Identification of a Pilot Scale Distillation Column: A Kernel Based Approach

Huyck, Bart; Brabanter, Kris De; Logist, Filip; Brabanter, Jos De; Impe, Jan Van; Moor, Bart De

doi:10.3182/20110828-6-it-1002.01512

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…This proposal has encouraged developing a wide range of methods where artificial neural network (ANN), fuzzy neural network (FNN), waveguides, etc. are applied in [14].…”

Section: Introductionmentioning

confidence: 99%

System identification using NARX and centrifugal compressor control through the intelligent, active method—Case study: K‐250 centrifugal compressor

Khosravi

Chatraei

Shahgholian

et al. 2022

Asian Journal of Control

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Surge and constant pressure are some of the most critical issues in compressor control. In this paper, the problem of the surge and constant pressure in the presence of environmental disturbances is solved. Proposed design for control system based on proportional-integral controllers, adaptive neuro-fuzzy inference system (FIS), and particle swarm optimized neural fuzzy and for modeling neural network strategy fuzzy nonlinear automatic regression with external input is used. Based on this, for modeling, practical and real data are extracted from the K-250 compressor of Isfahan Steel Company. In the adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO)

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“…This proposal has encouraged developing a wide range of methods where artificial neural network (ANN), fuzzy neural network (FNN), waveguides, etc. are applied in [14].…”

Section: Introductionmentioning

confidence: 99%

System identification using NARX and centrifugal compressor control through the intelligent, active method—Case study: K‐250 centrifugal compressor

Khosravi

Chatraei

Shahgholian

et al. 2022

Asian Journal of Control

View full text Add to dashboard Cite

show abstract

“…More general nonlinear system identification methods can be based on support vector machines (e.g. [15][16]), manifold learning (e.g. [17]), or neural networks (e.g.…”

Section: Introductionmentioning

confidence: 99%

Black-Box Identification of a Thermoelectric Temperature Control System for Piezoresistive Pressure Sensor-Calibration

Dickow

Graf

Feiertag

2013

Artificial Intelligence and Applications / 794: Modelling, Identification and Control / 795: Parallel and Distributed Computing

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Calibration of piezoresistive pressure sensors requires an extensive acquisition of sensor characteristics. Calibration devices have to fulfill high standards of temperature and pressure accuracy and trajectory speed. While pressure control performs quite well, state-of-the-art devices show poor temperature control performance, due to their retarding and nonlinear temperature dynamics. To overcome this problem, the standard industrial PID temperature controller can be replaced by more sophisticated control methods. The vast majority of these control methods, such as model predictive control, require a model of the device's temperature behavior. Black-box models can deliver all necessary information for model-based control applications. This paper focuses on black-box model identification of a calibration device's temperature behavior. A single-input-singleoutput (SISO) model of the nonlinear temperature dynamics is learned by a recurrent neural-network (RNN) with Long-short-term-memory (LSTM) using the resilient propagation (RPROP −) training method. The method is applied to a real calibration fixture, which uses convective cooled Peltier-elements for temperature control. The use of LSTM leads to a timeseries-reproduction error, which is more than 10 times smaller, compared to a RNN having tanh activation functions instead of LSTM.

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Black box modeling of PIDs implemented in PLCs without structural information: a support vector regression approach

Sałat

Awtoniuk

2014

Neural Comput & Applic

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In this report, the parameters identification of a proportional–integral–derivative (PID) algorithm implemented in a programmable logic controller (PLC) using support vector regression (SVR) is presented. This report focuses on a black box model of the PID with additional functions and modifications provided by the manufacturers and without information on the exact structure. The process of feature selection and its impact on the training and testing abilities are emphasized. The method was tested on a real PLC (Siemens and General Electric) with the implemented PID. The results show that the SVR maps the function of the PID algorithms and the modifications introduced by the manufacturer of the PLC with high accuracy. With this approach, the simulation results can be directly used to tune the PID algorithms in the PLC. The method is sufficiently universal in that it can be applied to any PI or PID algorithm implemented in the PLC with additional functions and modifications that were previously considered to be trade secrets. This method can also be an alternative for engineers who need to tune the PID and do not have any such information on the structure and cannot use the default settings for the known structures.

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Identification of a Pilot Scale Distillation Column: A Kernel Based Approach

Cited by 8 publications

References 7 publications

System identification using NARX and centrifugal compressor control through the intelligent, active method—Case study: K‐250 centrifugal compressor

System identification using NARX and centrifugal compressor control through the intelligent, active method—Case study: K‐250 centrifugal compressor

Black-Box Identification of a Thermoelectric Temperature Control System for Piezoresistive Pressure Sensor-Calibration

Black box modeling of PIDs implemented in PLCs without structural information: a support vector regression approach

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