|Figure 2. Soft sensor supplements twice daily laboratory analysis of moisture in product coming out of calciners at Australian plant.
Source: Worsley Alumina
SABIC-Europe, at polyethylene plants in The Netherlands (Figure 3) and Germany, relies on Aspen Tech soft sensors for measurements usually not made by online sensors. SABIC monitors density of materials in the reactors and granulator outflow, among other variables. “Density measurement [on-line] is not common in our type of industry, so you have got to get samples and send them to a laboratory. You suffer from time delay as well as low-frequency information. So, a software sensor is here a great solution to better control,” Versteeg notes.
|Figure 3. This polyethylene plant in The Netherlands avoids delays in measurement of density incurred by conventional sampling.|
Using a soft sensor typically reduces laboratory analyses. Politeno needed five fewer analysts once it installed the sensors, Gailleaux says. SABIC-Europe saw the same trend, notes Versteeg. Oktay Karagoz, a process control engineer at SABIC-Polyolefine GmbH, in Gelsenkirchen, Germany, explains that soft sensors reduced the number of laboratory samples he needs by 75%.
In addition, the closeness of soft sensors’ predictions to results from laboratory analyses is boosting end-users’ confidence. Often times, the analytical value is not as good as the inferential value, Karagoz says. Indeed, laboratory analyses are more prone to bias, he adds. At SABIC-Polyolefine, he reckons that 95% of this is from the laboratory itself — e.g., from how the analyses are conducted, how samples are transferred, or from residue in sample ports in the process, when samples are taken shortly after the transition from one grade of product to another. The remaining 5% comes from the gas chromatograph.
Closing the loop
SABIC-Europe’s and Politeno’s use of soft sensors as part of MPCs exemplifies the most significant trend in their use: as the foundation of closed-loop control, either through MPCs (Figure 4) or multivariable controllers (MVCs).
Versteeg says that during transitions from one grade of product to another, when no laboratory data are available, SABIC relies on the MPC. Karagoz adds, “We reduced off-spec during transition… And during steady-state — only one product being produced — we don’t have any off-spec.”
Worsley Alumina pared off-spec product by teaming soft sensors and MVC, says D’Agostino. Worsley used the sensors to fill in the gaps between LOI measurements that were produced only twice daily by their laboratory. “If we can replace some of the analyses with the soft sensor, we can improve the control of the plant,” he says.
Sasol Germany GmbH, Brunsebuettal, also improved control (Figure 5). In 2001, it installed Pavilion soft sensors as an open-loop advisor in its fatty alcohols manufacturing line, to predict a property of an intermediate product stream from the reactors in the Zeigler process, explains Helge Timm, the engineer responsible for the facility’s advanced process control. Then, in 2002, Sasol installed eight more soft sensors in an MPC in the Ziegler process’ main alcohol distillation system. The controller predicts component concentrations in column product streams, Timm explains. The columns’ on-line gas chromatographs give values every 15 to 20 minutes, which he calls too infrequent.
|Figure 5. German facility employs soft sensors for closed-loop model-predictive control of its main alcohol distillation system.
Source: Sasol Germany
Use of the sensors and MPC has led to fewer lab analyses, less energy consumption in the distillation columns and better product quality, says Timm. “Soft sensors give the best control,” he believes.
Clearly, soft sensors are destined for a wider role in conventional closed-loop control. In addition, the inferential calculators are making a mark in emerging control approaches.
For instance, Cybosoft-General Cybernation Group Inc., Rancho Cordova, Calif., has developed soft sensors that are used in its model-free adaptive (MFA) control systems to predict the heating value of mixed gas and steam dryness of boilers, says Dr. George Cheng, the company’s chief technology officer. “Since MFA only needs the setpoint and process variable as the input information to compute its control output, it does not require some of the special information that a model-based approach may require.” So, MFA may need just one soft sensor for the process variable, he explains, while model-based approaches may need extra soft sensors to get the special information.
Use of soft-sensor-based MPCs in more-challenging environments is growing through a nonlinear approximating technology invented by Aspen Tech’s Turner in 2000. The problem encountered in nonlinear models, particularly black-box ones such as neural networks, is that the models may appear to be correct in certain regions but not at other operating points, he says. “Historically, nonlinear models that were accurate were ‘black boxes.’ More powerful nonlinear approximators, such as neural nets, have to be used in a pure advisory mode — outside of the controller — so that any potential unsafe conditions are not seen by the controller.”
Turner stresses that his technology is intrinsically safe and estimates it is in 300 to 500 models running globally, particularly in the bulk polymer industry. Conventional practice, he adds, is to use intrinsically safe models for control and that has meant relying on simple linear models or ones with transforms such as logarithms or quadratics. His model always matches the true direction of the process — e.g., it shows positive temperature gain if that is actually occurring — to prevent potentially unsafe control decisions.
Canada’s McMaster University, Hamilton, Ont., through its Academic Consortium, is working with Honeywell, says Morrison, to develop soft sensors that can handle multivariate analyses to detect general process abnormalities — for example, distillation column flooding or temperature excursions and even erroneous thermocouple readings in reboilers. “Multivariate uses principal-component analysis and could look at the outputs of hundreds of hard sensors and even soft sensors,” he says. What differentiates multivariate soft sensors from univariate ones is that the former looks at process quality, while the latter looks at product quality.
Users are specifying soft sensors more and more for control applications from the outset, Snyder says. “If it’s important enough for them to predict [a value with soft sensors], then it’s important enough for them to control.” He anticipates no quantum leap in soft-sensor technology in the next few years, but expects adoption of soft sensors to provide model-based predictions in a variety of new processes and industries, such as pharmaceuticals.
One overall trend is hard to miss: soft sensors will play an increasing role in automated controllers of whatever type.