Approximation of titration curves by artificial neural networks and its application to pH control

Scientia Iranica

Volumn 6, Issue 5, 2000, Pages 82-91

  Pishvaie, M R ^a   Shahrokhi, M ^a  

^a SHARIF UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICAL ENGINEERING; CONTROL SYSTEM; NEURAL NETWORK; PH;

EID: 0033709203     PISSN: 10263098     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (14)

1. Modeling of pH control
2. Non-linear control of pH processes using the strong acid equivalent
3. Myths about pH and pH control
4. Titration curve estimation for adaptive pH control
5. Non-linear and adaptive control of pH
6. Operating regime based process modeling and identification
7. Non-linear process identification
8. Global adaptive output-feedback control of non-linear systems: Part II: Non-linear parameterization
9. Recursive hybrid algorithm for non-linear system identification using radial basis function networks
10. Use of neural nets for dynamic modeling and control of chemical process systems
11. Approximations by superposition's of a sigmoidal function
12. Applying genetic algorithms to neural network learning
13. pH control using a simple set point change