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

An economic analysis and feasibility study of a sequential biosolids management process was developed and tested using Genetic Algorithm (GA). The algorithm was used to identify trends and behaviors of the "Biosolids Process Train". This heuristic method of analysis is robust in that it will not only simulate different design scenarios, its analysis will also suggest possible solutions which meet predetermined requirements. This concept was adopted because GA's biggest advantage is the capability to analyze multiple objective functions, design variables, and constraints. The range of "good approximations" provided by the GA solutions could be useful for municipal wastewater planners who need to search for potential alternatives and evaluate new technologies for managing biosolids. The unit processes in the model were arranged sequentially so the effect modifications to thickening and dewatering parameters could easily be observed further along in the process. The model was extended to examine the supernatant return flow quality and the potential impact on the wastewater treatment plant. Results from a sensitivity analysis on operating expenses reveals the impact that fluctuations in fuel, electricity, and labor costs can have on the total biosolids management cost as well as the selection of the appropriate treatment sequence.