An Entropy Based Bayesian Network Framework for System Health Monitoring.

Oil pipeline network system health monitoring is important primarily due to the high cost of failure consequences. Optimal sensor selection helps provide more effective system health information from the perspective of economic and technical constraints. Optimization models confront different issues. For instance, many oil pipeline system performance models are inherently nonlinear, requiring nonlinear modelling. Optimization also confronts modeling uncertainties. Oil pipeline systems are among the most complicated and uncertain dynamic systems, as they include human elements, complex failure mechanisms, control systems, and most importantly component interactions. In this paper, an entropy-based Bayesian network optimization methodology for sensor selection and placement under uncertainty is developed. Entropy is a commonly used measure of information often been used to characterize uncertainty, particularly to quantify the effectiveness of measured signals of sensors in system health monitoring contexts. The entropy based Bayesian network optimization outlined herein also incorporates the effect that sensor reliability has on system information entropy content, which can also be related to the sensor cost. This approach is developed further by incorporating system information entropy and sensor costs in order to evaluate the performance of sensor combinations. The paper illustrates the approach using a simple oil pipeline network example. The so-called particle swarm optimization algorithm is used to solve the multi-objective optimization model, establishing the Pareto frontier.

Remediation of groundwater contaminated with arsenic through enhanced natural attenuation: Batch and column studies.

Batch and column laboratory experiments were conducted on natural sediment and groundwater samples from a contaminated site in Maine, USA with the aim of lowering the dissolved arsenate [As(V)] concentrations through chemical enhancement of natural attenuation capacity. In batch factorial experiments, two levels of treatment for three parameters (pH, Ca, and Fe) were studied at different levels of phosphate to evaluate their impact on As(V) solubility. Results illustrated that lowering pH, adding Ca, and adding Fe significantly increased the sorption capacity of sediments. Overall, Fe amendment had the highest individual impact on As(V) levels. To provide further evidence for the positive impact of Ca on As(V) adsorption, isotherm experiments were conducted at three different levels of Ca concentrations. A consistent increase in adsorption capacity (26-37%) of sediments was observed with the addition of Ca. The observed favorable effect of Ca on As(V) adsorption is likely caused by an increase in the surface positive charges due to surface accumulation of Ca2+ ions. Column experiments were conducted by flowing contaminated groundwater with elevated pH, As(V), and phosphate through both uncontaminated and contaminated sediments. Potential in-situ remediation scenarios were simulated by adding a chemical amendment feed to the columns injecting Fe(II) or Ca as well as simultaneous pH adjustment. Results showed a temporary and limited decrease in As(V) concentrations under the Ca treatment (39-41%) and higher levels of attenuation in Fe(II) treated columns (50-91%) but only after a certain number of pore volumes (18-20). This study illustrates the importance of considering geochemical parameters including pH, redox potential, presence of competing ions, and sediment chemical and physical characteristics when considering enhancing the natural attenuation capacity of sediments to mitigate As contamination in natural systems.