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Title: Nonlinear time series models for multivariable dynamic processes
Creator: Cinar, A.
Date: 1995-11
Publisher: ELSEVIER SCIENCE BV
Description: Several paradigms are available for developing nonlinear dynamic input-output models of processes. Polynomial models, threshold models, models...
Show moreSeveral paradigms are available for developing nonlinear dynamic input-output models of processes. Polynomial models, threshold models, models based on spline functions, and polynomial models with exponential and trigonometric functions can describe various types of nonlinearities and pathological behavior observed in many physical processes. A unified nonlinear model development framework is not available, and the search of the appropriate nonlinear structure is part of the model development effort. Various artificial neural network structures and nonlinear time series model structures are presented and illustrated by developing a model from data sets generated by a series of example systems. The use of a nonlinear model development paradigm which is not compatible with the types of nonlinearities that exist in the data can have a significant effect on model development effort and model accuracy.
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Title: FORCED PERIODIC OPERATION OF TUBULAR REACTORS
Creator: Ozgulsen, F., Cinar, A.
Date: 1994-10
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Description: Forced periodic operation of tubular reactors can increase conversion and yield if proper operating conditions and forcing policies are...
Show moreForced periodic operation of tubular reactors can increase conversion and yield if proper operating conditions and forcing policies are selected. A numerical approach is proposed for computing the effects of periodic input forcing by a shooting algorithm. Such computational tools permit the assessment of the benefits of forced periodic operation of a specific reactor system and the identification of the ranges of operating conditions where forced periodic operation is beneficial. This information provides valuable insight for planning detailed experimental studies. Determining the influential input variables and bracketing the ranges of the operating conditions for improving reactor performance will reduce significantly the experiments needed for selecting the most favorable reactor operation policies. The application of the numerical algorithm is illustrated by assessing the benefits of forced periodic operation to a CO oxidation reactor model. The results reveal substantial improvement in performance with slow cycling in feed concentration.
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Title: Multivariable MPC system performance assessment, monitoring, and diagnosis
Creator: Schaffer, J., Cinar, A.
Date: 2004-03
Publisher: ELSEVIER SCI LTD
Description: This study focuses on performance assessment and monitoring of model predictive control systems. A methodology is proposed to determine a...
Show moreThis study focuses on performance assessment and monitoring of model predictive control systems. A methodology is proposed to determine a benchmark and monitor model predictive control performance on-line. A performance measure based on the ratio of historical and achieved performance is used for monitoring and a ratio of design and achieved performance is used for diagnosis. Performance monitoring and diagnosis of causes for poor performance are integrated. A real-time knowledge-based system is developed to supervise monitoring and diagnosis activities. Case studies with linear and nonlinear models of an evaporator illustrate the methodology and limitations of linearity assumptions.
Endnote format citation for DOI:10.1016/j.jprocont.2003.07.003
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Title: Monitoring of multivariable dynamic processes and sensor auditing
Creator: Negiz, A., Cinar, A.
Date: 1998-10
Publisher: ELSEVIER SCI LTD
Description: Industrial continuous processes are usually operated under closed-loop control, yielding process measurements that are autocorrelated, cross...
Show moreIndustrial continuous processes are usually operated under closed-loop control, yielding process measurements that are autocorrelated, cross correlated, and collinear. A statistical process monitoring (SPM) method based on state variables is introduced to monitor such processes. The statistical model that describes the in-control variability is based on a canonical variate (CV) state space model. The CV state variables are linear combinations of the past process measurements which explain the variability of the future measurements the most, and they are regarded as the principal dynamic dimensions. A T-2 statistic based on the CV state variables is utilized for developing the SPM procedure. The CV state variables are also used for monitoring sensor reliability. An experimental application to a high temperature short time (HTST) pasteurization process illustrates the proposed methodology.
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Title: Empirical modeling of systems with output multiplicities by multivariate additive NARX models
Creator: Decicco, J., Cinar, A.
Date: 2000-06
Publisher: AMER CHEMICAL SOC
Description: Multivariable additive NARX (nonlinear autoregressive with exogenous inputs) modeling of process systems is presented. The model structure is...
Show moreMultivariable additive NARX (nonlinear autoregressive with exogenous inputs) modeling of process systems is presented. The model structure is similar to that of a generalized additive model (GAM) and is estimated with a nonlinear canonical variate analysis (CVA) algorithm called CANALS. The system is modeled by partitioning the data into two groups of variables. The first is a collection of future outputs, and the second is a collection of past input and outputs and future inputs. This approach is similar to linear subspace state-space modeling. An illustrative example of modeling is presented on the basis of a simulated continuous chemical reactor that exhibits multiple steady states in the outputs for a fixed level of the input.
