Development of Wastewater Treatment Networks Using the P-Graph Approach

A steady growth in the world population and industrialization has led to an increased demand for water. With such a limited supply, but a growing need for water, we must explore alternate methods in obtaining freshwater. Thus, as a population, it will become necessary to rely on treated water to satisfy these growing needs. There are various available treatment methods to purify water. Treatment methods are most effective when applied in series with other treatment technologies to meet purity standards. However, applying different technologies gives rise to a large number of possible treatment pathways. In this work, by using optimization, we identified treatment networks that meet purity requirements while minimizing the cost of purification using the P-graph approach. This was done by building up a case study using a municipal wastewater source. Through the generation of a superstructure containing all possible treatment technologies organized into a four-stage process, we simplified the model and formulated a mixed-integer nonlinear programming (MINLP) problem. Using the advanced branch and bound (ABB) solver in P-Graph we generated a list of all feasible treatment networks ranked from the least to the highest cost of purification. Using this software, we can find treatment solutions for different wastewater sources. It can be as simple as municipal wastewater to more complex sources, such as tannery wastewater. The tannery wastewater contains contaminants that can be more difficult to remove, for example, metals like chromium. Thus, we can adjust the P-Graph as such, implementing additional treatment technologies targeting the inputted contaminants in tannery wastewater to optimize impurity removal while minimizing costs.