Real-time monitoring and fault detection of pulsed-spray fluid-bed granulation using near-infrared spectroscopy and multivariate process trajectories

Pulsed spray is a useful tool forgranule size control in fluid bed granulation.To improve the quality control of pulsed-spray fluid bed granulation,a combination of in-line near-infrared(NIR)spectroscopy and p「incipal component analysis was used to develop multivariate statistical process control(MSPC)charts.Different types of MSPC charts were developed,including principal component score charts,Hotelling's T2 control charts,and distance to model X control charts,to monitor the batch evolution throughout the granulation process.Correlation optimized warping was used as an alignment method to deal with the time variation in batches caused by the granulation mechanism in MSPC modeling.The control charts developed in this study were validated on normal batches and tested on four batches that deviated from normal processing conditions to achieve real-time fault analysis.The results indicated that the NIR spectroscopy-based MSPC model included the variability in the sample set constituting the model and could withstand external variability.This research demonstrated the application of synchronized NIR spectra in conjunction w让h multivariate batch modeling as an attractive tool for process monitoring and a fault diagnosis method for effective process control in pulsed-spray fluid bed granulation.

New Method for Multivariate Statistical Process Monitoring

A new method using discriminant analysis and control charts is proposed for monitoring multivariate process operations more reliably.Fisher discriminant analysis （FDA） is used to derive a feature discriminant direction （FDD） between each normal and fault operations,and each FDD thus decided constructs the feature space of each fault operation.Individuals control charts （XmR charts） are used to monitor multivariate processes using the process data projected onto feature spaces.Upper control limit （UCL） and lower control limit （LCL） on each feature space from normal process operation are calculated for XmR charts,and are used to distinguish fault from normal.A variation trend on an XmR chart reveals the type of relevant fault operation.Applications to Tennessee Eastman simulation processes show that this proposed method can result in better monitoring performance than principal component analysis （PCA）-based methods and can better identify step type faults on XmR charts.

Some Group Runs Based Multivariate Control Charts for Monitoring the Process Mean Vector

In this article, we propose two control charts namely, the “Multivariate Group Runs’ (MV-GR-M)” and the “Multivariate Modified Group Runs’ (MV-MGR-M)” control charts, based on the multivariate normal processes, for monitoring the process mean vector. Methods to obtain the design parameters and operations of these control charts are discussed. Performances of the proposed charts are compared with some existing control charts. It is verified that, the proposed charts give a significant reduction in the out-of-control “Average Time to Signal” (ATS) in the zero state, as well in the steady state compared to the Hotelling’s T2 and the synthetic T2 control charts.

A New Approach to Diagnosing Signals from Multivariate EWMA Control Chart

Since Lowry et al. [1992] proposed a multivariate version of theexponentially weighted moving average (EWMA) control chart, the multivariate EWMA control chart hasbecome more and more popular in monitoring production processes, especially in chemical processes.A major advantage of multivariate EWMA statistics is that it is sensitive to small and moderateshifts in the mean vector. However, when a multivariate EWMA chart issues a signal, it is difficultto identify which variable or set of variables is out of control. In this paper, we introduce anew approach to diagnosing signals from a multivariate EWMA control chart. The implementationprocedure is that when the multivariate EWMA control chart issues a signal, we adopt a univariatediagnostic procedure to identify the variables or/and the principal components that caused thesignal.

Improving reservoir volumetric estimations in petroleum resource assessment using discovery process models

The reservoir volumetric approach represents a widely accepted, but flawed method of petroleum play resource calculation. In this paper, we propose a combination of techniques that can improve the applicability and quality of the resource estimation. These techniques include： 1） the use of the Multivariate Discovery Process model （MDP） to derive unbiased distribution parameters of reservoir volumetric variables and to reveal correlations among the variables; 2） the use of the Geo-anchored method to estimate simultaneously the number of oil and gas pools in the same play; and 3） the crossvalidation of assessment results from different methods. These techniques are illustrated by using an example of crude oil and natural gas resource assessment of the Sverdrup Basin, Canadian Archipelago. The example shows that when direct volumetric measurements of the untested prospects are not available, the MDP model can help derive unbiased estimates of the distribution parameters by using information from the discovered oil and gas accumulations. It also shows that an estimation of the number of oil and gas accumulations and associated size ranges from a discovery process model can provide an alternative and efficient approach when inadequate geological data hinder the estimation. Cross-examination of assessment results derived using different methods allows one to focus on and analyze the causes for the major differences, thus providing a more reliable assessment outcome.

