A general mixed-integer nonlinear programming (MINLP) model is developed in this study to synthesize water networks in batch processes. The proposed model formulation is believed to be superior to the available ones. In the past, the tasks of optimizing batch schedules, water-reuse subsystems, and wastewater treatment subsystems were performed individually. In this study, all three optimization problems are incorporated in the same mathematical programming model. By properly addressing the issue of interaction between subsystems, better overall designs can be generated. The resulting design specifications include the following:  the production schedule, the number and sizes of buffer tanks, the physical configuration of the pipeline network, and the operating policies of water flows. The network structure can also be strategically manipulated by imposing suitable logic constraints. A series of illustrative examples are presented to demonstrate the effectiveness of the proposed approach.

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A design procedure to generate practical structures for the water-usage and -treatment networks is presented in this paper. The optimization strategies used in the proposed procedure are developed on the basis of a modified version of the existing nonlinear programming model. In particular, a systematic method is used to incorporate additional design options and a fixed number of repeated treatment units into the superstructure. Also, to account for the possible existence of unrecoverable solutes, the inequality constraints on their concentrations are added in the revised model formulation. To enhance convergence efficiency, a reliable method is developed in this work to produce a good initial guess. The advantages of this initialization technique are demonstrated with several examples adopted from the literature. Finally, several useful solution techniques to manipulate the structural properties of water networks are provided at the end of this paper. The effectiveness of our approach for creating favorable network structures is shown in the results of case studies.

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