Experimental simultaneous state and parameter identification of a pH neutralization process based on an extended Kalman Filter

Yoo, Ahrim; Lee, Tae Chul; Yang, Dae Ryook

doi:10.1007/bf02705516

Cited by 10 publications

(6 citation statements)

References 21 publications

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“…The experiment is separated in two ways, the pH control without load change and disturbances absent, the second test is pH control with load (disturbance) change. All the simulation was compared with PID scheme only [11].…”

Section: Resultsmentioning

confidence: 99%

“…Petrokimia Gresik [11]. It has effluent waste process unit to neutralize of the waste product before flowing into river.…”

Section: The Nonlinear Ph Neutralization Processmentioning

confidence: 99%

“…Based on the electroneutrality condition, the sum of the ionic charge in the solution must be zero. Therefore we have, (11) Actually, the pH value is not defined as a hydrogen ion concentration but as ion activity. However, it can be assumed that the ion activity is same as the ion concentration for the infinitely diluted solution.…”

Section: The Nonlinear Ph Neutralization Processmentioning

confidence: 99%

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Self-Tuning Pid Neural Network Controller to Control Nonlinear Ph Neutralization in Waste Water Treatment

Cordova

Wijaya

2007

JTS

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89AbstractThe conventional PID control (linear) is popular control scheme that is used in almost pH control at waste water treatment process. pH model with respect to titration liquid flow rate has been known to be intrinsically difficult and nonlinear, especially when the process is conducted to non-linear range pH reference (pH 4-8), the settling time reach a long time. Therefore in this paper the nonlinear controller with self-tuning PID scheme is performed handle pH by training a neural-network based on backpropagation error signals. The pH process model is combination between CSTR (Continuous Stirred Tank Reactor) linear dynamic with H 3 PO 4 , HF, HCl, H 2 S flow rate and nonlinear static electro neutrality (wiener process model). The simulation result has a good and suitable performance under several tests (set-point and load change). The simulation result show the pH control system can follow the change of pH set-point and load with the average steady state error 0.00034. The settling time achieved at 50 second faster than conventional PID controller scheme.

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Section: Resultsmentioning

confidence: 99%

“…Petrokimia Gresik [11]. It has effluent waste process unit to neutralize of the waste product before flowing into river.…”

Section: The Nonlinear Ph Neutralization Processmentioning

confidence: 99%

Section: The Nonlinear Ph Neutralization Processmentioning

confidence: 99%

See 1 more Smart Citation

Self-Tuning Pid Neural Network Controller to Control Nonlinear Ph Neutralization in Waste Water Treatment

Cordova

Wijaya

2007

JTS

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“…As it is shown by different authors, Kalman filtering can detect and remove noise from measurements and states [13][14][15] and is a specific case of DR [12] and can be used for DDR. To illustrate the suitability of DDRMethod class, Kalman filtering (KF) and extended Kalman filtering (EKF) methods were classified in a class, namely DDRByKalman, and inherited methods and attributes of DDRMethod class.…”

Section: Kalman Filtering Applied As a Class For Dynamic Data Reconcimentioning

confidence: 99%

Data reconciliation: Development of an object-oriented software tool

Farzi

Mehrabani-Zeinabad

Boozarjomehry

2008

Korean J. Chem. Eng.

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Object-oriented modeling methodology is used for encapsulating different methods and attributes of data reconciliation (DR) in classes. Classes which are defined for DR, cover steady-state, dynamic, linear and nonlinear DR problems. Two main classes are Constraints and DR and defined for manipulating constraints and general DR problem. The remaining classes are derived from these two classes. A class namely DDRMethod is developed for encapsulating all common attributes and methods needed for any DDR method. Developed DR software and the method of performing dynamic DR are discussed in this paper. Two illustrative examples of Extended Kalman Filtering and artificial neural networks are used for DDR and two classes of DDRByKalman and NetDDRMethod developed by inheritance from DDRMethod class for these two methods. Performance of the proposed method is investigated by DDR of temperature measurements of a distillation column.

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“…It is well known that the extended Kalman filter (EKF) can be used [15] for the joint estimation of the systems described by (8), which can be summarized as the following formulas.…”

Section: Basic Principals Of Stfmentioning

confidence: 99%

Model-Based Water Wall Fault Detection and Diagnosis of FBC Boiler Using Strong Tracking Filter

Sun

Jiang

et al. 2014

Mathematical Problems in Engineering

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Fluidized bed combustion (FBC) boilers have received increasing attention in recent decades. The erosion issue on the water wall is one of the most common and serious faults for FBC boilers. Unlike direct measurement of tube thickness used by ultrasonic methods, the wastage of water wall is reconsidered equally as the variation of the overall heat transfer coefficient in the furnace. In this paper, a model-based approach is presented to estimate internal states and heat transfer coefficient dually from the noisy measurable outputs. The estimated parameter is compared with the normal value. Then the modified Bayesian algorithm is adopted for fault detection and diagnosis (FDD). The simulation results demonstrate that the approach is feasible and effective.

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Experimental simultaneous state and parameter identification of a pH neutralization process based on an extended Kalman Filter

Cited by 10 publications

References 21 publications

Self-Tuning Pid Neural Network Controller to Control Nonlinear Ph Neutralization in Waste Water Treatment

Self-Tuning Pid Neural Network Controller to Control Nonlinear Ph Neutralization in Waste Water Treatment

Data reconciliation: Development of an object-oriented software tool

Model-Based Water Wall Fault Detection and Diagnosis of FBC Boiler Using Strong Tracking Filter

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