Nonlinear control of pH processes using the strong acid equivalent

Wright, Raymond A.; Kravaris, Costas

doi:10.1021/ie00055a022

Cited by 134 publications

(94 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The equilibrium relationship for water is given as (3) where is the dissociation constant of water (10 -14 ).…”

Section: Dynamic Ph Process Modelmentioning

confidence: 99%

“…The associated model has been used by researchers as a platform for many subsequent investigations and forms the basis to introduce new and improved forms of dynamic modeling and pH control of neutralization process using the concept of reaction invariant and strong acid equivalent [2], [3]. Many different and practical approaches for pH control based on feedforward and gain scheduling techniques have also been proposed in the literature [4], [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural Control of Neutralization Process using Fuzzy Inference System based Lookup Table

Singh¹,

Bhanot²,

Mohanta³

2013

IJCA

View full text Add to dashboard Cite

Over a number of years, pH control of neutralization process is recognized as a benchmark for modeling and control of nonlinear processes. This paper first describes dynamic modeling of pH neutralization process. Thereafter fuzzy logic based pH control scheme for neutralization process is developed. Further, a two-dimensional (2-D) lookup table is generated based on defuzzification mechanism of fuzzy inference system (FIS). Finally, using this lookup table, a neural network control for pH neutralization process is developed. Performances of fuzzy logic based control and lookup table based neural network control for servo and regulatory operations are compared based on integral square error (ISE) and integral absolute error (IAE) criterions. Results indicate that lookup table based neural network control performs better than fuzzy logic based control. General TermsNonlinear process control, fuzzy logic control, neural network control.

show abstract

“…The equilibrium relationship for water is given as (3) where is the dissociation constant of water (10 -14 ).…”

Section: Dynamic Ph Process Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Control of Neutralization Process using Fuzzy Inference System based Lookup Table

Singh¹,

Bhanot²,

Mohanta³

2013

IJCA

View full text Add to dashboard Cite

show abstract

“…Therefore, it is difficult to develop a sound mathematical model of the pH process for designing a proper controller. Many researchers proposed control strategies based on the titration curve (see Wright and Kravaris, 1991 and references therein). Wiener models are used for controller design by Kalafatis et al (2005).…”

Section: Ph Controlmentioning

confidence: 99%

Model-Free Learning Control of Chemical Processes

Syafiie¹,

Tadeo²,

Martínez³

2008

Reinforcement Learning

View full text Add to dashboard Cite

“…Output transformations can be found in various processes such as pH neutralization 18,19 and high purity distillation. 20 The pH neutralization process dynamics are described well by the Wiener model and can be controlled through an output transformation.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear PI controllers with output transformations

Lee

Edgar

2015

AIChE Journal

View full text Add to dashboard Cite

Well-designed nonlinear proportional-integral (PI) controllers are successful for nonlinear dynamical processes like linear PI controllers are for linear processes. Two nonlinear blocks representing proportional and integral terms can be designed so that the linearized controllers perform the same as linear PI controllers for linearized processes at the given operating points. For some nonlinear processes, nonlinear blocks for nonlinear PI controllers can be singular at some operating points, and control performances can be poor for set points near those points. To mitigate such disadvantages, new nonlinear PI controllers that introduce output transformations are proposed. Several examples are given, showing the performance of the proposed nonlinear PI controllers. V C 2015 American Institute of Chemical Engineers AIChE J, 61: [4264][4265][4266][4267][4268][4269] 2015

show abstract

Nonlinear control of pH processes using the strong acid equivalent

Cited by 134 publications

References 15 publications

Neural Control of Neutralization Process using Fuzzy Inference System based Lookup Table

Neural Control of Neutralization Process using Fuzzy Inference System based Lookup Table

Model-Free Learning Control of Chemical Processes

Nonlinear PI controllers with output transformations

Contact Info

Product

Resources

About