Sample Conditioning Systems Need Love Too

Process Users and Integrators Must Carefully Design and Match SCSs With Application Requirements and Specifications, Complete Application Data Sheets and Deploy Climate-Controlled Shelters

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Similarly, because users are experiencing alteration of sample composition due to ambient exposure of sample transfer lines, some process vendors are designing gas chromatographs for field installation near the user's field application sites for hydrocarbon processing industry (HPI) and chemical processing industry (CPI) applications.

Implementing climate-controlled shelters for SCSs and analyzers also means that care is needed when designing and installing these shelters. For example, it's important to use steel I-beams, rather than C-channel, for the base structure of analyzer shelters. Lift extensions must be added to base beams for lifting the completed shelter into its position, often within the boundaries of an operating process unit. It's also important to use a lift cable spreader, so lifting cables will not crush the shelter's roof edges. In one recent case, it did not become evident that the weight of a shelter was more than its base beams could handle until a crew started to lift it using base beam lift extensions at its four corners, and the shelter base beams buckled! These are design and infrastructure problems, but they also require significant and continual attention to detail.

APC and Process Physical Properties

Though low-PPM sample conditioning for quality is a more recent trend than repeatable accuracy for regulatory compliance, process analyzers provide signals for a growing advanced process control (APC) engineering initiative.

Real-time process control and optimization techniques often need measurements of process physical properties in order to be fully effective. Ideally, these measurements would be available on-process in real time, but generally they're only available infrequently and as a result of an off-line laboratory measurement, which is often subject to high levels of method variability and significant delays. To overcome these limitations of lab-based measurements, inferred measurements called inferential sensors (ISs) are used to provide real-time predictions of process physical properties based on mathematical models and standard on-line process measurements such as temperature, pressure and flow.

To be effective, ISs must be based on well-maintained process measurements that can be related to the physical properties in a manner that can be captured in a mathematical relationship. Often, this relationship is developed using correlation models based on numerous lab samples and statistical techniques. Generally, this relationship is only valid in a certain operating region, so the process must be controlled to stay within this region of validity, and/or the process must be carefully monitored to alert when the operating regime no longer fits the range of model validity. Furthermore, the correlation model is often subject to a slowly developing bias or offset that comes from unmeasured disturbances and/or modeling error. This bias must be guarded against and corrected for by periodic validation of the on-line mathematical model predictions versus off-line lab results.

Alternatively, physical properties can often be measured with on-line process analyzers that have the advantages of being more accurate than the ISs, as well as having faster and more frequent measurements compared to lab results, with a (potentially) higher degree of repeatability. However, process analyzers are usually expensive to purchase and install, and they require on-going maintenance to maintain calibration and resolve sample system issues that periodically arise.

The choice of which technique to use in any given application can be difficult to answer. Generally, one has to trade the economic value of the optimization and control effectiveness with the cost and effort of the measurement technique. Clearly, optimization and control can generally provide more value with more accurate, more frequent and more quickly available physical property measurements. Online process analyzers do require an upfront capital outlay and an on-going maintenance effort, but can often provide superior measurements when done properly. Lab-based measurements often do not require the capital outlay, but do carry an on-going lab cost associated with the labor rates of the technicians running the samples, and often have higher levels of sampling and technician/method variability compared to process analyzers. Inferential sensors can be a good solution with a low cost of entry if the correlation model's accuracy is sufficient, and the appropriate monitoring and validity checks are put into place.

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