TY - JOUR SN - 18650929 PB - Springer Verlag EP - 311 AV - none TI - Artificial neural network for anomalies detection in distillation column SP - 302 N1 - cited By 15; Conference of 17th International Conference on Asia Simulation, AsiaSim 2017 ; Conference Date: 27 August 2017 Through 29 August 2017; Conference Code:197069 Y1 - 2017/// VL - 751 UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028959028&doi=10.1007%2f978-981-10-6463-0_26&partnerID=40&md5=980c6b5bb27fd10ef7e69935e938799c A1 - Taqvi, S.A. A1 - Tufa, L.D. A1 - Zabiri, H. A1 - Mahadzir, S. A1 - Shah Maulud, A. A1 - Uddin, F. JF - Communications in Computer and Information Science KW - Backpropagation; Backpropagation algorithms; Computer software; Damage detection; Fault detection; Mean square error; Neural networks KW - ASPEN PLUS; Bottom temperature; Ethanol-water system; Industrial problem; Pilot scale distillation; Process Variables; Product degradation; Root mean square errors KW - Distillation columns ID - scholars9201 N2 - Early detection of anomalies can assist to avoid major losses in term of product degradation, machines�?? damages as well as human health issues. This research aims to use Artificial Neural Network to recognize anomalies in the distillation column. The pilot scale distillation column for the ethanol-water system is selected for the study. Faults are generated by variation in feed rate, feed composition and reboiler duty using Aspen Plus® dynamic simulation. The effect of these faults on process variables i.e. changes in distillate and bottom composition, distillate and bottom temperature, bottom flow rate, and the pressure drop is observed. The network is trained using back propagation algorithm to determine root mean square error (RMSE). Based on RMSE minimization, the (6-8-6) net serves as the best choice for the case studied for efficient fault detection. The presented techniques are general in nature and easily applicable to various other industrial problems. © Springer Nature Singapore Pte Ltd. 2017. ER -