Syed A. Taqvi

Artificial Neural Network for Anomalies Detection in Distillation Column

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.

Fault detection in distillation column using NARX neural network

Fault detection in the process industries is one of the most challenging tasks. It requires timely detection of anomalies which are present with noisy measurements of a large number of variable, highly correlated data with complex interactions and fault symptoms. This study proposes the robust fault detection method for the distillation column. Fault detection and diagnosis (FDD) for process monitoring and control has been an effective field of research for two decades. This area has been used widely in sophisticated engineering design applications to ensure the proper functionality and performance diagnosis of advanced and complex technologies. Robust fault detection of the realistic faults in distillation column in dynamic condition has been considered in this study. For early detection of faults, the model is based on nonlinear autoregressive with exogenous input (NARX) network. Tapped delays lines (TDLs) have been used for the input and output sequences. A case study was carried out with three different fault scenarios, i.e., valve sticking at reflux and reboiler, and tray upset. These faults would cause the product degradation. The normal data (no fault) is used for the training of neural network in all three cases. It is shown that the proposed algorithm can be used for the detection of both internal and external faults in the distillation column for dynamic system monitoring and to predict the probability of failure.

Aspen Plusî Simulation of a Coal Gasification Process (Geometric Analysis)

A semi-batch foam-flotation in which air is continuously sparged through an emulsion, with added surfactant, a coagulant, and a solvent, has been shown to be effective in the treatment of steel-rolling mill effluents. The effect of time of flotation, effects of surfactant and alum concentrations, and effect of the solvent volume were all experimentally explored. The oil recovery increased with concentrations of alum and sodium lauryl sulphate of up to around 4 g/l, and then leveled off. Volume of the solvent layer at the top improved the separation of oil with an optimum ratio of 0.167 ml solvent per ml of emulsion. The oil separation was highest for the time of flotation of about 25 minutes, and reemulsification of the separated self-emulsifiable oil was observed beyond this time. A model reported in the literature for the semi-batch flotation has been shown to be inadequate in predicting the experimental data on separation of oil. A mathematical model developed for the separation by foam flotation based on an analogy with a chemical reaction was found to be appreciably better in its predictive capability than the one reported in literature. The new mathematical model has established the separation of oil by foam flotation as a second-order process, and its predictions can be further fine tuned using a parameter referred to as a sticking coefficient (β). The values of β for the two effluents investigated were equal to 7.9 × 10-5 and 6.7 × 10-5, respectively.

Realizing the Value of Big Data in Process Monitoring and Control: Current Issues and Opportunities

With the advancement of Big Data, data analytics have benefited many organizations and industries. The continuous improvement of process industries is a challenging task that requires insights for efficient process monitoring with minimum downtime and safer operations. These industries generate a large amount of data i.e. “Big Data” every second that contains useful information. For better control, different analysis can be executed using statistical methods, data mining or machine learning. This data can be used for daily process operations and decision making. However, the past literature on Big Data have put limited focus on realizing its implication in process monitoring and control. The paper emphasizes on the issues and opportunities faced by process industries in terms of Big Data collection, storage and analysis for process monitoring and control.

Aspen Plus® Simulation Studies of Steam Gasification in Fluidized Bed Reactor for Hydrogen Production Using Palm Kernel Shell

In this paper, a steady state simulation for hydrogen production from steam gasification of Palm kernel shell was developed and studied. The gasification pilot plant process has been modelled in Aspen Plus® using Gibbs reactor (R-Gibbs). The effects of different operating parameters using sensitivity analysis, including gasification temperature 600–900 °C and steam flow rate (1 to 2 kg/hr.), on hydrogen yields and Syngas composition were investigated. The simulation results have shown the main gas components in Synthesis gas were H2, CO, CO2, CH4. The product gas hydrogen yield increases with the increase in temperature. The hydrogen concentration improved from 22.52 vol. % to 36.06 vol.%, but the CO concentration decreased from 37.53 vol.% to 28.37% with increasing temperature from 650–900 °C under the operating parameters of the steam flow rate of 1.56 kg/hr.