Agency: Department of Energy | Branch: | Program: STTR | Phase: Phase I | Award Amount: 100.00K | Year: 2011
Frac fluids are viscous solutions that are pumped under high pressure to fracture subsurface formations for recovery of natural gas. As it is pumped to the surface, the flowback water picks up large quantities of various contaminants that make it difficult to dispose or recycle. Though this water contains hazardous substances such as heavy metals (i.e. copper, lead, zinc, and cadmium), arsenic, selenium, and numerous organic compounds, it is often improperly disposed to local streams without little or no treatment. Frac Biologics preliminary experiments using Biofilm Technology to treat frac flowback water have shown that removal of heavy metals is feasible. The objective of the Phase I research is to isolate natural halophilic biofilm organisms that will cost effectively remove heavy metals, arsenic, selenium, and organic compounds. During Phase II, biofilm columns will be built to process frac flowback water and prove the effectiveness of the technology. Commercial Applications and Other Benefits: Frac Biologics, Inc. was formed in 2009 to commercialize Biofilm Technology, specifically the remediation of contaminated water, such as acid mine drainage and frac flowback water. After biofilm columns are shown to effectively treat frac flowback water in Phase II research, the technology will be commercialized by scaling up to large-scale biofilm systems that can treat contaminated water on site
Gonzalez-Gonzalez D.,Frac Biologics, Inc. |
Cantu-Sifuentes M.,Frac Biologics, Inc. |
Praga-Alejo R.,Autonomous University of Coahuila |
Flores-Hermosillo B.,Autonomous University of Coahuila |
Zuniga-Salazar R.,Autonomous University of Coahuila
Engineering Applications of Artificial Intelligence | Year: 2014
It is expected that samples in reliability analysis contain both censored and complete failure data; thus, the maximum likelihood method is used to estimate the parameters of the related distribution. Nonetheless, samples may contain only censored data; therefore introducing a high degree of uncertainty which does result in non-viability for either the likelihood method or for statistical inference. This paper proposes the use of fuzzy probability theory to account for the uncertainty and the prior knowledge of the process in the parameters estimation, for censored data. The proposed method was applied to risk based inspection. Results demonstrate that our method represents a reliable option for using the expert knowledge about the component and the physics of the failure mode. Additionally, an inspection time was estimated based on target risk; the results confirm that the methodology could be used to develop maintenance plans. © 2014 Elsevier Ltd.
Praga-Alejo R.J.,Frac Biologics, Inc. |
Gonzalez-Gonzalez D.S.,Frac Biologics, Inc. |
Cantu-Sifuentes M.,Frac Biologics, Inc. |
Perez-Villanueva P.,Frac Biologics, Inc. |
And 2 more authors.
Engineering Applications of Artificial Intelligence | Year: 2013
A Hybrid Learning Process method was fitted into a RBF. The resulting redesigned RBF intends to show how to test if the statistical assumptions are fulfilled and to apply statistical inference to the redesigned RBFNN bearing in mind that it allows to determine the relationship between a response (to a process) and one or more independent variables, testing how much each factor contributes to the total variation of the response is also feasible. The results show that statistical methods such as inference, Residual Analysis, and statistical metrics are all good alternatives and excellent methods for validation of the effectiveness of the Neural Network models. The foremost conclusion is that the resulting redesigned Radial Basis Function improved the accuracy of the model after using a Hybrid Learning Process; moreover, the new model also validates the statistical assumptions for using statistical inference and statistical analysis, satisfying the assumptions required for ANOVA to determine the statistical significance and the relationship between variables. © 2013 Elsevier Ltd. All rights reserved.