Use of Neural Network for Modeling of Liquid-Liquid Extraction Process in The RDC Column

摘要: Beberapa Model Matematik telah dibentuk bagi proses yang melibatkan Turus Pengekstrakan Cakera Berputar (RDC). Model-model ini menunjukkan bahawa hidrodinamik dan peralihan jisim adalah faktor-faktor penting keberkesanan turus. Biasanya, model simulasi matematik menerangkan berlaku di dalam turus terlalu rumit. Ianya juga memerlukan masa pengkomputeran lebih menghasilkan data untuk tujuan analisis lanjut. Oleh itu, satu pendekatan alternatif berdasarkan rangkaian neural dipertimbangkan cepat. Kertas menbincangkan aplikasi baru teknik Rangkaian Artificial Neural memodelkan pengekstrakan cecair RDC. Dalam penyelidikan juga, ANN dilatih menggunakan dari Arshad(2000). mampu 128 RDC dengan nilai ralat RMS 1.0E-07. Bandingan antara output Matematik(2000) ditunjukkan sini. Katakunci: Peralihan jisim; Berputar; Rambatan Balik Several Mathematical Models have been developed for processes involving Rotating Disc Contactor (RDC) Column. These models indicated that the hydrodynamic and mass transfer are important factors column performances. Usually, mathematical simulation describing in very complex. It also needs excessive computer time to produce further analysis. Therefore, an alternative approach based on Network is considered assist speeding up process. This paper presents a new application of (ANN) techniques modeling liquid-liquid extraction process In this work, was trained with simulated obtained from Arshad (2000). The able generate error value comparison between Model(2000) presented. Keywords: Mass Transfer; Column; Back-propagation