TY  - CONF
N1  - cited By 10; Conference of 2008 International Conference on Control, Automation and Systems, ICCAS 2008 ; Conference Date: 14 October 2008 Through 17 October 2008; Conference Code:74903
KW  - Backpropagation; Electric control equipment; Functions; Hydrogen; Mathematical models; Model predictive control; Neural networks; Predictive control systems; Proportional control systems; Quality control; Three term control systems; Time varying control systems; Two term control systems
KW  -  Artificial neural network; Control strategies; Dead times; Empirical models; Empirical techniques; Hydrogen ions; Intelligent process control; Logarithmic relationships; Modeling and controls; Neutralization processes; Non linearities; PID controllers; Predictive controllers
KW  -  Process control
SP  - 1196
ID  - scholars416
TI  - Modeling and control of pH neutralization using neural network predictive controller
SN  - 9788995003893
Y1  - 2008///
EP  - 1199
A1  - Elarafi, M.G.M.K.
A1  - Hisham, S.B.
CY  - Seoul
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-58149099970&doi=10.1109%2fICCAS.2008.4694329&partnerID=40&md5=9696f1a3f7f5636da5ef918d02aed134
AV  - none
N2  - The difficulty of controlling pH neutralization processes resides in the non-linearity of such processes. This behavior is due to the logarithmic relationship between the hydrogen ions concentrations H+ and the level of pH. The control strategy to be developed very much depends on the feasibility of the mathematical model that represents the process. This paper illustrates feasible modeling of the pH neutralization plant using empirical techniques and investigates the performance of an artificial neural network predictive controller against the more traditional PID controllers. As a conclusion, a feasible empirical model was found closest to a second-order with dead time. The artificial neural network predictive controller has outperformed the conventional PI / PID controllers.
ER  -