Water Research

Mathematical modeling of autotrophic denitrification (AD) process with sulphide as electron donor

Authors: Guihua Xu, Feng Jun Yin, Shaohua Chen, Yuanjian Xu, Han-Qing Yu

Abstract

Autotrophic denitrification (AD) plays a critical role in nitrate removal from organic carbon-deficient wastewaters with a high level of nitrogen oxides. However, the AD process is not included in the current denitrification models, which limits the application of AD technology for wastewater treatment. In this work, a kinetic model for AD process involved 4 processes and 5 components with 9 parameters is established to describe the sulphide biooxidation and nitrite removal process. In this model, 4 oxidation–reduction reactions using sulphide as electronic donor in the AD process are taken into account. The model parameters are optimized by fitting data from the experiments with different combinations of sulphide, sulphur, sulphate, nitrate and nitrite at various concentrations. Model calibration and validation results demonstrate that the developed model is able to reasonably describe the removal rates of nitrate, nitrite, sulphide and sulphur in the AD process. The model simulation results also show that the sulphur term (η(S)) in the kinetic equations of nitrate, nitrite, sulphur and sulphate remains constant, rather than being controlled by its own concentration. Furthermore, with this model the products of sulphide biooxidation in the AD process, sulphur and sulphate, and their concentrations can be accurately predicted. Therefore, this model provides a strategy to control the sulphate concentration below the discharge limits or recover sulphur as the main end product from sulphide biooxidation.

Objective

Q: What problem does this paper attempt to solve?

Kimi: This paper primarily aims to address how to improve the understanding and application of the autotrophic denitrification (AD) process through mathematical modeling. Autotrophic denitrification is an important wastewater treatment technology that can convert nitrates in wastewater into nitrogen gas by using inorganic substances such as sulfide as electron donors, thereby removing nitrogen oxides, especially in conditions lacking organic carbon.

The main objectives of the paper include:

1. Establishing a Kinetic Model: Developing a kinetic model that includes multiple redox reactions and components to describe the biooxidation of sulfide and the removal process of nitrites.

2. Parameter Optimization: Optimizing the parameters within the model using experimental data to enhance the model's predictive accuracy.

3. Model Validation: Validating the model's predictive capabilities and accuracy by comparing it with experimental data from other researchers.

4. Controlling Sulfate Concentration: Using the model to control the generation of sulfate during the AD process, ensuring its concentration is below discharge standards, or recovering sulfur from the biooxidation of sulfide.

5. Predicting Oxidation Products of Sulfide: Accurately predicting the concentrations of sulfide oxidation products (such as sulfur and sulfate) in the AD process to provide a basis for process control.

Through this research, the paper aims to provide environmental engineers with a powerful tool to more effectively design, operate, and optimize the autotrophic denitrification process, reduce the emission of nitrogen oxides during wastewater treatment, and achieve the recovery and reuse of resources. This has significant practical implications for environmental protection and sustainable development.

Related

Q: What are the relevant studies?

Kimi: The related research mentioned in this paper mainly focuses on the kinetics of the autotrophic denitrification (AD) process, the reaction mechanisms, and the application of sulfide as an electron donor in the AD process. Here are some research areas and specific works related to the paper:

1. Kinetic Studies of Autotrophic Denitrification: Research on the reaction kinetics in the AD process, including the rates of biooxidation reduction reactions of sulfide and nitrates, half-saturation constants, and microbial growth rates.

2. Application of Sulfide as an Electron Donor: Exploring the efficiency and mechanism of sulfide as an electron donor in the AD process for the removal of nitrates and nitrites.

3. Control of Sulfate Concentration: Studying how to adjust operational parameters to control the generation of sulfate to meet discharge standards or achieve sulfur recovery.

4. Mathematical Modeling of the AD Process: Developing and validating mathematical models that describe the AD process to predict and optimize process performance.

5. Microbial Community Analysis: Investigating the microbial community structure involved in the AD process and the roles of different microorganisms in sulfide oxidation and nitrate reduction.

6. Application of AD Technology in Environmental Engineering: Applying AD technology in actual wastewater treatment projects to evaluate its treatment effects and cost-effectiveness under different conditions.