Endnote format citation for DOI:10.1021/ie9906464
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Title: Statistical monitoring of multivariable dynamic processes with state-space models
Creator: Negiz, A., Cinar, A.
Date: 1997-08
Publisher: AMER INST CHEMICAL ENGINEERS
Description: Industrial continuous processes may have a large number of process variables and are usually operated for extended periods at fixed operating...
Show moreIndustrial continuous processes may have a large number of process variables and are usually operated for extended periods at fixed operating points under closed-loop control, yielding process measurements that are autocorrelated, cross-correlated and collinear. A statistical process monitoring (SPM) method based on multivariate statistics and system theory is introduced to monitor the variability of such processes. The statistical model that describes the in-control variability is based on a canonical-variate (CV) stare-space model that is an equivalent representation of a vector autoregressive moving-average rime-series model. The CV state variables obtained from the state-space model are linear combinations of the past process measurements that explain the variability of the future measurements the most. Because of this distinctive feature, the CV state variables are regarded as the principal dynamic directions A T-2 statistic based on the CV state variables is used for developing an SPM procedure. Simple examples based on simulated data and an experimental application based on a high-temperature short-time milk pasteurization process illustrate advantages of the proposed SPM method.
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Title: Integration of multivariate SPM and FDD by parity space technique for a food pasteurization process
Creator: Kosebalaban, F., Cinar, A.
Date: 2001-03-15
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Description: Multivariate statistical process monitoring (MSPM), contribution plots, and parity space fault diagnosis (FD) techniques are used to detect...
Show moreMultivariate statistical process monitoring (MSPM), contribution plots, and parity space fault diagnosis (FD) techniques are used to detect abnormal operation of dynamic processes and diagnose sensor and actuator faults. The methods are illustrated by monitoring the critical control points (CCP) and diagnosing causes of abnormal operation of a pilot pasteurization plant. An empirical model of the process is developed by using subspace state space system identification methods and normal process data. The process data collected under the influence of different magnitude and duration of faults in sensors and actuators are used to validate the MSPM and FD techniques. T-2 and squared prediction error (SPEN) charts are used as MSPM charts. A parity space technique for dynamic stochastic systems and dynamic trends in contribution plots of T-2 and SPEN statistics are used for FD. The detection and FD by these techniques show significant improvements over univariate methods.
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Title: Diagnosis of process disturbances by statistical distance and angle measures
Creator: Raich, A., Cinar, A.
Date: 1997
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Description: Disturbance and fault diagnosis techniques that rely on statistical methods traditionally utilize distance based discrimination functions....
Show moreDisturbance and fault diagnosis techniques that rely on statistical methods traditionally utilize distance based discrimination functions. Complementary information is contained in the angular relations between data clusters representing process operations under various disturbances. A novel disturbance diagnosis approach is presented based on angle discriminants. The diagnosis method is successful in cases where distance based discrimination is not very accurate. The methodology is illustrated by diagnosing various disturbances in the Tennessee Eastman process and compared with the diagnosis utilizing distance based algorithms.
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Title: Statistical process monitoring and disturbance diagnosis in multivariable continuous processes
Creator: Raich, A., Cinar, A.
Date: 1996-04
Publisher: AMER INST CHEMICAL ENGINEERS
Description: Detecting out-of-control status and diagnosing disturbances leading to the abnormal process operation early are crucial in minimizing product...
Show moreDetecting out-of-control status and diagnosing disturbances leading to the abnormal process operation early are crucial in minimizing product quality variations Multivariate statistical techniques are used to develop detection methodology for abnormal process behavior and diagnosis of disturbances causing poor process performance. Principal components and discriminant analysis ave applied to quantitatively describe and interpret step, ramp and random-variation disturbances. All disturbances require high-dimensional models for accurate description and cannot be discriminated by biplots. Diagnosis of simultaneous multiple faults is addressed by building quantitative measures of overlap between models of single faults and their combinations. These measures are used to identify the existence of secondary disturbances and distinguish their components. The methodology is illustrated by monitoring the Tennessee Eastman plant simulation benchmark problem subjected to different disturbances. Most of the disturbances can be diagnosed correctly, the success rate being higher for step and vamp disturbances than random-variation disturbances.
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Title: Fault detection and diagnosis in a food pasteurization process with hidden Markov models
Creator: Tokatli, F., Cinar, A.
Date: 2004-12
Publisher: CANADIAN SOC CHEMICAL ENGINEERING
Description: Hidden Markov Models (HMM) are used to detect abnormal operation of dynamic processes and diagnose sensor and actuator faults. The method is...
