Soft Se - American Chemical Society

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Chapter 9

Soft Sensors: Chemoinformatic Model for Efficient Control and Operation in Chemical Plants Hiromasa Kaneko and Kimito Funatsu* Department of Chemical System Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan *E-mail: [email protected]

Soft sensors are an essential tool for controlling chemical and industrial plants. In this book chapter, we introduce soft sensors, their applications, their roles, their problems and their research examples such as adaptive soft sensors, database monitoring and efficient process control. The use of soft sensors enables chemical industrial plants to be operated more effectively and stably.

Introduction In operating chemical industrial plants, plant operators have to monitor operating conditions and control process variables. Thus, process variables such as temperature, pressure, liquid level and concentration of products need to be measured in real time. However, some of them are not easy to measure in real time because of technical difficulties, large measurement delays, high investment cost and so on. Therefore, soft sensors (1–3) are widely used to predict values of process variables that are difficult to measure in real time. Figure 1 shows the basic concept of a soft sensor. An inferential model is constructed between process variables that are easy to measure in real time, which are called X-variables, and process variables that are difficult to measure in real time, which are called y-variables, using chemoinformatics methods. The values of y can then be predicted using that model with a high degree of accuracy. Both lab samples and measurements of online analyzers are examples of y-variables.

© 2016 American Chemical Society Bienstock et al.; Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.


Figure 1. Basic concept of a soft sensor.

Soft sensor models can be categorized into three types: first-principle models (white-box models), statistical models (black-box models) and hybrid models (gray-box models). First-principle models are constructed based on physico-chemical models of the actual process. Although no operating data are required in model construction, not all phenomena cannot be considered in first-principle models and their predictive ability becomes low in unimagined disturbances in chemical industrial plants. Statistical models are built using a operating data set. An adequate amount of data is required to construct appropriate statistical models. Hybrid models are the combination of first-principle models and statistical analysis. Due to the huge operating data sets, statistical models and hybrid models are mainly used in chemical and industrial plants. Principal component regression (4) and partial least squares (PLS) (5) are mainly used as a statistically modeling method for soft sensors since X-variables are usually correlated in operating data. Nonlinear PLS (6, 7), artificial neural network (8, 9), locally-weighted PLS (10, 11) and support vector regression (12, 13) are employed to handle nonlinear relationships between X and y. Least absolute shrinkage and selection operator (LASSO) (14) can both select X-variables and construct regression models. Because X-variables can affect y-variables with time-delays, important X-variables and optimal time-delays of each variable are selected simultaneously using genetic algorithm-based process variables and dynamics selection (GAVDS) (15). In petrochemical processes such as distillation columns (9, 16) and chemical reactors (17), the use of soft sensors is increasingly common since the number of y-variables is large in product quality that should be controlled. Examples of y-variables are concentration of chemical components, 90% distilling temperature, specific weight, polymer density and melt flow rate. X-variables are temperature, pressure, liquid level, flow rate and so on. Plant operators can acquire y-values estimated by soft sensors and can use them for real time process control, which leads to much cost-saving in operating plants. In pharmaceutical processes, tablets whose key ingredient is the drug compound must be produced, meeting rigorous quality requirements in spite of the variance of raw materials and changes of production facilities (18). When 160