Specifically, the paper cites some related research works, such as:

• The study by An, S., Tang, K., Nemati, M. (2010) focused on the effects of oil reservoir microbial cultures in simultaneous biodesulfurization and denitrification processes.
• Mora, M., Dorado, A.D., Gamisans, X., Gabriel, D. (2015a) investigated the kinetics of autotrophic denitrification with thiosulfate.
• Xu, G., Peng, J., Feng, C., et al. (2015) evaluated the performance of simultaneous autotrophic and heterotrophic denitrification processes and bacterial community structure.

These studies provide important scientific foundations for understanding the AD process and serve as a reference for further technological development and optimization.

Solution

Q: How the paper solves this problem?

Kimi: The paper addresses the mathematical modeling issue of the autotrophic denitrification (AD) process through the following methods:

1. Establishing a Kinetic Model: The researchers developed a kinetic model that includes 4 processes and 5 components, covering the biooxidation of sulfide and the removal of nitrates and nitrites in the AD process. The model incorporates redox reactions using sulfide as an electron donor.

2. Parameter Optimization: Through experimental data, the researchers optimized the 9 parameters within the model. These parameters include the maximum specific growth rate, electron distribution coefficients, half-saturation constants, etc., and their optimal values were determined by fitting the experimental data.

3. Model Calibration and Validation: The researchers calibrated and validated the model using different experimental datasets, which included various concentrations of sulfide, sulfur, sulfate, nitrates, and nitrites. The model's predictions matched the experimental data, proving the model's effectiveness.

4. Analysis of the Sulfur Term (η(S)): The researchers discussed the representation of the sulfur term (η(S)) in the kinetic equations. By comparing the model's predictive results, they determined that the sulfur term is better expressed as a constant rather than the traditional Monod type during the AD process.

5. Prediction and Control of Products: Using the established model, the researchers were able to accurately predict the concentrations of sulfide oxidation products (sulfur and sulfate) in the AD process, thus providing strategies for controlling sulfate concentration and recovering sulfur.

Through these methods, the paper not only enhances the understanding of the AD process but also provides a powerful tool for engineering applications, enabling more effective design, operation, and optimization of the autotrophic denitrification process. This reduces the emission of nitrogen oxides during wastewater treatment and achieves the recovery and reuse of resources, which has significant practical implications for environmental protection and sustainable development.

Experiment

Q: What experiments were done for the paper?

Kimi: Based on the content provided from the paper, the authors conducted the following experiments to support their research and model development:

1. Experimental Setup: A bench-scale Sequencing Batch Reactor (SBR) with a working volume of 10 liters was constructed to serve as the AD reactor. This reactor was operated in a batch mode with a hydraulic retention time of 18 hours, settling time of 2.0 hours, drainage time of 0.20 hours, influent time of 0.40 hours, and idle time of 3.0 hours.

2. Synthetic Wastewater Composition: The components of synthetic wastewater included Na2S·9H2O, KNO3, NH4Cl, KH2PO4, NaHCO3, CaCl2, MgCl2·6H2O, and trace elements. HCl was used to adjust the wastewater pH to approximately 7.2. Background concentrations of S2-, SO42-, NO3-, and NO2- in the water were also recorded.

3. Inoculated Sludge: Inoculated sludge was collected from the Shiweitou Municipal Wastewater Treatment Plant in Xiamen, China, with a suspended solid (SS) concentration of 3214 ± 212 mg L-1.

4. Reactor Operation Conditions: The reactor temperature was set at 30 ± 0.5°C, as this temperature range is optimal for the growth of autotrophic denitrifying bacteria. An electric hand blender at 200 rpm was used to achieve uniform mixing. After the reactor reached a steady state, the SS concentration varied slightly and remained at 1480 ± 157 mg L-1.

5. Solids Retention Time: Considering the slow growth of autotrophic denitrifying bacteria (ADB), the solids retention time was set at a relatively long period of 30 days by discharging excess sludge to enrich autotrophic sludge.

6. Experimental Operations: After reaching a steady state, experiments on the AD reactor were conducted at two S/N (sulfide/nitrogen) molar ratios. Sufficient time was given to ensure the sludge adapted to the changes in substrate before moving on to the next test.

7. Data Collection: Data on the concentrations of S2-, SO42-, NO3-, and NO2- were collected, as well as performance changes of the reactor over the course of one year.