Incremental multivariable predictive functional control and its application in a gas fractionation unit

The control of gas fractionation unit(GFU) in petroleum industry is very difficult due to multivariable characteristics and a large time delay.PID controllers are still applied in most industry processes.However,the traditional PID control has been proven not sufficient and capable for this particular petro-chemical process.In this work,an incremental multivariable predictive functional control(IMPFC) algorithm was proposed with less online computation,great precision and fast response.An incremental transfer function matrix model was set up through the step-response data,and predictive outputs were deduced with the theory of single-value optimization.The results show that the method can optimize the incremental control variable and reject the constraint of the incremental control variable with the positional predictive functional control algorithm,and thereby making the control variable smoother.The predictive output error and future set-point were approximated by a polynomial,which can overcome the problem under the model mismatch and make the predictive outputs track the reference trajectory.Then,the design of incremental multivariable predictive functional control was studied.Simulation and application results show that the proposed control strategy is effective and feasible to improve control performance and robustness of process.

Machining Error Control by Integrating Multivariate Statistical Process Control and Stream of Variations Methodology

For aircraft manufacturing industries, the analyses and prediction of part machining error during machining process are very important to control and improve part machining quality. In order to effectively control machining error, the method of integrating multivariate statistical process control （MSPC） and stream of variations （SoV） is proposed. Firstly, machining error is modeled by multi-operation approaches for part machining process. SoV is adopted to establish the mathematic model of the relationship between the error of upstream operations and the error of downstream operations. Here error sources not only include the influence of upstream operations but also include many of other error sources. The standard model and the predicted model about SoV are built respectively by whether the operation is done or not to satisfy different requests during part machining process. Secondly, the method of one-step ahead forecast error （OSFE） is used to eliminate autocorrelativity of the sample data from the SoV model, and the T2 control chart in MSPC is built to realize machining error detection according to the data characteristics of the above error model, which can judge whether the operation is out of control or not. If it is, then feedback is sent to the operations. The error model is modified by adjusting the operation out of control, and continually it is used to monitor operations. Finally, a machining instance containing two operations demonstrates the effectiveness of the machining error control method presented in this paper.

PID Controller Tuning for a Multivariable Glass Furnace Process by Genetic Algorithm

Standard genetic algorithms （SGAs） are investigated to optimise discrete-time proportional-integral-derivative （PID） con- troller parameters, by three tuning approaches, for a multivariable glass furnace process with loop interaction. Initially, standard genetic algorithms （SGAs） are used to identify control oriented models of the plant which are subsequently used for controller optimisa- tion. An individual tuning approach without loop interaction is considered first to categorise the genetic operators, cost functions and improve searching boundaries to attain the desired performance criteria. The second tuning approach considers controller parameters optimisation with loop interaction and individual cost functions. While, the third tuning approach utilises a modified cost function which includes the total effect of both controlled variables, glass temperature and excess oxygen. This modified cost function is shown to exhibit improved control robustness and disturbance rejection under loop interaction.

Quality-related monitoring of papermaking wastewater treatment processes using dynamic multiblock partial least squares

Environmental problems have attracted much attention in recent years,especially for papermak-ing wastewater discharge.To reduce the loss of effluence discharge violation,quality-related multivariate statistical methods have been successfully applied to achieve a robust wastewater treatment system.In this work,a new dynamic multiblock partial least squares(DMBPLS)is pro-posed to extract the time-varying information in a large-scale papermaking wastewater treatment process.By introducing augmented matrices to input and output data,the proposed method not only handles the dynamic characteristic of data and reduces the time delay of fault detection,but enhances the interpretability of model.In addition,the DMBPLS provides a capability of fault location,which has certain guiding significance for fault recovery.In comparison with other mod-els,the DMBPLS has a superior fault detection result.Specifically,the maximum fault detection rate of the DMBPLS is improved by 35.93%and 12.5%for bias and drifting faults,respectively,in comparison with partial least squares(PLS).