Stochastic multi-objective performance optimization of an in-stream woodchip denitrifying bioreactor

Woodchip denitrifying bioreactors (WDBs) that target filtration of nitrate from farm drainage water are gaining recognition as a tool for tackling the issue of diffuse nitrate pollution from agricultural landscapes. Whilst the hydraulic regime and concentration of nitrate in the drainage water constitute two fundamental environmental variables that determine the size of a denitrifying bioreactor, the issue of over- or under-treatment of water that might otherwise promote undesirable pollution swapping phenomena and construction costs also need to be factored into the overall design process. Conventional methods for optimizing the design of denitrifying bioreactors generally rely on deterministic models, even though many of the design parameters are not known with confidence. In this work we apply an alternative design philosophy and demonstrate how the bioreactor design process can be improved through application of stochastic methods. The design aspect of an 'in-stream' WDB planned for installation on a farm in New Zealand is structured as a multi-objective performance optimization problem that is solved in a stochastic framework, using freely available open source tools. Uncertainty considerations regarding values of physical parameters that govern bioreactor performance are incorporated into the assessment, from which a Pareto set of optimal designs was obtained. A 75 m long bioreactor of 1.5 m height was selected as the preferred choice from the optimal set of design solutions. Assuming a 10-year operational life, it is predicted the cost of nitrate removal by the planned denitrifying bioreactor will be NZ$9.70/kg-N (approx.US$6.60/kg-N).