Use of neural network models to predict industrial bioreactor effluent quality

Engineered bioreactors are useful tools for degrading wastes from crude oil refining facilities. One such bioreactor forms part of the wastewater remediation process used at a refinery in the San Francisco Bay Area. The flow rate and chemical concentrations of the waste vary, and it is necessary to be able to predict the efficiency of the reactor degradation process for this varied input. The complex biological, physical, and chemical processes of the reactor make deterministic modeling unsuitable. Therefore, predictive modeling for this system was performed using a neural network model. A predictive, time-series neural network model requires a complete data set. Often, in the case of a large industrial facility, data are missing. Various techniques can be used to reconstruct missing data, but comparisons of techniques have not been performed for large-scale remediation processes. In this manuscript, four techniques are used for reconstructing missing data to examine which ones provide superior predictive capabilities. It was found that the interpolated and moving average values methods provided the best predictions. The mean and median replacement methods, commonly used in neural network modeling, provided much poorer predictions. Another goal of this study is to determine which water quality parameters are more accurately predicted than others. In this study, pH was the most accurately predicted, while ammonia and total phenolics concentrations were the least accurately predicted.