Comparison of melt flow index of propylene polymerisation in loop reactors using first principles and artificial neural network models

• Main Authors: Jumari, N. F., Mohd. Yusof, K.
• Format: Article
• Published: Italian Association of Chemical Engineering - AIDIC 2017
• Subjects: TP Chemical technology
• Online Access: http://eprints.utm.my/id/eprint/75526/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019401608&doi=10.3303%2fCET1756028&partnerID=40&md5=12d97cc48301665d0dc96df1dc744577
• Institution: Universiti Teknologi Malaysia

The inability to measure product quality in polymerisation industries on-line causes major difficulties. There are no on-line instruments to measure resin characteristics that define polymer quality, such as melt flow index (MFI) and density. MFI always often have to be evaluated in a time consuming and manpower intensive lab analysis. In most plants, MFI is measured only several times a day using a manual analytical test. An on-line MFI measurement is essential in fulfilling customer requirements and preventing losses. This paper presents models for soft sensors to measure MFI in industrial polypropylene loop reactors using first principle (FP) model and artificial neural network (ANN) model. For the FP model, two industrial interconnected loop reactors for propylene polymerisation are modelled as two continuous stirred tank reactors (CSTRs) in series. The mathematical models of nonlinear differential equations which describe the polymerisation process were solved numerically. The ANN model of the two loop reactors are developed by employing the concept of Feed- Forward Back Propagation (FFBP) network architecture using Levenberg-Marquardt training method. The ANN model act as estimator to predict the polymer MFI. Both models are developed and simulated in MATLAB. The simulation results of the MFI between FPM and ANN model are compared and analysed. The prediction of the ANN model is found to be more accurate compare to the MFI calculated by the FP model. The ANN model prediction is good within the range of training data. The CPU time recorded that ANN model is much faster than FP model.