(702c) Systematic Design of Wastewater Treatment Networks Using the P-Graph Approach: A Tannery Waste Case Study | AIChE

(702c) Systematic Design of Wastewater Treatment Networks Using the P-Graph Approach: A Tannery Waste Case Study


Aboagye, E. A. - Presenter, Rowan University
Desai, M., Rowan University
Tran, C., PSEG Institute for Sustainability Studies
Yenkie, K., Rowan University
Friedler, F., Pazmany Peter Catholic University
Cabezas, H., University of Miskolc
Freshwater contributes to just 2.5% of the globally water availability and the rest is in the form of seawater and ice. Still we are very much dependent on it for use in domestic, agricultural, and industrial sectors. It has been estimated that the global demand for water will exceed availability by 40% by the year 2030 if the operations continue as usual. Furthermore, the water demand for industrial purposes is anticipated to increase by 400% by the year 2050. Thus, we need to find alternatives to meet these growing demands.

After use, the water gets contaminated with pollutants and therefore, becomes unsafe for drinking and other purposes. About 80% of wastewater is released into the environment without adequate treatment. By treating wastewater, this growing demand can be satisfied effectively. Wastewater is mostly treated in three main stages namely, primary stage, secondary stage, and tertiary stage. Sometimes pretreatment is necessary to remove large solid particles or cause charged suspended solid particles to settle. There are also various technologies that can be categorized into the different stages of the treatment process due to their efficiencies and driving forces for contaminant removal. The challenge then is which technology to select at each stage of the treatment to meet purity standards, as well as minimize purification cost. With the application of discrete optimization, the challenge to meet purity requirements with minimum purification cost can be solved (Yenkie, 2019; Yenkie et al., 2019).

One of the biggest sources of wastewater is the tannery industry. Most tannery wastewater contains higher dissolved organic carbons and total suspended solids (Inc et al., 2002). Further, chromium ions are predominantly found in these wastewater streams in higher concentrations. Since chromium possesses a higher health risk to humans, there is the need to treat these wastewater streams to reduce these chromium ions to within the accepted concentration levels before being reused for the particular purpose or discharge safely for natural remediation. Thus, our approach is demonstrated via a representative case study of tannery wastewater.

In this work, a stagewise wastewater treatment approach is implemented. Modular design based on superstructure generation which comprises all the possible treatment technologies, flows, connections, bypasses, and mixers is applied (Yenkie et al., 2019). The P-graph approach, which is optimization-based, has been used extensively to evaluate problems of combinatorial nature, such as supply chain, evacuation routes design, and process network synthesis (PNS)(Friedler et al., 1998, 1992, p.; Vance et al., 2013). The advantage of using P-graph studio is that not only are the best feasible structures in terms of cost obtained, but also a ranked list of all feasible near-optimal solutions is generated. With this information, consideration of additional performance criteria such as operability, flexibility, and sustainability can be considered before a holistic decision is taken. Therefore, the P-graph approach was used to solve the representative case study. The selection of technologies at each stage of the treatment process was formulated as a mixed integer programming (MIP) problem. The problem was then solved using the advanced branch-and-bound (ABB) which is an MIP solver in P-graph studio.


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Yenkie, K.M., Burnham, S., Dailey, J., Cabezas, H., Friedler, F., 2019. Generating Efficient Wastewater Treatment Networks: an integrated approach comprising of contaminant properties, technology suitability, plant design, and process optimization, in: Computer Aided Chemical Engineering. Elsevier, pp. 1603–1608. https://doi.org/10.1016/B978-0-12-818634-3.50268-X