Building knowledge from plant operating data for process improvement applications Ramasamy, M., Zabiri, H., Lemma, T. D., Totok, R. B., and Osman, M. Chemical Engineering Department, Universiti Teknologi PETRONAS, 31750 Tronoh, Malaysia. ABSTRACT: Large amounts of data collected and stored in process control computers are rich in information but poor in knowledge. Careful and systematic selection and analysis of data can provide more insight (knowledge) into the equipment/process. This knowledge in the form of mathematical models (empirical or semi-empirical) provide the basis for the process improvement applications such as system identification for control, process monitoring, fault detection, soft sensor development, etc. In this paper, four case studies have been presented to illustrate the potential for building knowledge from plant operating data using multivariate statistical analysis and neural networks. In the first case study, a MIMO parsimonious orthonormal basis filter based prediction model has been developed for a pilot scale distillation column. The second example illustrates the detection of control valve stiction using nonlinear principal component analysis (NLPCA) using data collected from an operating plant. In the third example, data from a refinery crude preheat train is analyzed for monitoring the thermal efficiency of the heat exchangers and a fouling prediction model was developed. The last case study illustrates the development of a soft sensor in a pilot scale distillation column. In conclusion, the potential of historical operating data in providing information to build knowledge which in turn can be used for the process operational excellence has been demonstrated. KEYWORDS: Megavariate data, prediction model, fault detection, process monitoring, soft sensor, neural networks
1 INTRODUCTION Globally, process and manufacturing industries are striving to improve product quality and efficiencies through excellence in operation. Significant investments have been made in upgrading instrumentation, data acquisition, computing infrastructures and advanced control systems. It is expected that with more process and product data readily available, useful information and better process knowledge can be gained and used in process operation improvement applications. The modern measurement techniques in process industries enable large amounts of operating data to be collected and stored. With large volumes of data available in the plant historians, the associated data analysis and modeling have become increasingly complex. As a result, much of the available data is either ignored or heavily compressed. A significant amount of the information resident in the data and the potential knowledge derived from this information is not discovered, diminishing the results from the investment made in the information technology infrastructure. The key challenge is how to exploit all the useful information content from a multivariate data. Generally, the process industries data are characterized by missing values, presence of outliers, drifting data, co-linearity in data, multisampling rates and measurement delays. Analysis and interpretation of such complex data is a challenging task and building models (knowledge) require robust mathematical techniques that are capable of dealing with all the above complexities and drawbacks. Moreover, difficulties in developing accurate mechanistic models shifted the attention of the researchers from mechanistic modeling to sophisticated use of historical plant data to develop empirical models. Although several techniques were developed and used, the two major techniques that were widely and
successfully used since the mid 1980s are the multivariate statistical analysis and artificial neural networks (ANN). In this article, a brief introduction is given to multivariate statistical analysis (Section 2) and artificial neural networks (Section 3). Subsequently, four examples dealing with the above techniques are provided that illustrate the potential for building knowledge from historical data. input/output measurements for the prediction of quality variables (Martin et al., 1995). It is possible to effectively treat noisy and highly correlated process measurements using PCA and PLS. PCA models a set of data X onto itself. The data typically contain measurements taken on a process which are highly correlated and as a consequence their covariance matrix Σ is nearly singular. PCA explains the variance of this matrix in terms of a number 2 MULTIVARIATE STATISTICAL ANALYSIS The multivariate statistical techniques, such as principal component analysis (PCA) and projection to latent structures (PLS), project multivariate data down onto lower dimensional space which contains the relevant process information in two or three latent variables. The linear technique of PCA seeks to explain the variance in the data matrix, whilst PLS allows models to be developed which relate the process of new latent variables called principal components. The first principal component is that linear combination of original variables which explains the greatest amount of variability (t i = Xp i ). The loadings, p i, define the direction of greatest variability, and the score vector, t i, represents the projection of each object onto p i. The second principal component is defined to be orthogonal to the first and explains the next greatest amount of variability, i.e., t 2 = E 1 p 2 where E 1 = X t 1 p T 1.
