Optimizing municipal wastewater treatment plants using an improved multi-objective optimization method

Multi‐objective optimisation for design and operation of anaerobic digestion using GA‐ANN and NSGA‐II

“…Recently, multi‐objective evolutionary algorithms have received increasing attention due to their population‐based structures that can easily obtain multiple Pareto‐optimal solutions in a single run . 31 NSGA‐II is a fast and elitist multi‐objective genetic algorithm, which was proposed by Dev et al The cross‐over and mutation operators remain as usual, but selection operator works differently from simple GA.…”

“…The multi-objective optimisation procedure using non-dominated sorting genetic algorithms-II Recently, multi-objective evolutionary algorithms have received increasing attention due to their population-based structures that can easily obtain multiple Pareto-optimal solutions in a single run. 6,13,31 NSGA-II is a fast and elitist multi-objective genetic algorithm, which was proposed by Dev et al 25 The cross-over and mutation operators remain as usual, but selection operator works differently from simple GA. Selection is done with the help of a crowded-comparison operator, based on ranking (according to non-domination level), and crowding distance.…”

“…Therefore, how to balance the effluent quality and biogas production becomes a critical issue for operation of an anaerobic wastewater treatment process, which necessitates a multi‐objective optimisation. The multi‐objective optimisation problem is a common phenomenon encountered in many practical processes . The fundamental theory of multi‐objective optimisation has been well established; however, there are still many challenges for the practical implementation .…”

“…The multi-objective optimisation problem is a common phenomenon encountered in many practical processes. 13 The fundamental theory of multi-objective optimisation has been well established; however, there are still many challenges for the practical implementation. 14,15 Up to now, most of the optimisation research has been on the basic of process simulation 16 -18 and single objective optimisation, 6,19,20 which has many limitations.…”

Optimal strategies evaluated by multi-objective optimization method for improving the performance of a novel cycle operating activated sludge process

“…Fortunately, multi-objective optimization method can overcome these drawbacks: (1) it can simultaneously optimize different objectives without weighting factors; (2) a set of equally good solutions named Pareto solutions can be obtained, where the trade-offs between different objectives can be expressed and thus decision maker (DM) can make the final decision based on his/her preference after evaluating the trade-offs. Until now several published literatures have paid attention to applying multiobjective optimization method in ASP, however, they mainly emphasized to optimal design ASP [21], to find the optimal setpoints of manipulated variables [22,23], to evaluate the influence of input uncertainty in decision making process [24] or to find the Pareto solutions among a few sets of operating parameters [25].…”