A dynamic model of a fill-and-draw reactor and its implications for hazardous waste treatment

A dynamic model of a fill-and-draw reactor has been developed, which includes a number of operational parameters such as the draw-down volume, the fraction of total cycle time devoted to fill, reaction, and draw, and the concentration of the toxic substance in the feed to the reactor. The model has been solved numerically for two cases, one assuming Monod kinetics, and one considering substrate inhibition (Andrews kinetics). The conversions achieved with this type of reactor have been compared to that of a conventional activated sludge (CSTR) design. These results indicate that for most practical settings of the operating parameters, the volume of a fill-and-draw reactor needed to achieve the same conversion can be many times smaller than that of a CSTR at the same throughput. For example, with Monod kinetics, in order to achieve 99% conversion of phenol at a feed concentration of 40 ppm, a CSTR requires 9 times the volume of a fill-and-draw reactor for the same throughput. An experimental 5-liter fill-and-draw reactor has been operated with phenol and a pure culture of Pseudomonas putida. Results compared very well to the model predictions

A Process Capability Analysis Method Using Adjusted Modified Sample Entropy

Citation: Koppel, S., & Chang, S. I. (2016). A Process Capability Analysis Method Using Adjusted Modified Sample Entropy. Procedia Manufacturing, 5, 122-131. doi:10.1016/j.promfg.2016.08.012The evolution of sensors and data storage possibilities has created possibilities for more precise data collection in processes. However, process capability analysis has become more difficult. Traditional methods, such as process capability ratios, cannot handle large volumes of process data over time because these methods assume normal process distribution that is not changing. Entropy methods have been proposed for process capability studies because entropy is not dependent on distribution and can therefore provide accurate readings in changing distribution environments. The goal of this paper is to explore the use of entropy-based methods, specifically modified Sample Entropy to identify process variations over time. A study based on simulated data sets showed that the proposed method provides process capability information. © 2016 The Author