Show moreHidden Markov Models (HMM) are used to detect abnormal operation of dynamic processes and diagnose sensor and actuator faults. The method is illustrated by monitoring the operation of a pasteurization plant and diagnosing causes of abnormal operation. Process data collected under the influence of faults of different magnitude and duration in sensors and actuators are used to illustrate the use of HMM in the detection and diagnosis of process faults. Case studies with experimental data from a high-temperature-short-time pasteurization system showed that HMM can diagnose the faults with certain characteristics such as fault duration and magnitude.
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Title: Statistical monitoring of multistage, multiphase batch processes
Creator: Undey, C., Cinar, A.
Date: 2002-10
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Description: The monitoring of intermediate phases of production is as important as monitoring and control of the final stage. Here a framework is proposed...
Show moreThe monitoring of intermediate phases of production is as important as monitoring and control of the final stage. Here a framework is proposed for monitoring overall process performance at the end of each batch.
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Title: An intelligent system for multivariate statistical process monitoring and diagnosis
Creator: Tatara, E., Cinar, A.
Date: 2002-04
Publisher: I S A-THE INSTRUMENTATION SYSTEMS AUTOMATION SOC
Description: A knowledge-based system (KBS) was designed for automated system identification, process monitoring, and diagnosis of sensor faults. The real...
Show moreA knowledge-based system (KBS) was designed for automated system identification, process monitoring, and diagnosis of sensor faults. The real-time KBS consists of a supervisory system using G2 KBS development software linked with external statistical modules for system identification and sensor fault diagnosis. The various statistical techniques were prototyped in MATLAB, converted to ANSI C code, and linked with the G2 Standard Interface. The KBS automatically performs all operations of data collection, identification, monitoring, and sensor fault diagnosis with little or no input from the user. Navigation throughout the KBS is via menu buttons on each user-accessible screen. Selected process variables are displayed on charts showing the history of the variables over a period of time. Multivariate statistical tests and contribution plots are also shown graphically. The KBS was evaluated using simulation studies with a polymerization reactor through a nonlinear dynamic model. Both normal operation conditions as well as conditions of process disturbances were observed to evaluate the KBS performance. Specific user-defined disturbances were added to the simulation, and the KBS correctly diagnosed both process and sensor faults when present.
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Title: DYNAMIC BEHAVIOR OF PERIODIC FEEDBACK-CONTROL SYSTEMS
Creator: Kendra, S., Cinar, A.
Date: 1991
Publisher: GORDON BREACH SCI PUBL LTD
Description: Periodic feedback is a control strategy implemented by fast zero-average oscillations in the parameters of the control law. It is well known...
Show morePeriodic feedback is a control strategy implemented by fast zero-average oscillations in the parameters of the control law. It is well known that time invariant feedback control offers no means for zeros relocation. Periodic feedback allows zeros relocation in an asymptotic sense, resulting in improved closed loop characteristics, such as increased gain margins.
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Title: PLS, balanced, and canonical variate realization techniques for identifying VARMA models in state space
Creator: Negiz, A., Cinar, A.
Date: 1997-10
Publisher: ELSEVIER SCIENCE BV
Description: This paper demonstrates the application of PLS regression, balanced realization, and canonical variate (CV) state space modeling techniques in...
Show moreThis paper demonstrates the application of PLS regression, balanced realization, and canonical variate (CV) state space modeling techniques in identifying stationary vector autoregressive moving average (VARMA) type of time series models in state space. An example VARMA process model is used to generate data, carry out modeling activities, and compare the three model development techniques. All realization methods provide equivalent state space models. Balanced realization can not handle singularities in the covariance matrix of past observations while all other methods can accommodate such singularities. Balanced realization and classical PLS do not provide minimal state variables that are orthogonal. 'Orthogonal states' PLS and canonical variate state space realization give orthogonal state variables that provide robust parameter estimates from real data, however the PLS method requires an additional singular value decomposition step.
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Title: Interpreting ECG data by integrating statistical and artificial intelligence tools
Creator: Tatara, E., Cinar, A.
Date: 2002-01
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Description: The use of an automated system integrating data conditioning, statistical methods, and artificial intelligence tools to summarize and...
Show moreThe use of an automated system integrating data conditioning, statistical methods, and artificial intelligence tools to summarize and interpret high-frequency physiological data such as the electrocardiogram is investigated. The development of a methodology and its associated tools for real-time patient monitoring and diagnosis is accomplished by using the commercial programming environments MATLAB and G2, a real-time knowledge-based system (KBS) development shell. Data interpretation and classification is performed by integrating statistical classification methods and knowledge-based techniques with a graphical user interface that provides quick access to the analysis results as well as the original data. A KBS was developed that incorporates various statistical methods with a rule-based decision system to detect abnormal situations, provide preliminary interpretation and diagnosis, and to report these findings to the healthcare provider
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Title: Multivariate statistical methods for monitoring continuous processes: Assessment of discrimination power of disturbance models and diagnosis of multiple disturbances
Creator: Raich, A. C., Cinar, A.
