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Title: Measuring complexity in reactor networks with cubic autocatalytic reactions
Creator: Tatara, E., Birol, I., Cinar, A., Teymour, F.
Date: 2005-04-13
Publisher: AMER CHEMICAL SOC
Description: Systems with high steady-state multiplicity and rich dynamic behavior are difficult to investigate using conventional reductionist methods. A...
Show moreSystems with high steady-state multiplicity and rich dynamic behavior are difficult to investigate using conventional reductionist methods. A network of more than five reactors hosting cubic autocatalytic reactions may potentially have more than 101 steady states and many distinct dynamic regimes, all for the same parameter set. This paper discusses how the static complexity of such systems can be measured to give a holistic picture. To achieve this, stochastic simulations were performed to statistically determine the bifurcation structure of the system, and the gathered information is summarized using a measure akin to fractal dimension. With this measure, the growth of static complexity is investigated as a function of the network size.
Endnote format citation for DOI:10.1021/ie049246t
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Title: Modeling, monitoring and control strategies for high temperature short time pasteurization systems - 1. Empirical model development
Creator: Negiz, A., Ramanauskas, P., Cinar, A., Schlesser, J. E., Armstrong, D. J.
Date: 1998-02
Publisher: ELSEVIER SCI LTD
Description: Dynamic models of high temperature short time (HTST) pasteurization systems can be developed by using empirical model development paradigms...
Show moreDynamic models of high temperature short time (HTST) pasteurization systems can be developed by using empirical model development paradigms such as transfer functions and times series models. Properly designed experiments that excite all output variables provide good data that enable the development of accurate dynamic models. These models are used in feedback control and statistical process monitoring system design. The methodology for time series model development for HTST pasteurization processes is illustrated by using data collected from a pilot scale HTST pasteurization system.
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Title: STABILITY OF TUBULAR AND AUTOTHERMAL PACKED-BED REACTORS USING PHASE PLANE ANALYSIS
Creator: Chylla, R. W., Adomaitis, R. A., Cinar, A.
Date: 1987-07
Publisher: AMER CHEMICAL SOC
Description: The regions of stability and parametric sensitivity of countercurrent reactor/heat exchangers are determined explicitly in the plane of inlet...
Show moreThe regions of stability and parametric sensitivity of countercurrent reactor/heat exchangers are determined explicitly in the plane of inlet feed temperature-inlet coolant temperature. The concept of phase plane analysis is generalized to include all orders of reaction rate expressions, a broader range of system parameters, and is extended to the case of autothermal reactors. An industrial hydrocarbon oxidation reactor model and an autothermal CO oxidation reactor model have been used to illustrate and to evaluate the analysis method. The approach presented here is appealing since the region of safe inlet temperatures is determined explicitly and the region of safe operation can be optimized with respect to the reactor design parameters.
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Title: Modeling, monitoring and control strategies for high temperature short time pasteurization systems - 3. Statistical monitoring of product lethality and process sensor reliability
Creator: Negiz, A., Ramanauskas, P., Cinar, A., Schlesser, J. E., Armstrong, D. J.
Date: 1998-02
Publisher: ELSEVIER SCI LTD
Description: Statistical process monitoring (SPM) is used in food processing industries to improve productivity and product quality. SPM can also provide...
Show moreStatistical process monitoring (SPM) is used in food processing industries to improve productivity and product quality. SPM can also provide information to operators on how close a process is to non-compliance to product safety limits, and carry out periodic checks of sensor accuracy at high frequency. Traditional SPM tools such as Shewhart charts are not appropriate for continuous food processes because of autocorrelation in data. Four alternative SPM techniques are presented and applied to high temperature short time (HTST) dairy pasteurization. The study attempted to achieve compliance of the HTST process operation with the recommended Pasteurized Milk Ordinance by providing a margin between the alarm limits of the monitoring chart and the safety limits. Monitoring of residuals and parameter change detection techniques ave used for monitoring processes with autocorrelated variables. Hotelling's T-2 and residuals of canonical variates techniques are used for monitoring multivariable processes.
