Consider Robust Inferential Sensors

Figure 1. Predictions from model closely matched measured values.

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.

Figure 2. Model consistently provided accurate predictions.

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 ADVANTAGESRobust 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.• Inferential sensors can be used not only for estimating parameters but also for running “what-if” scenarios in production planning.In addition, compared to competitive inferential sensors based on other data-driven methods such as neural networks or multivariate statistical regression, robust inferential sensors provide a variety of technical advantages:Better prediction outside the model development range. A key issue for neural networks is that they cannot extrapolate outside the range of the data from which they have been trained. Robust inferential sensors with the optimal complexity of their models resolve this problem. In principle, simple models and smooth functions generalize better than complex highly nonlinear models. A second-order polynomial generalizes better than a 15th-order polynomial, which usually captures noise instead of a functional relationship. Based on several implemented cases, selecting models with proper complexity and smoothness can enable reliable performance at 20–25% outside the model development range [3]. Non-black-box models. Most production engineers dislike black boxes and are very reluctant to implement them for process monitoring and control. This is one of the reasons why neural-network inferential sensors have not been accepted on a mass scale. Robust inferential sensors are based on explicit algebraic equations, which are more acceptable to users in manufacturing.Predictions based on an ensemble of models. It often is preferable to develop an inferential sensor that does not rely on a single model but instead on an ensemble of models, with the average of the various models used as the final prediction. One advantage of using an ensemble sensor is that the standard deviation of the different models in the ensemble can serve as a model disagreement measure. Another advantage is that the ensemble enables redundancy. Because inferential sensors mainly are used in processing conditions, frequently one or more of the instruments measuring the input variables may fail. If the ensemble consists of models with different input variables, another model available in the ensemble still can predict. Greater suitability for process control and optimization. Equation-based empirical models can be optimized explicitly and their responses and sensitivities to the inputs can be obtained by applying mathematical operators over the equations. This analytical predictability means that inferential sensors pose less risk than black-box-based models for closed-loop control. APPLICATION AREASIndustry rapidly realized the economic and technical benefits of inferential sensors. From the early 1990s on, vendors and articles in the literature have reported a spectacular record of successful applications.Environmental emission monitoring epitomizes the role inferential sensors can play. Traditionally, analytical instruments with high maintenance costs perform such monitoring. The inferential sensor alternative, implemented as a classical neural network, is much cheaper and provides accuracy acceptable for federal, state and local regulations in the United States and the European Union. Process variables enable inferring the level of NOx emissions in burners, heaters, incinerators, etc.One of the first popular applications of inferential sensors was for estimating product composition in distillation columns. However, the most widespread implementation in the chemical industry is for predicting polymer quality. Several polymer quality parameters, such as melt index, polymerization rate and conversion, are deduced from reactor temperature, jacket inlet and outlet temperatures, and the coolant flow rate through the jacket. Of special interest is the nonlinear controller developed by Rockwell Automation, called Process Perfecter, that optimizes the transition between different polymer products.Using inferential sensors for troubleshooting and closed-loop control has become a growing trend in the chemical industry. Today, to cope with rapid changes in demand for particular products, many plants strive for greater flexibility to operate at different production rates and grades. This brings new problems (e.g., increases in maintenance costs, unplanned shutdowns and final product quality deviations) — ones that inferential sensors can solve. For instance, Dow AgroSciences used a soft sensor to rid a herbicide product of an undesirable component, which had started appearing recently but was not present in previous production lots.The soft sensor helped identify the main variables that can predict this undesirable component and then can drill down to the root cause to eliminate it. Furthermore, operating in closed-loop control allows plants to be more flexible. However, the sampling rate used for analytical measurements is not fast enough for most control implementations — inferential sensors are the answer.Such sensors fill the growing need in industry for sophisticated nonlinear models of process quality parameters. A number of well-established vendors, such as Rockwell Automation, Aspen Technology, Siemens and Honeywell, already have implemented thousands of inferential sensors in a wide variety of industries. The benefit from improved quality and reduced process upsets is estimated in the hundreds of millions of dollars but the potential market is much bigger.SUCCESS STORIESLet’s now look at two examples of their successful application at Dow.Distillation tower control. Obtaining an accurate and fast prediction of a process quality variable (in this case, propylene concentration) can enable better control of a distillation column. The current analytical technique allows measurement of propylene every 10 minutes, which is not sufficient for control purposes. So, Dow turned to a robust inferential sensor that provides a prediction of propylene concentration every minute.An ensemble of equation-based models has been derived on a genetic programming toolbox developed internally at Dow. The key criterion for model selection was an optimal balance between performance and low complexity. Satisfying the requirement for robustness to measurement faults favors models with different inputs. This led to the selection of the following three nonlinear models: