Aeration optimization of a wastewater treatment plant using genetic algorithm

This paper discusses the aeration optimization problem of an intermittently aerated wastewater treatment plant by the application of a stochastic optimization approach, genetic algorithm (GA). In this process, the alternating aerobic and anoxic conditions needed for nitrogen removal are realized in a single basin by switching the aeration sequentially on and off. Since the operation of these plants may be challenging both for economical and technical reasons, several previous studies have investigated the possibility of reduction of the operating cost, however, it turned out that for long-term application these methods can save only limited per cent of the cost. Furthermore, these investigations also had to make problem simplifications in order to use optimization methods which usually need significant computational effort to give – only a local optimum – of the problem. The objective of this paper is to demonstrate an optimization procedure to minimize the pollution load in the receiving water body, rather than the operational cost, using a complete model of the treatment process. The results were evaluated based on rigorous evaluation criteria and showed that using GA-based optimization strategy an optimal solution can be efficiently found where both pollution load and energy consumption savings can reach up to 10% compared to traditional control strategies.