When we did process control improvements in the 1980s and 1990s, the major limitation was the lack of a reliable field analyzer. None of the plants had field analyzers on raw materials. The specialty chemicals production units had very few field analyzers and were flying blind. Plants for chemical intermediate products had field analyzers on most key product streams that were supported by an excellent, extensive plant analyzer group. Unfortunately, the technology was old, dating back to the 1970s. Many of the analyzers, analog circuits, and sampling systems were literally homemade and were dependent upon the expertise of the analyzer group. Most of these analyzer specialists have retired. Fortunately, the technology developed was adopted by new analyzer companies. Subsequently, the technology has steadily advanced and the electronics have become small computers providing diagnostics, intelligent interfaces, and standardized communication. Sample valves and sampling system design have also improved. Analyzer systems are more reliable today but still require maintenance and special expertise. Unfortunately, onsite analyzer specialists are becoming extinct.
Quite a bit of effort was devoted in the 1990s to developing artificial neural networks (ANN) to predict compositions in streams instead of installing analyzers. Unfortunately, the technology was oversold by big neural network suppliers, who claimed, “No hardware, no engineering, and no maintenance. Just dump all of your plant data into the ANN.” PID setpoints, process variables, and outputs were inputs, ignoring the fact that the PID algorithm was in play. The necessary Design of Experiments (DOE) was not done. Steady-state data without changes to process inputs led to relationships that violated first principles and wild extrapolations when the plant deviated from the test conditions. Principal components and drill-down into contributions were not available. Process engineers did not review the relationship of each input to the predicted output. Automated feedback correction from lab analysis was often not used. As the result of all this, ANN’s achievements were temporary at best. Today, ANN integrated into a DCS are easier to use and may offer benefits for focused applications.
There are some examples of a brighter future for online and at-line analysis. Coriolis meters offer an incredibly accurate, reliable, and nearly maintenance-free density measurement for online analysis where two components have significantly different densities. The capability has been extended with more sophisticated digital computations to include percent solids and bubbles. Conductivity and pH offer online analysis for high and low concentrations, respectively, of acids and bases. The Nova BioProfile Flex at-line analyzer with an automated sampling system is becoming the standard for bioreactors in the biopharmaceutical industry. Within minutes, the Nova analyzer can provide cell size, count, and health along with the concentration of nutrients and inhibiting byproducts from 1 ml samples.
Concept: Process efficiency and capacity often depend upon the composition of input and output process streams. Automation systems commonly measure temperature, pressure, level, pH, and flow but rarely composition. Sometimes pH, pressure, and temperature can be an indicator of composition in reactions and separations for a given set of input stream compositions. At present, lab measurements are relied upon for confirmation of the estimated values. Large continuous plants tend to have analyzers on product streams. What is primarily missing despite advances in technology are on-line and at-line analyzers for raw materials and batch operations.
Details: A history of lab results can show the variability of key components in key process streams. Simulations can show the effects of changes in composition on the process. Field analyzers on streams where the key components show variability or need to be optimized can be used for a higher level of control. The higher level can be as simple as cascade control or as sophisticated as model predictive control. Coriolis meters should be installed on all reactant and product streams (Tip #73). Online analyzers require less maintenance than at-line analyzers because there is no sampling system but they often provide an inferential measurement that does not reflect the effects of changes in process conditions. The most effective analyzer for the process industry is the gas chromatograph, as discussed in the December 2011 Control Talk column “Analyze This!” and the January 2012 Control Talk column “Gas Chromatographs Rule.”
Watch-outs: Droplets or solids remaining in process samples after sample conditioning will adversely affect the results from a gas chromatograph (GC). The sample used in a GC must be completely vaporized. The sample point for any at-line analyzer may not be representative of the process composition because of the separation of phases and
non-ideal mixing at the point of sample extraction. Near Infrared (NIR) analyzers are only as good as the set of samples used to develop the Projection to Latent Structure (PLS) statistical models. NIR models (NIR calibration) must be updated as raw materials and operating conditions change. Special mathematical expertise is needed for understanding and improving NIR models. The maintenance costs of analyzers (except for Coriolis meters) usually exceed the hardware cost. Analyzers should not be used in closed loop control until they have proven to be sufficiently accurate and reliable in actual plant operation. At-line analyzer sample time, cycle time, and multiplex time will increase the total loop deadtime, destabilizing the loop, unless the controller is retuned or an enhanced PID developed for wireless is used. Online first principle models and experimental models (e.g., linear dynamic estimators) periodically corrected by at-line analyzers can provide an immediate predicted composition, eliminating the additional deadtime.
Exceptions: Analyzers should not be installed if there is no onsite support with the required expertise or if the Return on Investment (ROI) is insufficient based on actual analyzer downtime and life cycle cost, which includes maintenance cost.
Insight: Field analyzers enable a higher level of control, such as model predictive control, to improve product quality and process efficiency and capacity.
Rule of Thumb: Install a field analyzer on key continuous streams and batch unit operations if there is an adequate ROI and onsite technical support.