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Selection of a multi-stage system for biosolids management applying genetic algorithm.

Yitzhak Stramer¹, Asher Brenner, Stuart B Cohen

¹Unit of Environmental Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel.

Environmental Science & Technology

|June 26, 2010

Summary

A Genetic Algorithm (GA) optimized a sequential biosolids management process, identifying cost-effective solutions for wastewater planners. This approach aids in evaluating new technologies and understanding operational cost impacts.

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Area of Science:

Environmental Engineering
Operations Research

Background:

Biosolids management is a critical component of wastewater treatment.
Optimizing treatment processes is essential for economic feasibility and environmental compliance.

Purpose of the Study:

To develop and test an economic analysis and feasibility study for a sequential biosolids management process.
To utilize a Genetic Algorithm (GA) for identifying trends and suggesting solutions for biosolids management.

Main Methods:

A sequential model of the "Biosolids Process Train" was developed.
A Genetic Algorithm (GA) was employed to simulate design scenarios and analyze multiple objective functions, variables, and constraints.
Sensitivity analysis was performed on operating expenses.

Main Results:

The GA provided a range of "good approximations" for biosolids management alternatives.
The model allowed for observation of the effects of parameter modifications on downstream processes and supernatant return flow.
Sensitivity analysis highlighted the impact of fuel, electricity, and labor costs on total management cost and treatment sequence selection.

Conclusions:

The GA is a robust tool for simulating biosolids management scenarios and suggesting optimal solutions.
The sequential modeling approach effectively demonstrates the impact of process modifications.
Understanding cost sensitivities is crucial for selecting appropriate biosolids treatment sequences.