Application of Bayesian Regularization Artificial Neural Network in explosion risk analysis of fixed offshore platform

Shi, Jihao; Zhu, Yan; Khan, Faisal; Chen, Guo-Ming

doi:10.1016/j.jlp.2018.10.009

Cited by 70 publications

(14 citation statements)

References 20 publications

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“…The study revealed that the use of the Bayesian regularization algorithm resulted in a good prediction of the wheat output energy which was in good agreement with the actual values. Shi et al [40] applied the Bayesian regularization algorithm in the ANN modeling of explosion risk analysis of a fixed offshore platform. The Bayesian regulation-trained ANN accurately predicted the cumulative frequency of the maximum overpressure.…”

Section: The Ann Model Predictive Analysismentioning

confidence: 99%

“…The Bayesian regularization training algorithm has the tendency to minimize the estimated errors through an inbuilt objective function that contains a residual sum of squares and the sum of squared weighs [40]. Hence, it is typical to obtain a good generalization model using the Bayesian regularization training algorithm [38].…”

Section: Network Training Testing and Validationmentioning

confidence: 99%

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Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming

et al. 2019

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This study investigates the applicability of the Leven-Marquardt algorithm, Bayesian regularization, and a scaled conjugate gradient algorithm as training algorithms for an artificial neural network (ANN) predictively modeling the rate of CO and H 2 production by methane dry reforming over a Co/Pr 2 O 3 catalyst. The dataset employed for the ANN modeling was obtained using a central composite experimental design. The input parameters consisted of CH 4 partial pressure, CO 2 partial pressure, and reaction temperature, while the target parameters included the rate of CO and H 2 production. A neural network architecture of 3 13 2, 3 15 2, and 3 15 2 representing the input layer, hidden neuron layer, and target (output) layer were employed for the Leven-Marquardt, Bayesian regularization, and scaled conjugate gradient training algorithms, respectively. The ANN training with each of the algorithms resulted in an accurate prediction of the rate of CO and H 2 production. The best prediction was, however, obtained using the Bayesian regularization algorithm with the lowest standard error of estimates (SEE). The high values of coefficient of determination (R 2 > 0.9) obtained from the parity plots are an indication that the predicted rates of CO and H 2 production were strongly correlated with the observed values.To overcome these challenges, several supported metal-based catalysts have been developed and tested. An extensive review by Abdullah et al. [10] revealed that supported nickel (Ni) catalysts have been mostly investigated for methane dry reforming due to its high catalytic performance. Nevertheless, the Ni-based catalysts are very prone to sintering and carbon deposition [11]. On the other hand, cobalt (Co)-based catalysts which have a comparative activity to Ni have been reported to show superior stability compare to Ni under the same process condition [12,13]. In our previous studies, the use of rare-earth metal oxide-supported Co catalysts for CO-rich hydrogen production showed considerable activity and stability [14][15][16]. However, one major challenge is understanding the kinetics of the methane dry reforming in terms of the rate of H 2 and CO production due to variations in the chemical composition of the various catalysts [17]. This challenge can be overcome by employing an artificial intelligence modeling approach for a better understanding of the process parameters [18,19]. Processes with non-linear and complex relationships between the input and the output parameters are often encountered in real life processes. The better understanding of the non-linear relationship between the input and the output parameters of the process can further be utilized to optimize the process operation and create the basis for the theoretical framework, process automation, and upscaling [20].An artificial intelligence modeling approach using an artificial neural network (ANN) has been widely employed for different catalytic processes, such as hydrodesulfurization [20], methanol steam reforming, glycerol steam reforming [2...

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Section: The Ann Model Predictive Analysismentioning

confidence: 99%

Section: Network Training Testing and Validationmentioning

confidence: 99%

Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming

et al. 2019

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“…Additionally, uncertainty estimation is important in applications like autonomous driving, and medical diagnosis, where machine learning must be complemented with uncertainty-aware models or human intervention [7,8]. The integration of probabilistic computing paradigms with ANNs allows regularization and enables us to model uncertainty in predictions [9][10][11][12]. This is achieved in Bayesian neural networks (BNNs) by incorporating Bayes theorem to the traditional neural network scheme [12,13].…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Accelerator to Avoid Inference Inaccuracy

Sebastian

Das

2022

Preprint

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Artificial neural networks (ANNs) have demonstrated their superiority over traditional computing architectures in tasks such as pattern classification and learning. While ANNs demonstrate high prediction accuracy, they do not measure uncertainty in predictions, and hence they can make wrong predictions with high confidence, which can be detrimental for many mission-critical applications. In contrast, Bayesian neural networks (BNNs) naturally include such uncertainty in their model. Unlike ANNs, where the synaptic weights are point estimates (single-valued), in BNNs, the weights are represented by probability distributions (e.g. Gaussian distribution). This makes the hardware implementation of BNNs challenging since on-chip Gaussian random number generators (GRNGs) based on silicon complementary metal oxide semiconductor (CMOS) technology are area and energy inefficient. Stochastic switching in memristors can be used to build probabilistic synapses but with very limited tunability. Additionally, memristor technology rely heavily on CMOS-based peripherals to emulate neurons. Here we introduce three-terminal memtransistors based on two-dimensional (2D) materials, which can emulate both probabilistic synapses as well as reconfigurable neurons. The cycle-to-cycle variation in the programming of the 2D memtransistor is exploited to achieve GRNG-based synapses, whereas 2D memtransistor based integrated circuits are used to obtain neurons with hyperbolic tangent and sigmoid activation functions. Finally, memtransistor-based synapses and neurons are combined in a crossbar array architecture to realize a BNN accelerator and the performance is evaluated using the IRIS data classification task.

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“…ey stated that the developed method is appropriate for evaluating an explosion [19]. ey provided a method for analyzing urban hydrogen refueling station risk using BNs.…”

Section: Introductionmentioning

confidence: 99%

Risk Analysis and Reliability Assessment of Overhead Cranes Using Fault Tree Analysis Integrated with Markov Chain and Fuzzy Bayesian Networks

Mohammadi

Fazli

Kaleh

et al. 2021

Mathematical Problems in Engineering

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Establishing an adequate level of reliability in the overhead crane operations is an important and vital principle to avoid undesirable consequences. To do this, it is appropriate to have a comprehensive approach for risk and reliability assessment of the most probable failure scenarios during overhead crane operations. In this study, fault tree analysis (FTA) in combination with fuzzy set theory, Bayesian network (BN), and Markov chain was used to evaluate the probability of top event and reliability of overhead cranes. A total of 47 basic events were identified for ladle fall in overhead cranes. The results showed that the probability of the ladle fall in the FT approach is equal to 0.0523035 and in the BN approach in the prior event is equal to 0.0273394 which is less than the FT method. Based on the values predicted by Markov chain, the reliability of the system decreases over time by 67.9% after 60 months. This study showed that the plan for ladle fall prevention should consider all influencing parameters identified by proper risk assessment methodologies.

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Application of Bayesian Regularization Artificial Neural Network in explosion risk analysis of fixed offshore platform

Cited by 70 publications

References 20 publications

Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming

Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming

A Neural Network Accelerator to Avoid Inference Inaccuracy

Risk Analysis and Reliability Assessment of Overhead Cranes Using Fault Tree Analysis Integrated with Markov Chain and Fuzzy Bayesian Networks

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