Title :
pH process modeling using neural network
Author :
Wei, Liejiang ; Qiang, Yan ; Li, Shaonian ; Yue, Dalin ; Yang, Shuntai
Author_Institution :
Sch. of Energy & Power Eng., Lanzhou Univ. of Tech, Lanzhou, China
Abstract :
The method of modeling a kind of highly nonlinear process using neural network is presented, in which a CSTR (Continuous Stired Tank Reactor) system involved a strong acid strong base react is studied as a typical case of highly nonlinear process, and convergence problem is discessed preliminarily during neural network modeling . It has been pointed out that the three layered BP neural network has a good ability to model the highly nonlinear process of pH.
Keywords :
backpropagation; chemical engineering computing; chemical reactors; neural nets; pH; BP neural network; CSTR; continuous stirred tank reactor; convergence problem; nonlinear process; pH process modeling; strong acid strong base react; Artificial neural networks; Chemical processes; Chemical reactors; Computational modeling; Computers; Process control; USA Councils; BP algorithm; highly nonlinear; modeling; neural network; pH process;
Conference_Titel :
Mechanic Automation and Control Engineering (MACE), 2011 Second International Conference on
Conference_Location :
Hohhot
Print_ISBN :
978-1-4244-9436-1
DOI :
10.1109/MACE.2011.5987694