Date: 1995-11
Publisher: ELSEVIER SCIENCE BV
Description: A new methodology was reported [1,2] for integrated use of principal components analysis (PCA) and discriminant analysis in order to determine...
Show moreA new methodology was reported [1,2] for integrated use of principal components analysis (PCA) and discriminant analysis in order to determine out-of-control status of a continuous process and to diagnose the source causes for abnormal behavior. Most of the disturbances were identified with good rates of success, with a higher success rate for step or ramp type of disturbances. Quantitative tools that evaluate overlap and similarity between high-dimensional PCA models are proposed in this communication, and their implications on determining the discrimination power of PCA models of processes operating under disturbances are discussed. Diagnosis of several disturbances occurring simultaneously is also investigated. The criterion developed provide upper limits of discrimination power of various single and multiple process disturbances. The techniques developed are illustrated by assessing the process described by the Tennessee Eastman Control Challenge problem [3].
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Title: DESIGN OF RESILIENT CONTROLLABLE CHEMICAL PROCESSES - AN AUTOTHERMAL REACTOR CASE-STUDY
Creator: Chylla, R. W., Cinar, A.
Date: 1990-07
Publisher: AMER CHEMICAL SOC
Description: A technique for the analysis of state-space linear systems is applied to the problem of selection of resilient chemical process designs....
Show moreA technique for the analysis of state-space linear systems is applied to the problem of selection of resilient chemical process designs. Structural Dominance Analysis affords the evaluation of many process design and control configurations and assessment of the effects of potential manipulated variables and disturbances. After a brief presentation of the analysis method, a complex multibed tubular autothermal reactor system is examined. Resilient process configurations, ease of control, and effects of various inputs on reactor state variables and outputs are considered, and effective control configurations are selected.
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Title: NUMERICAL SINGULARITY ANALYSIS
Creator: Adomaitis, R. A., Cinar, A.
Date: 1991
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Description: A numerical scheme is presented for classifying the different static bifurcation behaviors exhibited by a nonlinear system in its remaining...
Show moreA numerical scheme is presented for classifying the different static bifurcation behaviors exhibited by a nonlinear system in its remaining parameter space. This numerical technique differs from previously published schemes in that the application of singularity theory is done numerically and requires no explicit differentiations of the system in question. It also does not require the reduction of the mathematical model to a scalar equation. The utility of this multivariable scheme will be demonstrated through an application to a seven PDE tubular packed-bed reactor model.
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Title: Controller performance assessment by frequency domain techniques
Creator: Kendra, S. J., Cinar, A.
Date: 1997-06
Publisher: ELSEVIER SCI LTD
Description: A system identification based method for assessing the performance of closed-loop systems is proposed, utilizing measures which coincide...
Show moreA system identification based method for assessing the performance of closed-loop systems is proposed, utilizing measures which coincide naturally with classical and modern frequency domain design specifications. Standard robust control system design methodologies seek to maximize closed-loop performance, subject to strict robustness requirements and include specifications for bandwidth and peak magnitude of the sensitivity and complementary sensitivity functions. Estimates of these transfer functions can be obtained by exciting the reference input with a zero mean, pseudo random binary sequence, observing the process output and error response, and developing a closed-loop model. Performance assessment is based on the comparison between the observed frequency response characteristics and the design specifications. Selection of appropriate model structures, experiment design, and model validation which will ensure reasonable estimates of the closed-loop transfer functions are considered in this paper. A case study involving the performance assessment of a packed bed tubular reactor control system is presented.
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Title: SYNTHESIS OF MODEL-BASED CONTROLLERS FOR AN AUTOTHERMAL REACTOR
Creator: Chylla, R. W., Cinar, A.
Date: 1990-08
Publisher: CANADIAN SOC CHEMICAL ENGINEERING
Description: Model-based controllers for a bench scale autothermal tubular packed-bed reactor have been formulated using the Internal Model Control (IMC)...
Show moreModel-based controllers for a bench scale autothermal tubular packed-bed reactor have been formulated using the Internal Model Control (IMC) approach. The Structural Dominance Analysis technique has been used in developing the reduced-order models. Controller performance at robust and sensitive steady states have been assessed through simulations and experiments. Both PI and model-based controllers can regulate reactor operation at robust steady states, but only third order IMC controllers are able to regulate reactor operation at the sensitive steady state.
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