Analysis of artificial neural network in prediction of circulation rate for a natural circulation vertical thermosiphon reboiler

Hakeem, M.A.; Kamil, Mohammad

doi:10.1016/j.applthermaleng.2016.10.119

Cited by 22 publications

(8 citation statements)

References 16 publications

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“…The detection methods employed multiple sensors, namely, a tactile sensor (a vibration sensor) and an optical sensor (a CCD camera). Three learning classifiers, based on Bayes classifier (BC) [11], artificial neural network (ANN) [12], and -nearest neighbor (KNN) [13] methods were compared with the proposed method. Similarly, 100 data sets and 80 images were randomly selected as training samples, and the other 200 sets and 200 images were used as validation samples for evaluating the performance of the detection methods.…”

Section: General Classification Results Obtained Using the Pca/ Svm-bmentioning

confidence: 99%

PCA/SVM-Based Method for Pattern Detection in a Multisensor System

Lin

2018

Mathematical Problems in Engineering

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This paper presents a multivariate analysis framework for pattern detection in a multisensor system; the proposed principal component analysis (PCA)/support vector machine-(SVM-) based supervision scheme can identify patterns in the multisensory system. Although the PCA and SVM are commonly used in pattern recognition, an effective methodology using the PCA/SVM for multisensory system remains unexplored. Pattern detection in a multisensor system has long been a challenge. For example, object inspections in multisensor systems are difficult to perform because inspectors might fail to use multiple sensing devices when concurrently detecting different patterns. Therefore, to resolve this issue, this study proposes a novel framework for establishing indicators and corresponding thresholds to identify patterns in the system; it employs a feature-based scheme that integrates principal component analysis (PCA) with an SVM for effectively detecting patterns in the system. Experiments were conducted using a tactile and optical measurement system. The experimental results demonstrated that the proposed method can effectively identify patterns in multisensor systems by using a feature-based algorithm that combines PCA and SVM classification for detecting various patterns. Moreover, the proposed framework established alarm indicators and corresponding thresholds that can be used for pattern detection.

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Section: General Classification Results Obtained Using the Pca/ Svm-bmentioning

confidence: 99%

PCA/SVM-Based Method for Pattern Detection in a Multisensor System

Lin

2018

Mathematical Problems in Engineering

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“…For short-term price forecasting, the BP neural network can realize complex highly nonlinear mapping, but it has slow convergence speed and low prediction accuracy [28][29][30]. In these papers, the FC analysis is used to select the learning samples to find out the prediction categories similar to the forecasting date as the input samples of the neural network.…”

Section: Short-term Price Forecast Based On Fc and Ga-bp Neural Networkmentioning

confidence: 99%

Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game

et al. 2018

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The openness of the electricity retail market results in the power retailers facing fierce competition in the market. This article aims to analyze the electricity purchase optimization decision-making of each power retailer with the background of the big data era. First, in order to guide the power retailer to make a purchase of electricity, this paper considers the users' historical electricity consumption data and a comprehensive consideration of multiple factors, then uses the wavelet neural network (WNN) model based on "meteorological similarity day (MSD)" to forecast the user load demand. Second, in order to guide the quotation of the power retailer, this paper considers the multiple factors affecting the electricity price to cluster the sample set, and establishes a Genetic algorithm-back propagation (GA-BP) neural network model based on fuzzy clustering (FC) to predict the short-term market clearing price (MCP). Thirdly, based on Sealed-bid Auction (SA) in game theory, a Bayesian Game Model (BGM) of the power retailer's bidding strategy is constructed, and the optimal bidding strategy is obtained by obtaining the Bayesian Nash Equilibrium (BNE) under different probability distributions. Finally, a practical example is proposed to prove that the model and method can provide an effective reference for the decision-making optimization of the sales company.In 2014 alone, the power trade with EEX reached an astonishing 1952 tWh. The Pennsylvania New Jersey Maryland (PJM) exchange in the USA is currently responsible for the operation and management of power systems in 13 U.S. states and the District of Columbia. As a regional Independent system operation (ISO), PJM is responsible for centrally dispatching the largest and most complex power control area in the USA and ranks third in the world in scale. The PJM controlled area accounts for 8.7% of the total population (about 23 million), load 7.5%, installed capacity of 8% (about 58,698 MW), and transmission lines up to more than 12,800 kilometers [4]. The effective application of power big data for the profitability and control of power companies has a high value. Some experts have said that whenever the data utilization increases by 10%, it can cause the power grid to increase profits by 20% to 49%. In the face of a huge power system, data needs to be processed quickly, hence data mining technology came into being, and will play a key role in supporting the sales company to participate in market competition.Power-selling is the pillar business of the retailer. As an intermediate link between production and use, the power retailer needs to analyze the users' needs and predict the user's load demand based on the users' historical electricity consumption data, considering industry, meteorology, regional economy, related industries, and other factors, so that this can guide the retailer to develop electricity purchase. In recent years, numerous scholars have put forward many effective load forecasting methods. Vilar, J. et al. provided two procedures to obtain predi...

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“…The number of neurons in the input and output layers equals the number of independent and dependent variables, respectively [3,4,6]. Optimization of the number of hidden layers and hidden neurons was performed by means of a network growing strategy, because smaller networks with fewer weights and biases usually generalize better.…”

Section: Figure 2 Ann Schematic Modelmentioning

confidence: 99%

“…Once trained, they are a quick and reliable tool for anticipating thermal processes performance. Other advantages are precise approximations of complex non-linear problems, greater efficiency than phenomenological models and "black box" approach, hence not requiring a detailed knowledge of the physical phenomena describing the system under analysis [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…On previous researches, statistical parameters like mean absolute percentage error (MAPE), mean squared error (MSE), coefficient of determination ( 2 R ) and correlation coefficient ( R ) were used to choose the network structure that performs better. However, generalization ability was not evaluated even when overfitting and extrapolation errors are well-known limitations of ANN [4,6]. Industry equipment simulations require not only to be accurate, but also reasonably effective beyond experimental data.…”

Section: Introductionmentioning

confidence: 99%

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Artificial neural network modeling of hydrogen sulphide gas coolers ensuring extrapolation capability

Escalona¹,

Góngora-Leyva²

2018

MMEP

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Hydrogen sulphide coolers are jacketed shell-and-tube heat exchangers designed to cool down the gas from 416.15 K to 310.15 K, as well as to remove sulphur carryovers. It is difficult to accurately compute their performance by traditional methods, since thermal analysis is based on several simplifications and empirical correlations. To overcome this limitation, the aim of present research was to propose an artificial neural network model for prediction of coolers outputs, using the mean absolute percentage error, correlation coefficient and extrapolation capability as selection criteria. Structure optimization was carried out through a network growing strategy, using 120 experimental data points for networks training, validation and testing. Model generalization was verified by comparing responses against the predictions of a validated phenomenological model, based on the ε-NTU method, for one set of 20 unseen data points. Best performance was obtained with the 6-5-4-3 multilayer perceptron, using the Levenberg-Marquardt learning algorithm. 99.47 % overall correlation and 0.33 % mean absolute percentage error were achieved when computing the hydrogen sulphide and water streams outlet temperatures. Despite the high prediction performance, a few model responses were found deprived of physical sense.

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Analysis of artificial neural network in prediction of circulation rate for a natural circulation vertical thermosiphon reboiler

Cited by 22 publications

References 16 publications

PCA/SVM-Based Method for Pattern Detection in a Multisensor System

PCA/SVM-Based Method for Pattern Detection in a Multisensor System

Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game

Artificial neural network modeling of hydrogen sulphide gas coolers ensuring extrapolation capability

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