochemical reactions contributing to microbiologically induced corrosion of a concrete sewer pipe [10]](https://thumb-ap.123doks.com/thumbv2/azpdfco/10576238.0/25.892.365.647.131.388/schematic-representation-biochemical-reactions-contributing-microbiologically-corrosion-concrete.webp)
Methods
- Research Design
- Study Area - Sewer Field Description
- Field Investigation
- On Site Data Collection
- Laboratory Analysis of the Collected Samples
- Statistical and Correlation Analysis of Each Parameter
- H 2 S Prediction Modeling
- Artificial Neural Network Modeling
Various parameters were measured at the inlet of the wastewater treatment plant using a mobile collection unit. The air quality parameters measured during this project are the H2S concentration in the headspace, the temperature of the headspace air, the moisture content and the airflow in the headspace. The mobile data collection unit, installed in the wastewater storage tank, was used to collect relevant data at the Al-Saad Wastewater Treatment Plant (ASWWTP), Al Ain, UAE (Appendix 1).
The unit was placed at the main works of the ASWWTP just before the mechanical screens. Measurements of H2S concentrations, moisture content, headspace air flow, the temperature of wastewater and headspace air, flow rate and wastewater depth were manually recorded in a data entry sheet by personnel at the wastewater treatment plant. Parameters such as DO, COD, TSS, TDS, TOC, chloride, sulfates, sulfides and electrical conductivity in the wastewater samples were measured in the laboratory.
Regression analysis is used to investigate the relationship between a dependent variable, (H2S concentration) and independent variables (significant parameters). The model with the highest R-sq value was selected as the model for the collected data. MATLAB© 2018a software was used to quantify the amount of H2S generated at the main works of the wastewater treatment plant.
The collected data of important parameters were divided into 70%, 15% and 15% for training, validation and testing respectively. The validation data set was combined with the training data set to decide when to stop the training process for the model to have good generalization properties. The desired output from the model is the concentration of H2S, and therefore the number of target layers is 1.
Results and Discussion
Characterization of Incoming Wastewater
- Wastewater Quantity
- Wastewater Quality
It is clear that the velocity is highest every day at 20:00. the first sampling event has a longer range of velocity compared to the second sampling event. The first sampling event has a longer cyclic pattern due to the wider range of velocity. However, in terms of H2S concentration, the range is wider in the second sampling event. The lower half of the first sampling event has higher H2S concentration values, resulting in an increased cycle.
Both cycles have 8:00 PM on one end and 8:00 AM on the other, depicting the start and end of the cycle. The highest point of the first and second cycle is at 4:00 am where H2S concentrations are 232 ppm for both cycles. The average pH value of the wastewater was 7.07, the lowest and highest values were 6.87 and 7.27, respectively (Table 2).
COD has been reported as one of the influential factors in the generation of H2S in wastewater [91]. In the second sampling event, flow rate and COD fluctuate with a difference of 2 hours as shown in Figure 9. In the first sampling event, COD increases with increasing flow rate but has a contrasting time difference compared to the second sampling event.
The average COD value of the waste water over both days was 279.2 ppm, the lowest and highest values were 79.6 ppm and 546 ppm respectively (Table 2). It was observed that the temperature of the wastewater is higher at night and lower during the day. Irrespective of the temperature of the headspace air, the waste water temperature could not reflect the impact of the ambient temperature.
H 2 S Variation During the Sampling Events
- Effect of Flowrate
- Effect of Sulfate Concentration
- Effect of Sulfide Concentration
- Effect of Humidity on H 2 S Concentration
- Effect of Total Sulfur on H 2 S Concentration
During both sampling events, the H2S concentration was at its peak when the flow rate was approximately 3,347 m3/hr. In general, the H2S concentration was lowest during the day, and lowest on the day of the second sampling. During both samples, the H2S concentration was lowest when the flow rate was approximately 3,649 m3/hr.
The contour plot shows the wastewater flow rate in m3/hr on the y-axis against time in hours in steps of 5 on the x-axis, and the H2S concentration in ppm on the z-axis. The contour plot shows sulfate in ppm on the y-axis against time in hours in steps of 5 on the x-axis, and the H2S concentration in ppm on the z-axis. There was a decrease in the H2S concentration between 4 pm and 9 pm, when the sulfate concentration was around 250 ppm.
At lower pH H2S concentration was higher, while at higher pH H2S was observed lower as shown in Figure 17. The first peak in H2S concentration coincides with the peak in humidity, however humidity reaches a peak and remains constant for some time. Neglecting the rapid changes in H2S concentration at the peak, H2S and humidity peaks can be considered coincidental.
Both peaks of the second sampling event coincide with the peaks in H2S concentration and humidity. The contour plot shows total sulfur in ppm on the y-axis against time in hours in steps of 5 on the x-axis and H2S concentration in ppm on the z-axis. However, the temperature in the wastewater was highest during this period, which may explain the increase in the H2S concentration.
The collection of wastewater samples from an underground rising main has been thought to have numerous obstacles due to the lack of well-established technologies for sampling, analysis, monitoring and evaluation of H2S generation and other influencing parameters. Waste water from certain parts of the city, together with the waste water collected in tankers, flows through an enclosed structure. The H2S measurements and wastewater samples were collected at the inlet of the structure prior to screening.
No H2S removal treatment procedures have been performed for or in this area. The H2S concentration was measured in the headspace, and sulphide and sulphate in the wastewater, assuming that no other forms of sulfur would be present that would contribute to the total sulfur concentration. Parameters that are not included in the study also have little or no influence on the H2S generation.
Headspace temperature, wastewater temperature, flow rate, wastewater depth, wastewater velocity, moisture content, DO, COD, chloride, sulfate, sulfide, TSS, TDS, pH, were measured. electrical conductivity, TOC and total sulfur and analyzed. H2S concentration in ppm was taken as the response variable and wastewater temperature (TL) in oC, wastewater depth (WD) in m, total sulfur (TS) in ppm and humidity (H) were predictors of continuous. From the model, it is evident that H2S concentration increases with increasing wastewater temperature, humidity, total sulfur and decreases with increasing wastewater depth.
This means that 74.3% of the variation in H2S concentration is explained by the parameters used in this model, as shown in Table 3. The model developed in this study could serve as an important tool as it can be used to control H2S . However, this model is only useful for predicting H2S in a specific model run.
Generalized H 2 S Emission Prediction Model
The network was tested using values of TL, WD, H and TS as input data and H2S concentration as target data. The dataset used in the model is also limited to the location of data collection. Since artificial neural network (ANN) models are data-driven, the model can be improved by training with more data.
Conclusion
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