An Approach to Robust and Flexible Modelling and Control of pH in Reactors

• Published in: Chemical engineering research & design Vol. 79; no. 3; pp. 323 - 334
• Main Authors: Mwembeshi, M.M., Kent, C.A., Salhi, S.
• Format: Journal Article
• Language: English
• Published: Rugby Elsevier B.V 01.04.2001; Institution of Chemical Engineers
• Subjects: Applied sciences; Chemical engineering; Exact sciences and technology; heuristics; modelling; neural networks; neutralization; Reactors; pH; heuristics; modelling; neutralization; neural networks; Phosphoric acid; Process control; Continuous stirred tank reactor; Modeling; Sulfuric acid; Hydrochloric acid; Time varying system; Feedforward neural nets; Sodium Hydroxides; Non linear model; Acetic acid; Dynamic model; Neutralization
• ISSN: 0263-8762
• DOI: 10.1205/026387601750281833

Preliminary investigations into the potential application of static feed forward neural networks in the dynamic modelling of pH in complex, time-varying systems have been carried out. To assist in network training and testing, a simplified, ‘global first principles’ (FP) model of the pH of such systems was developed, and used successfully to simulate input-output data. Neural networks with input information vectors enhanced by the introduction of auxiliary variables derived from acid-base principles were trained and tested on this data, using both Levenberg-Marquardt (L-M) and heuristic training algorithms. Both algorithms produced good predictions, but the heuristic algorithm required data pre-treatment to minimize its error. However, it trained much faster than the standard, L-M algorithm.