Designing effective wastewater treatment networks is challenging because of the large number of treatment options available for performing similar tasks. Each treatment option has variability in cost and contaminant removal efficiency. Moreover, their mathematical models are highly nonlinear, thus rendering them computationally intensive. Such systems yield mixed-integer nonlinear programming models which cannot be solved properly with contemporary optimization tools that may result in local optima or may fail to converge. Herein, the P-graph framework is employed, thus generating all potentially feasible process structures, which results in simpler, smaller mathematical models. All potentially feasible process networks are evaluated by nonlinear programming resulting in guaranteed global optimum; furthermore, the ranked list of the n-best networks is also available. With the proposed tool, better facilities can be designed handling complex waste streams with minimal cost and reasonable environmental impact. The novel method is illustrated with two case studies showing its computational effectiveness.

In the tannery industry approximately, 30 - 35 m³ of wastewater (WW) is generated per ton of rawhide processed. The WW comprises high concentrations of salts, ammonia, dye, solvents, and chromium. Of particular interest is chromium, which has been proven to cause dermatological, developmental, and reproductive issues on exposure. Thus, there is a need for appropriate treatment of the tannery WW before it is discharged for natural remediation. However, designing a treatment process is multifaceted due to the availability of multiple technologies that can perform similar tasks and the complex composition of waste streams. This necessitates the treatment to be performed in stages namely, primary, secondary, and tertiary. In some cases, pretreatment is required to enhance the recovery in the following stages. Due to the combinatorial nature of this problem, the P-graph approach, which uses principles from graph theory, can be used to synthesize a treatment pathway by selecting appropriate technologies at each stage, while meeting required purity specifications. Furthermore, the P-graph approach can provide alternate feasible treatment structures ranked based on Economics as well as Sustainability indicators, such as the Sustainable Process Index (SPI). In this work, a tannery WW case study is investigated with multiple stages and treatment technologies. A complex maximal structure is generated comprising all possible technologies, flows, connections, bypasses, mixers, and splitters. The models for each technology involve capital and operating costs, efficiency, and SPI at each stage of the treatment process. This problem is formulated in P-graph and solved using the Accelerated Branch-and-Bound algorithm.