Nuclear power plant fault-diagnosis using artificial neural networks

Artificial neural networks (ANNs) have been applied to various fields due to their fault and noise tolerance and generalization characteristics. As an application to nuclear engineering, we apply neural networks to the early recognition of nuclear power plant operational transients. If a transient or accident occurs, the network will advise the plant operators in a timely manner. More importantly, we investigate the ability of the network to provide a measure of the confidence level in its diagnosis. In this research an ANN is trained to diagnose the status of the San Onofre Nuclear Generation Station using data obtained from the plant`s training simulator. Stacked generalization is then applied to predict the error in the ANN diagnosis. The data used consisted of 10 scenarios that include typical design basis accidents as well as less severe transients. The results show that the trained network is capable of diagnosing all 10 instabilities as well as providing a measure of the level of confidence in its diagnoses

Using Butanol Fermentation Wastewater for Biobutanol Production after Removal of Inhibitory Compounds by Micro/Mesoporous Hyper-Cross-Linked Polymeric Adsorbent

In the present study, a novel micro/mesoporous hyper-cross-linked polymeric adsorbent, SY-01, was tested to remove several inhibitory compounds from butanol fermentation wastewater (BFW) for biobutanol production for the first time. Characterization of the SY-01 resin was determined by scanning electron microscopy, nitrogen adsorption desorption isotherms, Fourier transform infrared spectroscopy and elemental analysis. The results showed that the SY-01 resin possessed a high Brunauer-Emmett-Teller surface area (1334 m(2)/g) with large rnicropores and mesopores volumes (0.42 and 0.69 mL/g, respectively). After fixed-bed column adsorption, more than 96.0% of D-xylose and 95.0% of o-glucose remained in the treated butanol fermentation wastewater (TBFW). Acetic acid removal varied from 5.1% to 18.7%, butyric acid removal varied from 64.9% to 100% and color removal was effective between 52.9% and 99.2%. In the column desorption process, 99.4% of acetic acid and 99.1% of butyric acid were recovered by an acetone solution. Furthermore, the TBFW was used as substrate for biobutanol production by Clostridium acetobutylicum CH06. The detoxification by the SY-01 resin column increased the maximum acetone butanol ethanol concentration by 4.08 times and enhanced the total sugar utilization by 1.95 times. In conclusion, our results suggest a new approach for treating the butanol fermentation wastewater