Consider Robust Inferential Sensors

Easier-to-develop-and-maintain sensors offer significant benefits for chemical processes

By Arthur Kordon, Kordon Consulting, LLC; and Leo Chiang, Zdravko Stefanov and Ivan Castillo, The Dow Chemical Company

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Some critical parameters (composition, molecular distribution, density, viscosity, etc.) in chemical processes are not measured online. Instead, their values are determined either by laboratory samples or offline analysis. However, for process monitoring and quality supervision, the very slow response time of these relatively low frequency measurements (taken every several hours or even days) may cause loss of production due to poor quality control. In situations with potential for alarm showers, lack of critical parameters available online could result in a significant negative impact and eventually could lead to shutdowns. One of the approaches to address this issue is through development and installation of expensive hardware online analyzers. Another solution is to use soft or inferential sensors that deduce the critical parameters from easy-to-measure variables such as temperatures, pressures and flows.


Reference 1 describes the current state of the art of inferential sensors. At a very general level, the sensors fall into two different classes — model-driven and data-driven. Model-driven sensors are developed based on first-principles models, which can be costly and require deep process knowledge. Data-driven sensors are developed based on empirical models derived from plant data. The majority of applied inferential sensors are data-driven.

The data-driven sensor model-development process consists of data gathering, preprocessing, variable selection, model structure design (nonlinear or linear), and model validation. Once the inferential sensor is deployed, model maintenance mechanisms such as diagnosis of under-prediction are desirable for quickly tuning the model’s parameters or troubleshooting the process.

Several empirical modeling methods are used to extract relevant information from historical manufacturing data to develop inferential sensors. In the case of linear relationships between process and quality variables, multivariate statistical regression models such as partial least squares (PLS) can serve to find these empirical correlations. When dealing with high-dimensional data, a successful variable-selection procedure will improve the interpretation and identification of the underlying process conditions. Reference 2 provides an extensive review of PLS-based variable-selection methods that are effective in an industrial context. An important advantage of PLS is its capability of providing diagnosis where changes in operating conditions and faulty situations can be detected and utilized during the deployment stage.

Since the early 1990s, a more generic approach that captures nonlinear relationships based on artificial neural networks has been used. Neural networks are black-box empirical models designed by mimicking the human nervous system. They have several features that are very appropriate for inferential sensors’ design, such as universal approximation, models are developed by learning from data and can be implemented online.

Due to these features, many applied inferential sensors are based on neural networks. However, neural networks have some limitations, such as low performance outside the ranges of process inputs used for model development. Model development and maintenance require specialized training — and frequent retraining, which significantly increases the maintenance cost. In addition, model deployment demands specialized run-time licenses.

An alternative technology —the robust inferential sensor — has been under development at The Dow Chemical Company since 1997. It is based on genetic programming and resolves most of the issues of neural-network-based inferential sensors. The robust inferential sensors are in the form of explicit algebraic equations automatically generated with an optimal tradeoff between accurate predictions and simple expressions. As a result, they offer more robust performance in the face of minor process changes. Dow facilities, such as the Pittsburg, Calif., plant pictured on the opening page, are gaining significant benefits from robust inferential sensors. Reference 3 gives a detailed description of the technology. That paper is based mostly on the experience of applying robust inferential sensors at Dow.

IMPORTANT ADVANTAGES
Robust inferential sensors provide both economic and technical benefits.

From an economic standpoint:
• Inferential sensors allow tighter control of the most critical parameters for final product quality and, as a result, enable significant improvement in product consistency.
• Online estimates of critical parameters reduce process upsets through early detection of problems.
• The sensors improve working conditions by decreasing or eliminating laboratory measurements in a dangerous environment.
• Very often such sensors provide optimum economics. Their development and maintenance cost is much lower than that of first-principles models and less than the cost of buying and maintaining hardware sensors.

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