3 ARTIFICIAL NEURAL NETWORKS (ANN) Over the years, the application of ANN in process industries has been growing in acceptance. ANN is attractive due to its information processing characteristics such as nonlinearity, high parallelism, fault tolerance as well as capability to generalize performance. The third factor is the model size and complexity. A small network may not able to represent the real situation due to its limited capability, while a large network may over fit noise in the training data and fail to provide good generalization ability. Finally, the quality of a process model is also strongly dependent on network training. and handle imprecise information (Haskins and Himmelblau, 1988). Such characteristics have made ANN suitable for solving a variety of problems. This has been proven in various fields such as pattern recognition, system identification, prediction, signal processing, fault detection, soft sensors and others. In general, the development of a good ANN model depends on several factors. The first factor is related to the data being used. The model qualities are strongly influenced by the quality of data used. The second factor is network architecture or model structure. Different network architecture results in different estimation 4 ILLUSTRATIVE EXAMPLES The type of model, the model structure and configuration mainly depend on the objective of the model. It could be to estimate a quality parameter that is slow, expensive, or difficult to measure and infrequent quality variables (soft sensors) or to predict multi-step ahead such as in model predictive control or to diagnose a fault in the process/equipment (instrument failure, control valve stiction) or to monitor the process performance. The examples below illustrate each one of them from case studies being studied by our group.
Temperature, Temperature, o C 4.1 Example 1: Prediction Model Development model can be developed from the innovation sequence of the GOBF model. Model predictive control (MPC) applications largely depend on the accuracy 80 79 T 14 (Top Temperature) Measured Predicted of the models used for prediction. Since MPC involves optimization of a cost function to estimate, present and future, optimal control moves, the prediction model should be simple and parsimonious in parameters and thus making less computationally intensive. A Box-Jenkins type model involving a generalized orthonormal basis filter (GOBF) model as the deterministic part and auto regressive moving average (ARMA) noise model was developed for a pilot scale binary distillation column (Lemma et al. 2009). The major advantages of this type of model are: (i) the GOBF model is parsimonious; (ii) the model parameters can be estimated using least squares; (iii) a priori information on the time delay is not required; and (iv) the noise 78 77 o C 76 75 3000 3200 3400 3600 3800 4000 k 83.4 83.2 82.8 82.6 82.4 82.2 o C 83 82 81.8 (a) T 1 (Bottom Temperature) 81.6 3000 3200 3400 3600 3800 4000 k (b) Measured Predicted Figure 1. Prediction of temperatures by the GOBF-ARMA model: (a) top temperature and (b) bottom temperature Figure 1 shows the prediction of top and bottom temperatures by the multi-input multi-output GOBF-ARMA model. The input variables are the reflux and steam flow rates while the output variables are tray 1 and tray 14 temperatures.
4.2 Example 2: Fault detection Nonlinear principal component analysis (NLPCA) is a nonlinear generalization of PCA for feature extraction and was introduced by Kramer (1991). This autoassociative neural-network based generalization of PCA allows nonlinear mapping between the original and the reduced dimensional spaces. Applications of NLPCA can be found in many fields. The NLPCA structure is shown in Fig. 2. Both the first and final hidden layers have dimensions greater than the input/output layer. The key feature of the network is the bottleneck inner layer. The use of a single neuron in the bottleneck layer allows compression of the inputs to a onedimensional time series before the outputs are reconstructed in the demapping layer. Following convergence, the network bottleneck provides information which describes significant features or signatures of the original data. Input Layer Mapping Layer Bottleneck Layer Demapping Layer Output Layer m m p Fig. 2. The architecture of the five-layer feedforward auto-associative neural network. The network bottleneck, which represents the optimal one-dimensional curve, called the principal curve, characterizing the inputs, allows the usage of simple coefficient of determination R 2 in quantifying the nonlinear behavior of the loop. If R 2 value is much less than 1, then there is a possibility of presence of nonlinearity or Non-Gaussianity. The presence of nonlinearity or stiction is then detected via an index called the NLPCA curvature index, I NC, value. The proposed method has been successfully applied in the detection of stiction for some industrial control loops Zabiri and Ramasamy (2009). One of the control loops is a Liquified Petroleum Gas
