Risk-Based Fault Detection Using Bayesian Networks Based on Failure Mode and Effect Analysis.

In this article, the authors focus on the introduction of a hybrid method for risk-based fault detection (FD) using dynamic principal component analysis (DPCA) and failure method and effect analysis (FMEA) based Bayesian networks (BNs). The FD problem has garnered great interest in industrial application, yet methods for integrating process risk into the detection procedure are still scarce. It is, however, critical to assess the risk each possible process fault holds to differentiate between non-safety-critical and safety-critical abnormalities and thus minimize alarm rates. The proposed method utilizes a BN established through FMEA analysis of the supervised process and the results of dynamical principal component analysis to estimate a modified risk priority number (RPN) of different process states. The RPN is used parallel to the FD procedure, incorporating the results of both to differentiate between process abnormalities and highlight critical issues. The method is showcased using an industrial benchmark problem as well as the model of a reactor utilized in the emerging liquid organic hydrogen carrier (LOHC) technology.

Two variable semi-empirical and artificial neural-network-based modeling of peptide mobilities in CZE: the effect of temperature and organic modifier concentration.

This work was focused on investigating the effects of two separation influencing parameters in CZE, namely temperature and organic additive concentration upon the electrophoretic migration properties of model tripeptides. Two variable semi-empirical (TVSE) models and back-propagation artificial neural networks (ANN) were applied to predict the electrophoretic mobilities of the tripeptides with non-polar, polar, positively charged, negatively charged and aromatic R group characteristics. Previously published work on the subject did not account for the effect of temperature and buffer organic modifier concentration on peptide mobility, in spite of the fact that both were considered to be influential factors in peptide analysis. In this work, a substantial data set was generated consisting of actual electrophoretic mobilities of the model tripeptides in 30 mM phosphate buffer at pH 7.5, at 20, 25, 30, 35 and 40 degrees C and at four different organic additive containing running buffers (0, 5, 10 and 15% MeOH) applying two electric field strengths (12 and 16 kV) to assess our mobility predicting models. Based on the Arrhenius plots of natural logarithm of mobility versus reciprocal absolute temperature of the various experimental setups, the corresponding activation energy values were derived and evaluated. Calculated mobilities by TVSE and back-propagation ANN models were compared with each other and to the experimental data, respectively. Neural network approaches were able to model the complex impact of both temperature and organic additive concentrations and resulted in considerably higher predictive power over the TVSE models, justifying that the effect of these two factors should not be neglected.

Microfabricated devices in biotechnology and biochemical processing.

In the past few years, interdisciplinary science and technologies have converged to create exciting challenges and opportunities, which involve a new generation of integrated microfabricated devices. These new devices are referred to as 'lab-on-a-chip' or Micro Total Analysis Systems. Their development involves both established and evolving technologies, which include microlithography, micromachining, Micro Electro Mechanical Systems technology, microfluidics and nanotechnology. This review summarizes the key device subject areas and the basic interdisciplinary technologies, and gives a better understanding of how these technologies can be used to provide appropriate technical solutions to fundamental problems. Important applications for this novel 'synergized' technology in chemical and biotechnological processing, in addition to the application of simulation methods in the development of microfabricated devices, will also be discussed.

Micropreparative fraction collection in microfluidic devices.

Micropreparative fraction collection following microchip-based electrophoretic analysis of biomolecules is of major importance for a variety of biomedical applications. In this paper, we present a microfabricated device-based fraction collection system. Various size DNA fragments were separated and collected by simply redirecting the desired portions of the detected sample zones to corresponding collection wells using appropriate voltage manipulations. The efficiency of sampling and collection of the fractions was enhanced by placing a cross channel at or downstream of the detection point. Following the detection of the band of interest, the potentials were reconfigured to sampling/collection mode, so that the selected sample zone migrated to the appropriate collection well of the microdevice. The potential distribution assured that the rest of the analyte components in the separation column was retarded, stopped, or reversed, increasing in this way the spacing between the sample zone being collected and the immediately following one. By this means, a precise collection of spatially close consecutive bands could be facilitated. Once the target sample fraction reached the corresponding collection well, the potentials were switched back to separation mode. Alternation of the separation/detection and sampling/collection cycles was repeated until all required sample zones were physically isolated. The integrated device consists of a sample introduction, separation, fraction sampling, and fraction collection compartments. The feasibility of the fraction collection technique was tested on a mixture of dsDNA fragments. The amounts of DNA collected in this way were enough for further downstream sample processing, such as conventional PCR-based analysis.