Thesis Format

Integrated Article

Modeling and multi-objective optimization of wastewater treatment process

Author

Navneet Kaur, The University of Western OntarioFollow

Degree

Doctor of Philosophy

Program

Chemical and Biochemical Engineering

Supervisor

Ray, Ajay

Abstract

Wastewater is water that has already been used and requires treatment before releasing it into natural water bodies like lakes and rivers. Wastewater treatment is the process of removing impurities from wastewater. In this treatment process, the impurities are removed and converted to effluent. This effluent is returned to the water cycle with minimum impact on the environment. Conventional treatment plants consist of three stages: primary, secondary, and tertiary treatment. Treatment of wastewater is quite complicated because of the number of stages involved in this process. Most wastewater treatment plants are operated manually therefore at times it becomes difficult for operators to maintain desired effluent quality. Modeling and simulation technique is suggested to improve and predict the performance of the plant.

Mechanistic models involve fundamental equations to model the process, since wastewater treatment is a complicated process artificial neural network is suggested to model the process. This is a data-driven approach that identifies patterns between input and output data. This type of technique is called black-box modeling. The past four years' data is analyzed and used to predict the effluent quality of the plant. The effluent quality is measured in terms of four major pollutants namely biochemical oxygen demand, suspended solids, total phosphorus, and ammonia. 70% of the total data was used for training purposes and 30% was used for validation purposes. The correlation coefficient between the modeled values and actual values was around 0.97.

To minimize the concentration of the pollutants in the effluent stream multi-objective optimization is suggested. A genetic algorithm is used to solve multi-objective optimization of the treatment plant. An equalization tank or buffer system is suggested to counterbalance the fluctuating flow and composition of influent to the treatment plant. The decision variables associated with this process are the temperature of the influent stream, total sewage flow, biochemical oxygen demand, suspended solids, pH, total phosphorus, and ammonia of the influent stream. The optimizer was able to minimize the concentration of pollutants in the effluent stream and comply with the strict effluent regulations.

Summary for Lay Audience

The purpose of wastewater treatment is to remove the impurities before discharging them back into the environment. Untreated wastewater is harmful to both humankind and the environment. Improper operation of WWTP can cause environmental and various health issues like cholera and dysentery. The optimal operation of WWTP can improve efficiency and reduce the costs associated with various processes. In this research work, a multi-objective optimization approach has been used to minimize the concentration of pollutants in the effluent stream instead of a single-optimization approach. In the real world, multi-objective problems with conflicting objectives are frequently encountered. In this case, a set of equally good solutions is generated, also known as the Pareto set. Though sometimes it becomes difficult for the decision maker to choose a single optimal solution from a set of optimal solutions.

Wastewater treatment is a complex system and it is difficult to explore various design ideas on a pilot plant. Modeling helps in understanding how a system would behave in various conditions without experimentation. A WWTP model is a representation of physical and chemical processes involved in the purification of wastewater. In my research work, a black-box modeling approach has been employed to model WWTP. This type of modeling is based on the input-output behavior of the process in contrast to physical modeling which is time-consuming. A model based on ANN was developed to predict the quality of effluent stream.

To minimize the concentration of the pollutants in the effluent stream multi-objective optimization is suggested. A genetic algorithm is used to solve multi-objective optimization of the treatment plant. An equalization tank or buffer system is suggested to counterbalance the fluctuating flow and composition of influent to the treatment plant. The decision variables associated with this process are the temperature of the influent stream, total sewage flow, biochemical oxygen demand, suspended solids, pH, total phosphorus, and ammonia of the influent stream. The optimizer was able to minimize the concentration of pollutants in the effluent stream and comply with the strict effluent regulations.

Recommended Citation

Kaur, Navneet, "Modeling and multi-objective optimization of wastewater treatment process" (2023). Electronic Thesis and Dissertation Repository. 9146.
https://ir.lib.uwo.ca/etd/9146