The burgeoning biofuels sector (see Biofeedstocks see real growth) also stands to gain immediate benefits from being able to monitor product quality online. Manufacturers and buyers alike require the quality of the biofuels to meet recommended American Society for Testing and Materials (ASTM) specifications. With ASTM-certified analysis costing up to $1,200 per sample, plants want to know their output satisfies specifications before sending product for certification. To meet this new demand, Aspectrics, Pleasanton, Calif., has extended the capabilities of its MultiComponent 2750 EP-NIR analyzer to provide pass/fail information on multiple biofuel contaminants before samples are sent out for ASTM certification.
Based on Aspectrics' patented encoded photometric NIR spectroscopy technology, which mimics FTIR (Fourier Transform infrared) spectroscopy but in a more robust online form, the 2750 online biofuels analyzer can monitor methanol, water and total glycerin in biodiesel, water content in bioethanol, biodiesel blends and ethanol/gasoline blends. A single analyzer can process multiple samples at 100 scans/second, generating real-time results in a few seconds.
Besides the obvious necessity for rugged designs, taking analytical procedures out of the lab and into the plant often requires getting the equipment certified for use in the relevant process environment. A recent example was the September launch of the X-STREAM flameproof process gas analyzer by the Solon, Ohio-based Rosemount Analytical unit of Emerson Process Management (Figure 2).
Certified for use in Class 1, Zone 1, Group IIB + H2 hazardous areas, this unit is the latest addition to the X-STREAM series introduced in 2006, which offers single- and dual-channel analysis based on NDIR/UV/VIS (non-dispersive infrared, ultraviolet and visible) photometry, paramagnetic and electrochemical oxygen, and thermal conductivity sensor technologies.
Not a pipe dream
There's progress not only in analysis of streams but also in online monitoring of the condition of the piping through which they flow. In September, Emerson announced that it has teamed up with Rohrback Cosasco Systems, Santa Fe Springs, Calif., to introduce the MCS Microcor wireless transmitter for high-speed communication of corrosion rate data from the plant to its automation system. Earlier, in June, Honeywell Process Solutions, Phoenix, Ariz., launched its OneWireless mesh network solution, a range of products that includes a wireless corrosion monitor (Figure 3).
"This has turned our previous loop-powered digital HART corrosion transmitter [the SmartCET instrument] (see Innovative corrosion monitoring solutions enhance process optimization) into a battery-powered unit that now has a measurement cycle time of 30 seconds at its fastest speed," notes Dawn Eden, Houston-based marketing manager - corrosion.
Such response speed puts corrosion monitoring into the same league as the traditional process variables of temperature, pressure and flow — but begs the question "Is a near-real-time response really necessary for such a relatively long-term phenomenon as corrosion?"
The answer is an unequivocal "yes," says Eden, citing a couple of practical examples: "acid runaway situations where getting the concentration wrong can cause the diluted acid to tear through quite substantial thicknesses of piping in a matter of hours.... Similarly, if you're dealing with a process where product quality has to be absolutely right and you can't afford any contamination [from corrosion], then you want to have a very early warning of when something is going wrong."
Beyond the black box
However, many other types of online analysis remain either too expensive or simply impractical to implement. To handle such chores, the arrival around 15 years ago of a neural-network-based approach of soft sensors and inferential measurements seemed too good to be true. And, some might argue, so it has turned out. Others, though, see a widening role for inferential sensors as people gain more confidence in their capabilities.
"The key issue," says Arthur Kordon, data mining and modeling leader with Dow Chemical in Freeport, Texas, "is that data-driven solutions don't have the in-built credibility that you have with hardware solutions or models based on first principles. Another issue has proved to be the growing maintenance costs of inferential models based on a limited set of data, which means that you almost always have to extrapolate at some point — and unfortunately a neural network is not a technology that shines in extrapolation."
Those early "black box" types didn't just infer measurements, too often their sales pitches also inferred that all a plant had to do was supply sufficient empirical and historical process data for a model to be built that could predict product properties and provide input for closed loop control of the process.
Mike Brown, vice president solutions for Matrikon, Edmonton, Alberta, sums up those early days: "The problem was that people were pushing the technology so hard — exploiting the power of the CPU and all that availability of data — but doing it in a way that was ignoring basic engineering fundamentals. The whole concept of data mining was very powerful but the promise of the technology wasn't really delivering what clients were expecting. If you changed your feedstock, often your predictions were off but it took a while to realize this and retune the model. It was low cost to buy the technology — compared with buying the analytical hardware — but the support cost didn't go down significantly."