Water recycle networks

For a given system, the synthesis of wastewater reduction and water conservation networks entails answering several questions including  

In addition, several review articles on process integration design tools for energy conservation and wastewater reduction design have been published [15-17], Finally, several books solely devoted to these topics in detail have recently been published [18,19]. 

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Graphical Design Tools for Retrofitting Processes for Wastewater Reduction by Designing Water Recycle Networks... 

These two graphs will be illustrated using the water stream data provided in Table 6.2 below. A limitation of the graphical approach is that it can only be used to identify water recycle networks for streams containing only a single contaminant. As indicated previously, mathematical optimization approaches can be used to identify water recycle networks for streams involving multiple contaminants [14],... 

Table 6.2 Example water recycle network stream data [8],...

A shortcut graphical technique for water conservation design for retrofit processes has also been provided. Two important graphs are provided to create initial water recycle networks for consideration in retrofit processes. Of particular importance is the usefulness... 

Appendix 6A Illustrating the Water Recycle Network Design Guidelines... 

The results for this scenario were obtained using GAMS 2.5/CPLEX. The overall mathematical formulation entails 385 constraints, 175 continuous variables and 36 binary/discrete variables. Only 4 nodes were explored in the branch and bound algorithm leading to an optimal value of 215 t (fresh- and waste-water) in 0.17 CPU seconds. Figure 4.5 shows the water reuse/recycle network corresponding to fixed outlet concentration and variable water quantity for the literature example. It is worth noting that the quantity of water to processes 1 and 3 has been reduced by 5 and 12.5 t, respectively, from the specified quantity in order to maintain the outlet concentration at the maximum level. The overall water requirement has been reduced by almost 35% from the initial amount of 165 t. 

Fig. 4.5 Water reuse/recycle network corresponding to fixed outlet concentration (Majozi, 2005)...

The overall model for scenario 1, which is MILP, entails 1320 constraints, 546 continuous and 120 discrete/binary variables. 52 nodes were explored in the branch and bound algorithm and the optimal freshwater requirement of 1767.84 kg was reached in 1.61 CPU seconds. Figure 4.9 shows the corresponding water reuse/recycle network. 

The formulation for this scenario entails 1411 constraints, 511 continuous and 120 binary variables. The reduction in continuous variables compared to scenario 1 is due to the absence of linearization variables, since no attempt was made to linearize the scenario 2 model as explained in Section 4.3. An average of 1100 nodes were explored in the branch and bound search tree during the three major iterations between the MILP master problem and the NLP subproblem. The problem was solved in 6.54 CPU seconds resulting in an optimal objective of 2052.31 kg, which corresponds to 13% reduction in freshwater requirement. The corresponding water recycle/reuse network is shown in Fig. 4.10. 

The corresponding mathematical formulation entails 5534 constraints, 1217 continuous and 280 binary variables. An average of 4000 nodes were explored in the branch and bound search tree. The solution required three major iterations and took 309.41 CPU seconds to obtain the optimal solution of 1285.50 kg. This corresponds to 45.53% reduction in freshwater demand. A water reuse/recycle network that corresponds to this solution is shown in Fig. 4.11. 

Fig. 4.12 Water reuse/recycle network for scenario 4 - first case study...

The overall model for this scenario involves 5614 constraints, 1132 continuous 280 binary variables. Three major iterations with an average of 1200 nodes in the branch and bound search tree were required in the solution. The objective value of 1560 kg, which corresponds to 33.89% reduction in freshwater requirement, was obtained in 60.24 CPU seconds. An equivalent of this scenario, without reusable water storage, i.e. scenario 2, resulted in 13% reduction in fresh water. Figure 4.12 shows the water recycle/reuse network corresponding to this solution. 

Fig. 5.4 Water reuse/recycle network for minimum water use - model Ml (Majozi, 2006)...

Fig. 5.6 Water reuse/recycle network for minimum reusable water storage - model M2 (Majozi,... 

Another problem of significance is the optimum policy of water recycling. This subject is in itself substantial and cannot be handled here. An economical approach involves optimal allocation of streams, both as flow rates and contaminant concentration. The analysis may be performed systematically with tools based on the concept of water pinch and mass-exchange networks . This subject is treated thoroughly in specialized works, as in the books of El-Halwagi [19] and Smith [20]. A source-sink mapping technique developed around the acrylonitrile plant may be found in the book of Allen and Shoppard [21]. 

The pinch point(s) divides the network into water recycle sub-networks. No waste should be discharged from sources below the pinch. No freshwater should be used in any sink above the pinch. 

The pinch point(s) divides the network into water recycle sub-networks... 

Figure 6.A.9 A sample network solution. Ten solutions that meet the maximum water recycle...

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