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a tablet in a batch cannot pass the quality tests after several processes such as mixing, tabulating and coating, or even the final product test, all tablets in the batch are wasted, which takes enormous costs. Therefore, the quality of tablets should be monitored and controlled in real time, but quality measurements such as active pharmaceutical ingredient (API) content takes too much time. In addition, it is desired that the quality of not just some tablets, but all tablets could be measured in a batch. Process analytical technology (PAT) (19–23) is an important technique for monitoring, developing, controlling and designing critical product quality in pharmaceutical industry. Near infrared (NIR) spectroscopy (18), Raman spectroscopy (24) and so on have been focused to monitor product quality non-destructively in real time as X-variables. Soft sensor models are constructed between the quality of tablets and the intensity of NIR spectroscopy. Soft sensors can achieve real time release testing (RTRT), in which the quality is controlled in each process by monitoring the quality and doing appropriate actions in real time, and the final product test would not be required. In addition, control limits can be set and the quality of products can be controlled by using soft sensors, which is quality by design (QbD) (25). The use of soft sensors is expanding now in pharmaceutical processes. In water treatment fields such as sewage treatment and industrial liquid waste treatment, membrane bioreactors (MBRs) have been widely used to purify wastewater for reuse (26, 27). MBRs combine biological treatment with membrane filtration. First, bacteria within activated sludge metabolize the organic pollutants and produce environmentally-acceptable metabolites, then a microfiltration or ultrafiltration membrane separates liquids from solids, i.e. the clean water from the sludge. One of the critical difficulties is membrane fouling (28, 29). Membrane fouling is a phenomenon wherein foulants, such as activated sludge, sparingly-soluble compounds, high molecular weight solutes and colloids, absorb or deposit on the membrane surface and absorb onto and block the membrane pores. For example, in cases where MBRs are operated under constant-rate filtration, significant energy is required to achieve constant permeate flow rate due to membrane fouling. The intermittent filtration with aeration is a standard way to reduce fouling while operating MBRs. Backwashing can remove foulants on membrane and those into membrane pores. Furthermore, chemical cleaning must be carried out with chemical reagents after a given period of processing time, when the transmembrane pressure (TMP) exceeds a given value, because some foulants cannot be removed by physical cleaning and these residual foulants will prevent the recovery of membrane performance with time. On the one hand, frequent chemical cleaning requires much cost; on the other hand, MBR operation with a high TMP level takes much operation cost and membrane does not recover even with chemical cleaning if fouling progresses too much, which means that chemical cleaning at appropriate timing is desirable in MBR operation. Therefore, to enable chemical cleaning to be performed at appropriate time, membrane fouling must be predicted in the long term by using soft sensors (30–36). If predicted TMP exceeds a threshold at a time, for example, one week later, chemicals can be prepared for chemical cleaning before one week. When multiple MBRs are controlled in a central management office, a schedule of chemical cleaning for the MBRs can be created using fouling prediction results. 161


Soft sensors have been used in other fields such as prediction of soil moisture and nutrient concentration in agriculture (37, 38), prediction of fruit internal quality such as sugar concentration and acid degree in selection of fruits (39, 40), explosive detection (41), prediction of end points (42) and particle size distribution of powder (43) in iron manufacture. The range of application of soft sensors is also expanding and will be wider in the future.


Roles of Soft Sensors First, as we already mentioned in the introduction section, soft sensors are used instead of analyzers. Soft sensors predict values of difficult-to-measure variables continuously, and then, the predicted values can be used for continuous process control. In addition, measurement frequencies of analyzers can be reduced by using predicted values instead of measured values. Second, soft sensors are used for abnormal detection of analyzers. Figure 2 shows a time plot of concentration. If the data in Figure 2 are measured, the first sample and the last sample may be abnormal and the concentration analyzer may be broken because these are out of distribution. By using a soft sensor and comparing the measured values and the predicted values, abnormal events can be detected. Since outliers in y-values lead to wrong actions in process control and make process control to be difficult, the parallel use of y-analyzers and soft sensors contributes to stable process control.

Figure 2. Abnormal detection of an analyzer using a soft sensor. 162 Bienstock et al.; Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.


Third, soft sensors can be employed for interpretation of relationships among process variables. If the linear regression model is constructed as follows:

of course there is collinearity between process variables in fact and the interpretation is not so simple, but temperature will have a positive contribution to concentration and flow will have a negative contribution to concentration. The understanding of relationships between X and y contributes to the way to manipulate X-values for controlling y-values. Lastly, although monitoring y-values using soft sensors enables continuous process control, more efficient process control can be performed by inverse analysis of soft sensors (44). After construction of soft sensor models, the constructed soft sensor model is inversely analyzed to search the optimal operating procedure of X for efficiently and stably controlling y-values. The details of this method is explained in “Efficient Process Control Using Soft Sensors” section.

