Lemma Dendena Tufa

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.

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.

Iterative closed-loop identification of MIMO systems using ARX-based Leaky Least Mean Square Algorithm

Closed-loop identification of MIMO systems is considered. An iterative Leaky Least Mean Squares (LLMS) algorithm is proposed for the development of ARX structure. The performance of the proposed algorithm with respect to the existing recursive algorithms is investigated in a simulation study. The simulation results show that the proposed algorithm can produce more accurate parameter estimates than the conventional recursive algorithms.

A kinetic-based simulation model of palm kernel shell steam gasification in a circulating fluidized bed using Aspen Plus®: A case study

ABSTRACT A detailed simulation model for hydrogen production using catalytic steam gasification of palm kernel shell in an atmospheric dual fluidized bed gasifier using an Aspen Plus® simulator is developed. The catalytic adsorbent-based steam gasification of palm kernel shell is studied in a pilot scale dual fluidized bed reactor using coal bottom ash as a catalyst for hydrogen and syngas production. The use of a catalyst along with the adsorbent improved tar cracking and enhanced the hydrogen content of syngas. The effect of temperature and the steam–biomass ratio on hydrogen yield, syngas composition and lower and higher heating values was studied. An increase in steam–biomass ratio enhanced the hydrogen content from 60 to 72 mol%%. The maximum value of hydrogen production, i.e. 72 vol% was achieved at a steam–biomass ratio of 1.7. The use of adsorbent and coal bottom ash had a significant effect on hydrogen and syngas yield. A maximum of 80.1 vol% hydrogen was achieved at a temperature of 650 °C with a 1.25 steam–biomass ratio and 0.07 wt% coal bottom ash.

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.

System Identification Based Proxy Model of a Reservoir under Water Injection

Simulation of numerical reservoir models with thousands and millions of grid blocks may consume a significant amount of time and effort, even when high performance processors are used. In cases where the simulation runs are required for sensitivity analysis, dynamic control, and optimization, the act needs to be repeated several times by continuously changing parameters. This makes it even more time-consuming. Currently, proxy models that are based on response surface are being used to lessen the time required for running simulations during sensitivity analysis and optimization. Proxy models are lighter mathematical models that run faster and perform in place of heavier models that require large computations. Nevertheless, to acquire data for modeling and validation and develop the proxy model itself, hundreds of simulation runs are required. In this paper, a system identification based proxy model that requires only a single simulation run and a properly designed excitation signal was proposed and evaluated using a benchmark case study. The results show that, with proper design of excitation signal and proper selection of model structure, system identification based proxy models are found to be practical and efficient alternatives for mimicking the performance of numerical reservoir models. The resulting proxy models have potential applications for dynamic well control and optimization.

Isolation of Interacting Channels in Decentralized Control Systems Using Instrumental Variables Method

This paper describes the use of partial correlation based instrumental variables method for the identification and isolation of weak interaction dynamics between subsystems in decentralized control systems. Unlike the available methods based on the ordinary least square, the proposed method clearly discriminates the interaction channels that have significant contribution to the interconnected subsystem from the ones which do not by reducing the model error that arises due to the process inputs correlation. The efficacy of the proposed method is illustrated through a case study.

Detection of Model Parameter Mismatch Using Simplified Partial Correlation Analysis for Closed-Loop System

Partial correlation analysis is used in detecting the model-plant mismatch as it will give accurate location of mismatched submodel. In this work of model parameter mismatch detection in closed-loop system, a simplified method of partial correlation analysis is proposed. In this method, the identification step for input sensitivities relating setpoints and manipulated variables, Sru, is omitted due the ability of ARX model structure to capture the dynamic of the input-output data even though in the presence of unmeasured disturbance in closed-loop system. The ARX model structure is implemented in decorrelating the observed data from the correlated inputs. By using the ARX model, the mismatch is detected at the precise location compared to the detection using FIR decorrelation model.

A Comparison Study between Integrated OBFARX-NN and OBF-NN for Modeling of Nonlinear Systems in Extended Regions of Operation

In this paper the combination of linear and nonlinear models in parallel for nonlinear system identification is investigated. A residuals-based sequential identification algorithm using parallel integration of linear Orthornormal basis filters-Auto regressive with exogenous input (OBFARX) and a nonlinear neural network (NN) models is developed. The model performance is then compared against previously developed parallel OBF-NN model in a nonlinear CSTR case study in extended regions of operation (i.e. extrapolation capability).

Robust and Effective PID Controller Identification for Delay Dominant Systems

A novel PID controller identification method based on internal model control structure is proposed. The proposed method avoids the necessity of approximating the time delay for designing the PID controller. It results in a robust and effective PID controller tuning. The method is effective for both time constant and time delay dominant systems, with much improved performance for the latter case.