y pv op pv and sp (LPG) bottom flow control loop. Data on controller output (op) and controlled variable (pv) were collected from the plant. Figure 3(a) shows the time trends of pv, op and the set-point (sp), where oscillations in the pv and op are significantly obvious. The 1270 1260 1250 1240 1230 1220 0 50 100 150 200 250 300 350 400 450 500 62 60 58 0.8 0.6 0.4 0.2-0.2-0.4-0.6-0.8 56 0 50 100 150 200 250 300 350 400 450 500 1 0 (a) -1-1 -0.5 0 0.5 1 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 (b) op 0.2 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 u (c) Fig. 3. Analysis of data from an industrial LPG bottom flow control loop: (a) Time trend; (b) pv-op plot; (c) NLPCA s output. presence of stiction nonlinearity was confirmed by the very high I NC value of 78.63, and the corresponding NLPCA output is as shown in Fig. 3(c). This can further be verified by the distinct cycles in the characteristics of pv-op plot in Fig. 3(b) which is typical of stiction. 4.3 Example 3: Process Monitoring - Heat Exchanger Performance Analysis Fouling in Crude Preheat Train (CPT) in oil refineries is a serious problem that consumes additional energy and affects the plant economy. Understanding or predicting the fouling characteristics in CPT is imperative to operate the CPT in an optimal manner, with minimum or no fouling. However, fouling is very complex and determined by the crude/crude blend being processed in CPT, the temperatures and flow rates.
Fouling resistance (m 2 C/W)) C/W)) Fouling mechanism is very complex and it is difficult to develop a fundamental model to predict the fouling rate for different crude blends and different operating conditions. Recently, neural networks have been shown to approximate nonlinear functions up to any desired level of accuracy. In this study, a Multi Layer Perceptron (MLP) neural network with Nonlinear Auto Regressive with exogenous input (NARX) structure is used to model a heat exchanger in the CPT. In the heat exchanger chosen for the study, crude oil flows through the tube side and kerosene flows through the shell side. Data were collected from the plant historian for a period of two years consisting of: (i) operational data (cold and hot stream inlet and outlet temperatures, and flow rates); (ii) crude blend information; (iii) crude and product properties; (iv) operation and maintenance reports; and (v) heat exchanger design data. The data were analyzed for outliers using PCA and reconciled. Relevant input variables were selected through PLS. The data set was divided into training and validation sets. A neural network model with one hidden layer was chosen for modeling the heat exchanger. The number of nodes in the input layer was 13, equal to the number of input variables, and the number of neurons in the hidden layer was chosen as 18 by trial and error. Tangent hyperbolic activation function was used in the hidden layer. Figure 4 shows the comparison of prediction of fouling resistance over time with the actual fouling resistance. 10 x 10-3 9 8 7 6 5 4 3 2 0 20 40 60 80 100 120 Day Actual Predicted Figure 4. Actual and predicted fouling resistance during the validation period
4.4 Example 4: Soft Sensor Application Soft-sensor is a model that utilizes the measured values of some secondary variables of a process in order to estimate the value of an immeasurable primary variable of particular importance. Soft was built for predicting the composition in the top product. Figure 5 shows the comparison between the actual top product composition and predictions by the feedforward network with sigmoidal and linear transfer functions and one hidden layer. sensors have been widely reported to supplement online instrument measurements for process monitoring and control. The availability of large volume of data renders data-driven soft-sensor development a viable alternative. In this example, a pilot scale distillation column was operated with acetone-isopropyl alcohol as the feed material. Experiments were performed for variations in reflux flow rate, steam flow rate and feed flow rate. Samples were collected at the top product stream every six minutes and analyzed using a gas chromatography (GC). Other measurements include temperatures, flow rates, pressure, etc. were acquired through the data acquisition system. A neural network model Figure 5. Prediction of top-product composition by neural network models 5 CONCLUSIONS The potential of historical operating data in providing information to build knowledge which in turn can be used for operational excellence through various applications such as system identification, fault detection, process monitoring and soft sensor development has been demonstrated through appropriate case studies.
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