Problems of Soft Sensors Although soft sensors are a very useful tool, soft sensors have problems. Figure 3 shows the stages from data collection to operation of soft sensor models and problems corresponding to each stage. First, data are measured in processes and are collected for the construction and the validation of soft sensor models. Problems are reliability of data and data selection. Then, collected data are pre-processed. In this stage, outlier detection and noise treatment should be performed. After that, soft sensor models are constructed with the pre-processed data. Problems are selection of appropriate regression methods, over-fitting, nonlinearity among process variables, variable selection and consideration of dynamics. Then, constructed models are analyzed and operated. We should consider model interpretation, model validation, applicability domain and predictive accuracy, model degradation, maintenance of models, and detection and diagnosis of abnormal data. One of the crucial problems is degradation of soft sensor models or model degradation. Predictive accuracy of soft sensors decreases gradually, a result of the changing state of chemical plants due to factors such as catalyzing performance loss, and sensor and process drift. Kaneko and Funatsu categorized the degradation of soft sensor models (45). Figure 4 shows basic concepts of the degradation of linear soft sensor models constructed between X and y. Figure 4(a) and (b) represent shifts of y-values and X-values, respectively. These are corresponding to sensor and process drift, scale deposition on pipes, changes of operating conditions such as the amount of raw materials, and so on. The slope does not change between training data and new data, but values of y-variables or X-variables shift. Figure 4(c) represents changes of the slope of X and y. This is corresponding to catalyzing performance loss, changes of operating condition such as concentration in raw materials, and so on. Of course, shifts of y-values and X-values, and changes of the slope will occur simultaneously. 163



Figure 3. Flow of soft sensor analysis and problems in each stage

Figure 4. Basic concepts of the degradation of a linear soft sensor model (45). When we focus on the rate of the degradation, each shift or change happens gradually, rapidly, or instantly. For example, catalyzing performance loss, process and sensor drift, changes of external temperature, and scale deposition on pipes occur gradually; sharp changes in raw materials occurs rapidly; and correction of drift, regular repair of plants, and a stoppage of pipes occur instantly. Of course, this rapidity is continuous in fact. 164 Bienstock et al.; Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.


Adaptive Soft Sensors To avoid model degradation, adaptation mechanisms can be applied to soft sensors (46). These soft sensors are called adaptive soft sensors. For example, new data of both X and y are measured in chemical plants and used to reconstruct soft sensor models and predict y-values. Kaneko and Funatsu categorized adaptive soft sensor models and discussed characteristics of adaptive soft sensors for each type of the model degradation (45). Adaptive soft sensors include moving window (MW) models (16, 47–51), just-in-time (JIT) models (10, 11, 52–54) and time difference (TD) models (55–59). MW models are constructed with a recently measured data set; JIT models are constructed by assigning larger weights to the data that are most similar to a query; and TD models are constructed by considering the time difference of y-variables and that of the X-variables. Ensemble learning can be applied to adaptive models (48, 51, 60). Table 1 shows the characteristics of TD, MW, and JIT models. TD models can adapt shifts of both y-values and X-values because it achieves the same effect as bias update in prediction. Even when the shifts happen gradually, rapidly and instantly, TD models can follow the shifts appropriately. However, TD models cannot adapt changes of the slope (45).

Table 1. Characteristics of TD, MW, and JIT models (45). Degradation Type

Shift of y-value

Shift of X-value

Change of the slope

Shift of X-value andchange of the slope

TD model

MW model

JIT model

Gradual

○a

○

×c

Rapid

○

4b

×

Instant

○

×

×

Gradual

○

○

○

Rapid

○

4

○

Instant

○

×

○

Gradual

×

○

×

Rapid

×

4

×

Instant

×

×

×

Gradual

×

○

○×d

Rapid

×

4

○×

Instant

×

×

○×

Rapidity

a

b

The model can handle the degradation well. The model can handle the degradation to some extent. c The model cannot handle the degradation. d It depends on a situation whether the model can handle the degradation or not.

