Control Analysis and Tuning of an Industrial Temperature Control System

Zajic, Ivan; Larkowski, Tomasz; Burnham, Keith J.; Hill, Dean

doi:10.3182/20120328-3-it-3014.00115

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…The key idea of SRIVC identification is to assume that the disturbing noise is only available at the sampling instances, and termed as filtered discrete white noise. An iterative SRIVC is a successful stochastic identification technique − also reported by various researchers; present results also reaffirm those findings.…”

Section: Resultssupporting

confidence: 90%

“…Among these the prediction errors (PE) method and the instrument variable (IV) method are quite popular for both simulated and real data. The methods for CT model identification have been applied in various forms such as state vector filtering (SVF), instrumental-variable (IV), generalized Poisson moment functional (GPMF), subspace state-space estimation (N4SID), and simplified refined instrumental variable method for continuous-time (SRIVC). ,,− The merits and demerits of these methods have been studied in detail by Ljung et al In the present work for identifying the data driven models, various CT model identification algorithms like SRIVC, SVF, IV, GPFM, and N4SID of CONTSID toolbox , and SYSID toolbox of MATLAB have been used.…”

Section: System Identificationmentioning

confidence: 99%

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Modeling Identification and Control of an Air Preheating Furnace of a Pneumatic Conveying and Drying Process

Satpati

Koley

Datta³

2014

Ind. Eng. Chem. Res.

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The present work uses a scale down industrial pneumatic conveying and drying system in order to develop control-oriented models and suitable robust control strategies for the air preheating furnace of the system. A better control system has been achieved by utilizing the benefits of integrating first principle models, system identification techniques and parametric robust control methods. Though these processes are widely used in drying and transmission of different food, pharmaceutical and industrial products in the form of powder like fine-grained material, but suitable control oriented thermal models for these processes have not been studied. In the work the air preheating furnace of a pneumatic conveying and drying system is initially modeled with first principles. The novel dynamic models derived from first-principles is intended to evaluate dynamic changes in outlet air temperature corresponds to changes in current input to heating coils, air flow velocity and ambient temperature. Then a continuous time (CT) data driven model identification technique based on Simplified Refined Instrumental Variable (SRIV) approach has been applied in order to identify the model parameters as per the desired structures. The identified systems were then validated with different sets of experimental data, and found to be closely correlated. Finally a novel robust control law i.e. refined particle swarm optimization (PSO) enabled automated Quantitative Feedback Theory (QFT) (refined PSO-QFT) has been proposed and implemented in order to improve the temperature control system of the pneumatic conveying and drying process.

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