(372e) Involving the Water-Energy-Waste Nexus in the Optimal Design of Total Integrated Residential Complexes

This work presents a multiobjective optimization model for the design of an integrated residential complex. The addressed problem consists in determining the optimal configuration of an integrated residential complex, which seeks to satisfy the water, electricity, heating, and cooling needs as well as to treat the wastewater, emissions, and wastes accounting for the minimization of the associated cost, emissions, and the acceptability of the inhabitants. The economic objective functions include the minimization of the total annual cost and the return of investment, which include operating and capital costs for each of the required processing units, as well as the purchase and sale of resources to external costumers. The environmental objective functions involve the reduction of fresh water consumption, the minimization of greenhouse gas emissions and the quantification of the environmental impact caused by the use of fresh resources and by the management and installation of the selected units. In the same way, a social objective function, the process route health index to evaluate the damage to inhabitants’ health associated with the produced emissions for process units is considered to prove the acceptability of the proposed integrated system.

To solve this problem, a superstructure is proposed where all the possible configurations was included, as well as its possible solutions. Due to the number of objectives, it is not possible to find a solution to the problem through a Pareto curve and there may be a lot of dissatisfaction with one of the objectives. Therefore, for the solution of the problem, a different multistakeholder tool is used to find a balance between the objective functions and give a reasonable solution to the problem, is considered a residential complex located in the central-west region of Mexico, which consists of 1 440 households.

The proposed multiobjective optimization model was coded and solved in the software GAMS. The model consists of 1 098 continuous variables, 10 binary variables, and 3 570 constraints. The model includes nonconvex terms, so to solve the model in GAMS we need a global optimization solver; in this case, we selected Baron because a local solver did not provide a good solution. The weights for the objective functions for each of the scenarios of the case study for the multistakeholder approach were generated randomly in the software MatLab through the Latin hypercube function. Assigning random weights to the objective functions is only a representative option to show the effect of prioritizing one objective and to show the trade-offs of the considered objectives.

The dissatisfaction of each of the objective functions and the total dissatisfaction for all the scenarios were calculated. The scenario with the lowest total dissatisfaction represents the most attractive solution for this problem, where the individual dissatisfaction for the objectives are 14% for the total annual cost, 9% for the fresh water consume, 7% for the CO2 emissions, the return of investment is near for the upper bound with 99% of dissatisfaction, while the environmental impact and the process route health index are in their best solutions with 0% of dissatisfaction.