This paper presents an artificial intelligence based process modeling and optimization strategies, namely artificial neural network – differential evolution (ANN-DE) for modeling and optimization of ultraviolet (UV) transmittance of mono ethylene glycol (MEG) product. UV transmittance is one of the most important quality variable of MEG that has impact on the polyester product quality. UV transmittance measures the presence of undesirable compounds in MEG that absorb light in the ultraviolet region of the spectrum and indirectly measures the purity of MEG product. They are in trace quantities in the ppb ranges and primarily unknown in chemical structure. Thus, they cannot be measured directly. Off-line laboratory method for MEG UV measurement is common practice among the manufacturer, where a sample is withdrawn several times a day from the product stream and analyzed by time consuming laboratory analysis. In the event of a process malfunction or operating under suboptimal condition, the plant continues to produce off-spec product until lab results become available. It results in enormous financial losses for a large scale commercial plant. In the present paper a soft sensor was developed to predict the UV transmittance on real time basis and an online hybrid ANN-DE technique was used to optimize the process parameters so that UV is maximized. This paper describes a systematic approach to the development of inferential measurements of UV transmittance using ANN regression analysis. After predicting the UV accurately, model inputs are optimized using DEs to maximize the UV. The optimized solutions when verified in actual commercial plant resulted in a significant improvement in the MEG quality.
- modeling & optimization
Available at: http://works.bepress.com/sandip_lahiri/29/