Multi-objective optimization of operational variables in a waste incineration plant

One of the primary objectives of the operation of a waste incineration plant is to maximise throughput. However, increasing throughput can intensify the loading on the gas-clean-up system and also cause a violation of operational constraints. This may result in penalty costs due to excessive pollution emissions and the need for increased maintenance. Therefore, a multi-objective strategy is required to optimize plant operation in terms of economic and environmental goals, and operational constraints. This paper develops a supervisory level optimization tool for a waste incineration plant, which utilises a multi-objective genetic algorithm (MOGA). A MOGA is ideally suited to providing decision support to a human operator because it returns a Pareto-optimal set of solutions; this allows the user to transparently assess the benefits and penalties associated with a range of candidate control decisions. Specifically, the tool enables controllable parameters to be adjusted for maximum throughput, whilst minmising emissions and keeping within operational constraints. The optimization procedure is independent of plant construction and waste stream input, and is demonstrated here on the model of a municipal solid waste incineration plant.