System Identification I

This chapter contains sections titled: Historical Review Supervised Multilayer Networks Unsupervised Neural Networks: Kohonen Network Unsupervised Networks: Adaptive Resonance Theory Network Model Validation Summary References Recommended Exercises

Nonlinear System Identification

Synergistic effect on co-pyrolysis of rice husk and sewage sludge by thermal behavior, kinetics, thermodynamic parameters and artificial neural network.

Artificial neural network approach for the steam gasification of palm oil waste using bottom ash and CaO

The epidemic growth of the pharmaceuticals industries over the years in order to meet the human demands had exerted substantial pressure on the global environment, particularly water pollutions crisis. Herein, the photocatalytic degradation of phenol was investigated via commercial TiO2 nanoparticles. The Artificial Neural Network (ANN) and Response Surface Methodology (RSM) was employed for scrutinizing the suitable modelling and optimized condition of the TiO2 nanoparticles in yielding a profound rate of phenol removal. The parameters of investigation involved pH, phenol concentration, catalyst doses and degradation time. The RSM data shows the profound rate of phenol removal ˜ 99.48% was achieved by TiO2 NPs in a designed photocatalytic system that set at 5.42 pH, 15.21 mg/L phenol concentration, 1.75 g/L TiO2 dosage and 540 min irradiation time. The designed system fits well with the Pseudo-First-Order and the Langmuir isotherm model with R2 > 0.999. On the other hand, the ANN study revealed that the predicated model was perfectly fitted with the experimental data giving the highest value of R2. This work provides an insight into two different statistical modelling and optimization which could provide exposure for developing an optimized nanomaterial towards the removal of hazardous pollutant.

Modelling and optimization of photocatalytic degradation of phenol via TiO2 nanoparticles: An insight into response surface methodology and artificial neural network

The use of artificial neural networks (ANN) in fault detection analysis is widespread. This paper aims to provide an overview on its application in the field of fault identification and diagnosis (FID), as well as the guiding elements behind their successful implementations in engineering-related applications. In most of the reviewed studies, the ANN architecture of choice for FID problem-solving is the multilayer perceptron (MLP). This is likely due to its simplicity, flexibility, and established usage. Its use managed to find footing in a variety of fields in engineering very early on, even before the technology was as polished as it is today. Recurrent neural networks, while having overall stronger potential for solving dynamic problems, are only suggested for use after a simpler implementation in MLP was attempted. Across various ANN applications in FID, it is observed that preprocessing of the inputs is extremely important in obtaining the proper features for use in training the network, particularly when signal analysis is involved. Normalization is practically a standard for ANN use, and likely many other decision-based learning methods due to its ease of use and high impact on speed of convergence. A simple demonstration of ANN’s ease of use in solving a unique FID problem was also shown.

Neural network applications in fault diagnosis and detection: an overview of implementations in engineering-related systems

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.

Fault detection in distillation column using NARX neural network

https://cyberleninka.org/article/n/1417632.pdf

Optimization and Dynamics of Distillation Column Using Aspen Plus

A Review on Data-Driven Learning Approaches for Fault Detection and Diagnosis in Chemical Processes

Fault detection and diagnosis (FDD) in process industries is an important task for efficient process monitoring and plant safety. It is also essential for improving product quality and reducing pro...

Lemma Dendena Tufa

Papers

Neural network applications in fault diagnosis and detection: an overview of implementations in engineering-related systems

Fault detection in distillation column using NARX neural network

Optimization and Dynamics of Distillation Column Using Aspen Plus

A Review on Data-Driven Learning Approaches for Fault Detection and Diagnosis in Chemical Processes

Multiple Fault Diagnosis in Distillation Column Using Multikernel Support Vector Machine