These experiments aimed to simulate and study the AD process, providing experimental data for the establishment and validation of the mathematical model. Through these experiments, the researchers were able to optimize the model parameters and verify the model's accuracy in predicting the chemical transformations and removal efficiencies in the AD process.

Exploration

Q: What are the points that can be explored further?

Kimi: Although the paper provides an in-depth study and mathematical modeling of the autotrophic denitrification (AD) process, there are still several areas that can be further explored:

1. Further Validation of the Model: While the model has been validated with experimental data, its applicability and accuracy under different operating conditions, types of wastewater, or microbial communities still require further research and validation.

2. Role of Microbial Communities: The dynamic changes of microbial communities during the AD process, the interactions between different microbial populations, and their impact on the efficiency of AD are areas worthy of further investigation.

3. Process Optimization: Exploring different operational strategies, such as altering the ratio of sulfide to nitrate input, adjusting pH levels, and optimizing reactor design, to further improve the efficiency and stability of the AD process.

4. Influence of Environmental Factors: Investigating the effects of environmental factors like temperature, pH, and dissolved oxygen on the AD process, and how to control these factors in practical applications to achieve the best treatment outcomes.

5. Economic and Feasibility Analysis: Assessing the economic viability of AD technology, including cost-benefit analysis, energy consumption evaluation, and the feasibility of large-scale application.

6. Application of New Materials and Technologies: Researching the application of new bioreactor materials, biofilm technologies, or nanotechnology in the AD process to enhance treatment efficiency and reduce costs.

7. Stability in Long-Term Operation: Studying the stability and sustainability of AD systems during long-term operation, including solutions for issues like sludge aging, system clogging, and corrosion.

8. Integration with Other Treatment Technologies: Exploring the integration of AD technology with other wastewater treatment methods (such as aerobic treatment, anaerobic digestion, etc.) to achieve more efficient wastewater treatment and resource recovery.

By pursuing these further research points, a more comprehensive understanding and optimization of the AD process can be achieved, promoting its application in wastewater treatment and environmental protection.

Summary

Q: Summarize the main points of the paper.

Kimi: The main content of the paper "Mathematical modeling of autotrophic denitrification (AD) process with sulphide as electron donor" can be summarized as follows:

1. Research Background: Autotrophic denitrification (AD) is an important technology for removing nitrates from wastewater under conditions lacking organic carbon, using inorganic electron donors such as sulfide. Existing denitrification models do not include the AD process, which limits its application in wastewater treatment.

2. Research Objective: To develop a mathematical model to describe and optimize the AD process using sulfide as an electron donor, improving the understanding and control of the AD process for more effective removal of nitrates and nitrites from wastewater.

3. Methodology:

   • A kinetic model including 4 processes and 5 components was established, covering the biooxidation of sulfide and the removal of nitrates and nitrites.
   • Model parameters were optimized using experimental data, including maximum specific growth rate, electron distribution coefficients, and half-saturation constants.
   • The model was calibrated and validated to ensure its ability to accurately predict the transformation and removal efficiency of chemicals in the AD process.

4. Experimental Design:

   • A bench-scale Sequencing Batch Reactor (SBR) was used for the experiments.
   • Synthetic wastewater contained components such as sulfide, nitrates, ammonium chloride, phosphates, and bicarbonate.
   • Inoculated sludge was collected from a municipal wastewater treatment plant, and a one-year stable operation experiment was conducted in the reactor.

5. Key Findings:

   • The model could reasonably describe the removal rates of nitrates, nitrites, sulfides, and sulfur in the AD process.
   • Control strategies for sulfate generation could be achieved through model predictions to meet discharge standards or recover sulfur from sulfide biooxidation.
   • The model predictions matched the experimental data, proving the model's effectiveness and applicability.

6. Conclusion: The paper successfully developed a mathematical model that provides a powerful tool for the design, operation, and optimization of the AD process, helping to improve wastewater treatment efficiency, reduce nitrogen oxide emissions, and achieve sustainable resource utilization.

This paper offers new perspectives and tools for the development and application of autotrophic denitrification technology, providing valuable references for researchers and engineers in the field of environmental engineering.

More

These are just a few basic FAQs for understanding a paper with Kimi. if you would like to discuss the paper further with it, click here to jump to the Kimi web version and start a new session related to the paper.