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Title: VIBRATIONAL CONTROL OF CHEMICAL REACTORS - STABILIZATION AND CONVERSION IMPROVEMENT IN AN EXOTHERMIC CSTR
Creator: Cinar, A., Rigopoulos, K., Shu, X., Meerkov, S. M.
Date: 1987
Publisher: GORDON BREACH SCI PUBL LTD
Description: Vibrational control is an open-loop control strategy implemented by fast zero-average oscillations in the input variables of a system. It has...
Show moreVibrational control is an open-loop control strategy implemented by fast zero-average oscillations in the input variables of a system. It has been previously shown that vibrational control of an exothermic reaction in a CSTR, introduced via oscillations in the input flow rate, modifies the dynamic behavior of the reactor and enables its stabilized operation in the unstable steady-state region. In the present paper it is shown that simultaneous vibrations in input flow rate and concentration lead, in addition to a reduction of the amplitude of oscillations in reactor temperature and/or increase in reactant conversion. It is also shown that the phase shift between the vibrations of the two input variables affects the behavior of the system as well as the amplitude, the frequency and the form of forcing functions.
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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: Real-time batch process supervision by integrated knowledge-based systems and multivariate statistical methods
Creator: Undey, C., Tatara, E., Cinar, A.
Date: 2003-08
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Description: Real-time supervision of batch operations during the progress of a batch run offers many advantages over end-of-batch quality control. Process...
Show moreReal-time supervision of batch operations during the progress of a batch run offers many advantages over end-of-batch quality control. Process monitoring, quality estimation, and fault diagnosis activities are automated and supervised by embedding them into a real-time knowledge-based system (RTKBS). Interpretation of multivariate charts is also automated through a generic rule-base for efficient alarm handling and fault diagnosis. Multivariate statistical techniques such as multiway partial least squares (MPLS) provide a powerful modeling, monitoring, and supervision framework. Online process monitoring techniques are developed and extended to include predictions of end-of-batch quality measurements during the progress of a batch run. The integrated RTKBS and the implementation of MPLS-based process monitoring and quality control are illustrated using a fed-batch penicillin production benchmark process simulator.
Endnote format citation for DOI:10.1016/j.engappai.2003.09.003
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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: Batch process monitoring and its application to polymerization systems
Creator: Undey, C., Ertunc, S., Tatara, E., Teymour, F., Cinar, A.
Date: 2004-01
Publisher: WILEY-V C H VERLAG GMBH
Description: Slight changes in raw material properties or operating conditions during critical periods of operation of batch and semi-batch polymerization...
Show moreSlight changes in raw material properties or operating conditions during critical periods of operation of batch and semi-batch polymerization reactors may have a strong influence on reaction mechanism and impact final product quality. Online process monitoring, fault detection, fault diagnosis, and product quality prediction in real-time ensure safe reactor operation and warn operators about excursions from normal operation that may lead to deterioration in product properties. Multivariate statistical process monitoring and quality prediction using multiway principal components analysis and multiway partial least squares have been successful in detecting abnormalities in process operation and product quality. When abnormal process operation is detected, fault diagnosis tools are used to determine the source cause of the deviation. Illustrative case studies are presented via simulated polyvinyl acetate polymerization.
Endnote format citation for DOI:10.1002/masy.200450210
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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: An adaptive robust M-estimator for nonparametric nonlinear system identification
Creator: Wu, X. C., Cinar, A.
Date: 1996-08
Publisher: BUTTERWORTH-HEINEMANN LTD
Description: An adaptive robust M-estimator for nonparametric nonlinear system identification is proposed. This M-estimator is optimal over a broad class...
Show moreAn adaptive robust M-estimator for nonparametric nonlinear system identification is proposed. This M-estimator is optimal over a broad class of distributions in the sense of maximum likelihood estimation. The error distributions are described by the generalized exponential distribution family. It combines nonparametric regression techniques to form a powerful procedure for nonlinear system identification. The adaptive procedure's excellent performance characteristics are illustrated in a Monte Carlo study by comparing the results with previous methods.
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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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