165 Bienstock et al.; Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.


MW models should be used to follow gradual changes of the slope by adding new data to training data. However, it is difficult for MW models to adapt rapid and instant shifts because old data before shifts remains in training data in MW models the models are badly affected by old data. In case of JIT models, which are constructed with data sets close to test data in the space of X-variables, appropriate selection of data sets will be performed if a shift of X-values happens. However, besides that, data sets after shifts of y-values or changes of the slope cannot be selected because there is no change in the space of X-variables as shown in Figure 4(a) and (c). When shifts of X-values and changes of slope happen simultaneously, JIT models can adapt these changes appropriately if X-values change clearly and adequate amount of data in new situations is stored in database. This is because appropriate selection of data sets can be performed owing to shifts of X-values. The above discussion results were confirmed and knowledge and information on appropriate adaptive models for each type of the degradation could be obtained by analyzing simulated data sets and a real industrial data set (45). As shown in Table 1, there are no all-round adaptive models with high predictive ability in all types of model degradation. The important thing is appropriate adaptive models for each type of degradation. Here, a model selection method based on the reliability of TD models is introduced (61). TD models are used to predict values of y-variables and its reliability is monitored using the ensemble prediction method in which multiple predicted y-values are obtained by changing the differential values of X-variables and the standard deviation of multiple predicted y-values is an index of prediction reliability. When the reliability is low, a TD model is switched for an MW model or a JIT model. It was confirmed that a combination model of TD and MW models and that of TD and JIT outperformed a single TD model, a single MW model and a single JIT model through a case study using real industrial data. In addition, the predictive ability of a combination model of TD and MW models was higher than that of a combination model of TD and JIT models (61). By switching a TD model and an MW model, or a TD model and a JIT model, wide range of model degradation can be handled. However, the predictive ability of the current MW, JIT and TD models are not entirely sufficient when rapid changes in the slope, i.e. time-varying changes in processes, occur as shown in Table 1. Therefore, ensemble online support vector regression (EOSVR) (51) was developed as an MW model. Multiple SVR models with different hyperparametervalues predict multiple y-values. The predicted y-values are combined based on the current predictive ability of each SVR model and Bayes’ rule to produce a final predicted y-value. The current predictive ability of each SVR model is calculated as inversely proportional to the root-mean-square error for the midpoints between the k-nearest-neighbor data points (RMSEmidknn) (62) as follows:

where RMSEmidknn,i is the RMSEmidknn of the ith SVR model with the latest data. In addition, the standard deviation of the predicted y-values enables us to estimate 166 Bienstock et al.; Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: ... ACS Symposium Series; American Chemical Society: Washington, DC, 2016.

the prediction error in the final predicted y-value for each process state. The effectiveness of EOSVR and its superiority over traditional adaptive soft sensors were demonstrated by analyzing a numerical simulation data set in which the relationship between X and y is nonlinear and two real industrial data sets (51).


Database Monitoring for Soft Sensors To construct adaptive soft sensors with high predictive accuracy for a wide data range, database monitoring is a crucial problem (63). To reduce the size of a database, JIT models select new measurement data to be stored based on the prediction errors of a y-variable (64, 65). Jin et al. proposed a method in which a new sample should replace the most similar data in database (66). However, overlap between overall information in a database and that in a new measurement sample was not considered in data selection. Kaneko et al. proposed a database monitoring index (DMI) for database management (DBM) that examines the amount of information in a new measurement sample (20) and achieves maintenance-free DBM and highly predictive soft sensors (67). The DMI for managing databases is defined between two data (x(i), y(i)) and (x(j), y(j)) as follows:

where sim(x(i), x(j)) is the similarity between x(i) ∈ R1×m (m is the number of Xvariables), which cannot be zero, and x(j) ∈ R1×m, and a is a constant. Various similarity indexes can be used, such as the Gaussian kernel:

where γ is a tuning parameter controlling the width of the kernel function, and the inverse Euclidian distance:

