A water tree testing apparatus using a flat test piece comprising a candidate insulation material an d having a first surface having a plurality of electrode holes formed therein has: a liquid-permeable conductive first permeable member that is attached to the first surface and covers the electrode holes; a liquid-permeable conductive second permeable member that is attached to a second surface that is opposite side from the first surface, and extends along the second surface as to face the first permeable member with the test piece therebetween; a first water tank for immersing the first surface in the first aqueous solution; a second water tank for immersing the second surface in the second aqueous solution; a first electrode, and a second electrode.

A water tree testing apparatus using a flat test piece comprising a candidate insulation material an d having a first surface having a plurality of electrode holes formed therein has: a liquid-permeable conductive first permeable member that is attached to the first surface and covers the electrode holes; a liquid-permeable conductive second permeable member that is attached to a second surface that is opposite side from the first surface, and extends along the second surface as to face the first permeable member with the test piece therebetween; a first water tank for immersing the first surface in the first aqueous solution; a second water tank for immersing the second surface in the second aqueous solution; a first electrode, and a second electrode.

The following provides a system and method to predict an indicator of tube fouling in a fired apparatus such as a boiler. Historical data can be collected when the tubing is still considered to be clean, and used to build a first model between an indicator of fouling, such as tube skin temperature, and boiler load. The actual measurement of that indicator of fouling can then be compared against the model output, such that the error between the model and measurement is considered an indication of the tube fouling. Moreover, the rate of change of the model error can be used to measure the fouling rate. Next, historical data on the fluid feed quality can be collected and together with the historical error rate change data can be combined to develop a second model. This second model reflects how fluid feed quality variables may affect the fouling rate over time.

A computer-based corrosion/erosion module, communicatively coupled with a probe, estimates corrosion/erosion rates in a pipeline based on metal loss measurements. A High Integrity Protection System (HIPS), upstream of the corrosion/erosion module, includes at least two pressure-sensing elements, connected to the pipeline, for capturing pressure readings associated with inside pressures of the pipeline. The HIPS also includes at least two final elements configured to stop a flow of fluid through the pipeline. A logic solver, coupled with the corrosion/erosion module and the HIPS, is configured to automatically monitor mechanical integrity of the pipeline in real time using the captured pressure readings and estimated metal loss measurements. The logic solver determines a trip set point adjustment using the estimated metal loss measurements and provides the trip set point adjustment to the final elements.

A computer-based corrosion/erosion module, communicatively coupled with a probe, estimates corrosion/erosion rates in a pipeline based on metal loss measurements. A High Integrity Protection System (HIPS), upstream of the corrosion/erosion module, includes at least two pressure-sensing elements, connected to the pipeline, for capturing pressure readings associated with inside pressures of the pipeline. The HIPS also includes at least two final elements configured to stop a flow of fluid through the pipeline. A logic solver, coupled with the corrosion/erosion module and the HIPS, is configured to automatically monitor mechanical integrity of the pipeline in real time using the captured pressure readings and estimated metal loss measurements. The logic solver determines a trip set point adjustment using the estimated metal loss measurements and provides the trip set point adjustment to the final elements.

Calibration of a valve in a vacuum system and providing a diagnostic indication in the vacuum system using the calibration includes measuring conductance of the valve as a function of angular valve position and generating a conductance calibration map or function for use during operation of the valve. An actual angular valve position is set based on the received set point angular valve position and a difference between the measured valve conductance and a predefined metric of conductance versus angular valve position. An actual system conductance and a difference between the actual system conductance and a reference system conductance for the system are determined. The diagnostic indication of a fault in the system is generated based on the actual angular valve position of the valve and the difference between the actual system conductance and the reference system conductance for the system.

Methods for quantitatively determining the time (TNI) between (1) the application of this method and (2) the time at which the next out-of-service API 653 internal inspection of a steel, field-erected, aboveground storage tank (AST) containing petroleum/water products should be performed. These methods combine four in-service measurements of the thickness, integrity, and corrosion rate of the tank bottom with an empirical corrosion rate cumulative frequency distribution (CFD) for the tank of interest to develop a Bayesian tank bottom survival probability distribution to determine TNI. During this entire TNI time period, the risk of tank bottom failure is less than at the time these methods were applied. If available, the results of a previous out-of-service API 653 internal inspection are also used. These methods can be applied at any time while the tank is in-service to update the internal inspection interval previously determined in an out-of-service internal inspection of the tank.

Methods for quantitatively determining the time (TNI) between (1) the application of this method and (2) the time at which the next out-of-service API 653 internal inspection of a steel, field-erected, aboveground storage tank (AST) containing petroleum/water products should be performed. These methods combine four in-service measurements of the thickness, integrity, and corrosion rate of the tank bottom with an empirical corrosion rate cumulative frequency distribution (CFD) for the tank of interest to develop a Bayesian tank bottom survival probability distribution to determine TNI. During this entire TNI time period, the risk of tank bottom failure is less than at the time these methods were applied. If available, the results of a previous out-of-service API 653 internal inspection are also used. These methods can be applied at any time while the tank is in-service to update the internal inspection interval previously determined in an out-of-service internal inspection of the tank.

Industrial fluids may be monitored at the site of each industrial fluid by introducing a sample of the industrial fluid into a device employing a detection technique for detecting at least one composition within the sample. The detection technique may be or include surface enhanced Raman scattering (SERS), mass spectrometry (MS), nuclear magnetic resonance (NMR), ultraviolet light (UV) spectroscopy, UV spectrophotometry, indirect UV spectroscopy, contactless conductivity, laser induced fluorescence, and combinations thereof. In one non-limiting embodiment, a separation technique may be applied to the sample prior to the introduction of the sample into the device for detecting the composition.

Industrial fluids may be monitored at the site of each industrial fluid by introducing a sample of the industrial fluid into a device employing a detection technique for detecting at least one composition within the sample. The detection technique may be or include surface enhanced Raman scattering (SERS), mass spectrometry (MS), nuclear magnetic resonance (NMR), ultraviolet light (UV) spectroscopy, UV spectrophotometry, indirect UV spectroscopy, contactless conductivity, laser induced fluorescence, and combinations thereof. In one non-limiting embodiment, a separation technique may be applied to the sample prior to the introduction of the sample into the device for detecting the composition.