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Title: Automated control of high temperature short time pasteurization
Creator: Negiz, A., Cinar, A., Schlesser, J. E., Ramanauskas, P., Armstrong, D. J., Stroup, W.
Date: 1996-12
Publisher: ELSEVIER SCI LTD
Description: Cascade and multivariable control of a high temperature shot? time (HTST) pasteurization system were tested and compared with the performance...
Show moreCascade and multivariable control of a high temperature shot? time (HTST) pasteurization system were tested and compared with the performance of single-loop feedback control. Multivariable control was implemented on the basis of computations of product temperatures that yield equivalent lethality at a residence time of 15 s at 161 degrees F in the holding tube. Both cascade and multivariable controllers reduced product temperature fluctuations and overshoot compared to single-loop feedback control. Multivariable control was based on on-line computation of equivalent total lethality and it permitted operation at variable flow rates or at the most desirable temperatures for product quality and functionality. Copyright (C) 1996 Elsevier Science Ltd.
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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: MATHEMATICAL-MODELS OF COCURRENT SPRAY-DRYING
Creator: Negiz, A., Lagergren, E. S., Cinar, A.
Date: 1995-10
Publisher: AMER CHEMICAL SOC
Description: A steady state mathematical model for a cocurrent spray dryer-is developed. The model includes the mass, momentum, and energy balances for a...
Show moreA steady state mathematical model for a cocurrent spray dryer-is developed. The model includes the mass, momentum, and energy balances for a single drying droplet as well as the total energy and mass balances of the drying medium. A log normal droplet size distribution is assumed to hold at the exit of the twin-fluid atomizer located at the top of the drying chamber. The discretization of this log normal distribution with a certain number of bins yields a system of nonlinear coupled first-order differential equations as a function of the axial distance of the drying chamber. This system of equations is used to compute the axial changes in droplet diameter, density, velocity, moisture, and temperature for the droplets at each representative bin. Furthermore, the distributions of important process parameters such as droplet moisture content, diameter, density, and temperature are also obtainable along the length of the chamber. On the basis of the developed model, a constrained nonlinear optimization problem is solved, where the exit particle moisture content is minimized with respect to the process inputs subjected to a fixed mean particle diameter at the chamber exit. Response surface studies based on empirical models are also performed to illustrate the effectiveness of these techniques in achieving the optimal solution when an a priori model is not available. The structure of empirical models obtained from the model is shown to be in agreement with the structure of the empirical models obtained from the experimental studies.
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Title: Automated control and monitoring of thermal processing using high temperature, short time pasteurization
Creator: Schlesser, J. E., Armstrong, D. J., Cinar, A., Ramanauskas, P., Negiz, A.
Date: 1997-10
Publisher: AMER DAIRY SCIENCE ASSOC
Description: High temperature, short time pasteurization was used to evaluate a computer-based system for controlling the pasteurization process, acquiring...
Show moreHigh temperature, short time pasteurization was used to evaluate a computer-based system for controlling the pasteurization process, acquiring data, and monitoring records. Software was used for the control of hot water temperature, flow rate through the centrifugal timing pump, and diversion of under-processed product. Three types of control strategies were conducted: single loop, cascade, and multivariable. The single loop control strategy showed the most rapid responses to temperature changes, but the temperature response curve was slowest to return to its set point. The cascade control strategy showed slower recoveries to temperature changes, but the temperature response curve was smoother. The multivariable control strategy responded slightly faster than the cascade control strategy, and the temperature response curve was slightly smoother than the cascade control strategy. The multivariable control strategy was able to control the flow diversion valve by the use of a lethality controller. The data acquisition system, used to monitor the data obtained from the high temperature, short-time pasteurization system, was within +/- 0.1 degrees C of the temperature recorded by the safety thermal limit recorder. Reliability was determined by examining the changes in the position of the flow diversion valve to identify process deviations and by comparing the changes to the event marker on circular charts. The data acquisition system was an effective alternative for monitoring the completeness of data.
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Title: Modeling, monitoring and control strategies for high temperature short time pasteurization systems - 2. Lethality-based control
Creator: Negiz, A., Ramanauskas, P., Cinar, A., Schlesser, J. E., Armstrong, D. J.
Date: 1998-02
Publisher: ELSEVIER SCI LTD
Description: A lethality-based control system was designed to provide accurate control of a high temperature shout time (HTST) pasteurizer and to process...
Show moreA lethality-based control system was designed to provide accurate control of a high temperature shout time (HTST) pasteurizer and to process milk products with a lethality equivalent of 161 degrees F (71.67 degrees C) or above for 15 s. This control system provides significant flexibility in operating the process and optimizing functional properties of the food components. Multivariable control of an HTST pasteurizer is implemented by using product total lethality to determine the controller set-points. The equation that relates the temperature and flow rate combinations to the product total lethality, 161 degrees F (71.67 degrees C), 15 s, was modified to permit overprocessing levels specified by plant personnel. By using this equation and the set-point value selected for the other variable, set-point values for the temperature or the flow rate controller were computed. The flow and temperature controllers are integrated into a real-time monitoring and control system. The monitoring and control system includes the multivariable controller, the lethality rate calculation module, statistical monitoring of the total lethality, product flow rate, hot wafer outlet temperature, and holding tube exit temperature measurements, and the display screens for visual inspection of the monitoring tools. This 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.
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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: Intelligent process monitoring by interfacing knowledge-based systems and multivariate statistical monitoring
Creator: Norvilas, A., Negiz, A., Decicco, J., Cinar, A.
Date: 2000-08
Publisher: ELSEVIER SCI LTD
Description: An intelligent process monitoring and fault diagnosis environment has been developed by interfacing multivariate statistical process...
Show moreAn intelligent process monitoring and fault diagnosis environment has been developed by interfacing multivariate statistical process monitoring (MSPM) techniques and knowledge-based systems (KBS) for monitoring multivariable process operation. The real-time KBS developed in G2 is used with multivariate SPM methods based on canonical variate stare space (CVSS) process models. Fault detection is based on T-2 charts of state variables. Contribution plots in G2 are used for determining the process variables that have contributed to the out-of-control signal indicated by large T-2 values, and G2 Diagnostic Assistant (GDA) is used to diagnose the source causes of abnormal process behavior. The MSPM modules developed in Matlab are linked with G2. This intelligent monitoring and diagnosis system can be used to monitor multivariable processes with autocorrelated, cross-correlated, and collinear data. The structure of the integrated system is described and its performance is illustrated by simulation studies.
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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: 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: 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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