Assuming that there are no completely the same data, the denominator of eq. (5) is not zero. Using eqs. (3) and (4), the DMI is calculated as follows:

When eqs. (3) and (5), the DMI is represented as follows:



The DMI is large when two X and y data are dissimilar, and vice-versa. If the minimum DMI value between a new sample and data in database exceeds the threshold PDMI, the new sample contains sufficient new information and is stored in database. Given initial database, the hyperparameter a, which is important to determine the weight of y for X (67), in DMI and the threshold PDMI are automatically optimized, considering predictive ability of regression models (68). Assuming there exist some data that are similar to other data and are not indispensable in training data, PDMI can be discussed while repeating the deletion of one of such similar data and the check of the predictive ability of the regression model constructed with the remaining training data. DMI was modified for adaptive (both MW- and JIT-based) soft sensors with long-term high predictive ability (69). Case studies using simulated and real industrial data confirmed that highly predictive and adaptive soft sensors could be maintained over long periods with only a small number of data. DBM can also be applied to process monitoring (70), where models are updated or reconstructed with a database that includes new measurement data.

Efficient Process Control Using Soft Sensors Although proportional-integral-derivative (PID) controllers are used to control values of process variables, it is difficult to control values of process variables that are difficult to measure since PID controllers are based on the difference of a set point and a measured values of a controlled variable. Because soft sensors can estimate values of process variables that are difficult to measure in real time, continuous process control can be performed by using y-values estimated by soft sensors instead of measured y-values in PID control. However, this is far from enough to make full use of soft sensors. By analyzing soft sensor models inversely, more efficient way to control y-values would be found. Figure 5 shows the basic concept of inverse analysis of a soft sensor model, assumed that a soft sensor model is constructed already. First, basic patterns of changes of X-values are determined based on history data in which some control such as PID control is conducted. Basic patterns are simplified using some points and interpolation such as Hermite interpolation. For instance, piecewise cubic Hermite interpolating polynomial (PCHIP) (71) can be used to determine simplified points, in which Hermite interpolation is employed repeatedly. It should be noted that each point is determined by time and a y-value. Each point should be optimized so as to efficiently control y-values. Candidates for time and an X-value of every point are prepared and patterns in changing X-values are generated exhaustively. Then, each pattern of X-values is input into the constructed soft sensor model and the output pattern of y is checked in terms of controlled performance of y. Integral of squared error (ISE) and settling time, for example, are used to quantify controlled performance of y. ISE is given as follows:



where e(t) means the error between a set point and a y-value. If the number of candidates for time and X-values in the points is too high to check controlled performance of y for all candidates, an optimization method such as genetic algorithm can be used. This method is called inverse soft sensor-based feed forward control method (ISFF) (44). ISFF was applied to the change of a set point of y in a simulated continuous stirred-tank reactor system. Compared with a traditional PI controller, which was optimized in the system, ISFF could control y-values rapidly and stably. The details of the results are shown in the reference (44). ISFF can be switched to a feedback controller such as a PID controller since a soft sensor model includes estimation errors and only ISFF cannot completely settle y-values to a set point. In addition, a feedback function can be installed in ISFF by using adaptive soft sensors as shown in “Adaptive Soft Sensors” section.

Figure 5. Basic concepts of inverse analysis of a soft sensor model.

Conclusions Soft sensors are a useful tool in chemical industrial plants. Their applications spread in many fields such as petrochemical processes, pharmaceutical processes, water treatment fields, agriculture fields, selection of fruits, explosive detection and iron manufacture. However, there are still problems remaining and further revitalization of research and development is strongly desired for those problems in soft sensor analyses. Since problems in soft sensor analysis are similar to those in chemoinformatics and chemometrics, research products in chemoinformatics and chemometrics fields can be applied in soft sensor analyses widely. It is desirable that chemical industrial plants are operated and controlled more effectively and stably using soft sensors. 169


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