Optimal design of spatially constrained interplant water networks with direct recycling techniques using genetic algorithms

In this work, an industrial city spatial representation that accounts for different plant layouts and arrangements is utilized for interplant water network synthesis. The problem has been previously tackled using deterministic optimization methods. This work employs a stochastic optimization approach, using genetic algorithms, for the design of spatially constrained interplant water networks using direct recycling techniques. The approach identifies well-performing solutions in an evolutionary manner, by generating populations of candidate solutions, then sampling regions that are associated with the highest performance probabilities. This ensures that only the fittest designs survive, when evaluating the network performance. A fitness objective that accounts for both freshwater and piping costs was utilized in the design evaluation stage. When compared to the results that have been obtained using deterministic optimization, trade-off trends between the optimum cost of the network and fresh/waste targets were manifested by means of stochastic optimization. Enhanced network performance was attained for a reduced total cost, at the expense of a certain deviation from fresh/waste targets.