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Performance prediction of a cooling tower using artificial neural network

ENERGY CONVERSION AND MANAGEMENT 2007年第4期48卷 1349-1359页

作者： Hosoz, M. Ertunc, H. M. Bulgurcu, H. Kocaeli Univ Dept Mech Educ TR-41380 Kocaeli Turkey Kocaeli Univ Dept Engn Mech TR-41040 Kocaeli Turkey Balikesir Univ Dept Air Conditioning & Refrigerat Technol Balikesir Turkey

This paper describes an application of artificial neural networks (ANNs) to predict the performance of a cooling tower under a broad range of operating conditions. In order to gather data for training and testing the proposed ANN model, an experimental counter flow cooling tower was operated at steady state conditions while varying the dry bulb temperature and relative humidity of the air entering the tower and the temperature of the incoming hot water along with the flow rates of the air and water streams. Utilizing some of the experimental data for training, an ANN model based on a standard back propagation algorithm was developed. The model was used for predicting various performance parameters of the system, namely the heat rejection rate at the tower, the rate of water evaporated into the air stream, the temperature of the outgoing water stream and the dry bulb temperature and relative humidity of the outgoing air stream. The performances of the ANN predictions were tested using experimental data not employed in the training process. The predictions usually agreed well with the experimental values with correlation coefficients in the range of 0.975-0.994, mean relative errors in the range of 0.89-4.64% and very low root mean square errors. Furthermore, the ANN yielded agreeable results when it was used for predicting the system performance outside the range of the experiments. The results show that the ANN approach can be applied successfully and can provide high accuracy and reliability for predicting the performance of cooling towers. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： artificial neural network back-propagation algorithm cooling tower

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Supervised Learning in Model Reference Adaptive Sliding Mode Control

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2024年第6期22卷 2007-2015页

作者： Makke, Omar Lin, Feng Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA

The well known back-propagation algorithm has revolutionized machine learning and artificial intelligence, particularly in neural network applications. Although gradient descent-based algorithms are utilized in control applications, they are not as prevalent as in neural network applications. This discrepancy can be attributed to the successful development of various adaptation laws which ensure system stability while meeting the required design criteria. Many of these laws can be found in model reference adaptive control (MRAC) and adaptive sliding mode control (ASMC). This paper investigates the applicability of the Brandt-Lin (B-L) learning algorithm, mathematically equivalent to the back-propagation algorithm, in adaptive control applications. We find that combining the B-L learning algorithm with SMC yields a robust controller suitable for model reference adaptive sliding mode control (MRA-SMC). The controller is applicable to linear and a class of nonlinear dynamic systems and is suitable for efficient implementation. We derive the stability criteria for this controller and conduct simulations to study the adaptation's impact on chattering. Our work exemplifies one approach to adopt the back-propagation algorithm in control applications.

关键词： Adaptive control adaptive sliding mode control (ASMC) back-propagation algorithm chattering reduction model reference adaptive control (MRAC) online learning algorithms

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Predicting of torsional strength of RC beams by using different artificial neural network algorithms and building codes

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ADVANCES IN ENGINEERING SOFTWARE 2010年第7-8期41卷 946-955页

作者： Arslan, M. Hakan Selcuk Univ Engn & Architecture Fac Dept Civil Engn Konya Turkey

In this study, the efficiency of different artificial neural networks (ANNs) in predicting the torsional strength of reinforced concrete (RC) beams is firstly explored. Experimental data of 76 rectangular RC beams from an existing database in the literature were used to develop ANN model. The input parameters affecting the torsional strength were selected as cross-sectional area of beams, dimensions of closed stirrups, spacing of stirrups, cross-sectional area of one-leg of closed stirrup, yield strength of stirrup and longitudinal reinforcement, steel ratio of stirrups, steel ratio of longitudinal reinforcement and concrete compressive strength. Each parameter was arranged in an input vector and a corresponding output vector that includes the torsional strength of RC beam. For all outputs, the ANN models were trained and tested using three layered 11 back-propagation methods. The initial performance evaluation of 11 different back propagations was compared with each other. In addition to these, the paper presents a short review of the well-known building codes provisions for the design of RC beams under pure torsion. The accuracy of the codes in predicting the torsional strength of RC beams was also examined with comparable way by using same test data. The study shows that the ANN models give reasonable predictions of the ultimate torsional strength of RC beams (R-2 approximate to 0.988). Moreover, the study concludes that all ANN models predict the torsional strength of RC beams better than existing building code equations for torsion. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Reinforced concrete beam Artificial neural network Torsional strength Building code Theoretical model back-propagation algorithm

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Prediction of glass forming region in the Sm-Si-Al-O-N system with an artificial neural network

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MATERIALS LETTERS 1996年第4-6期28卷 281-288页

作者： Tu, HY Wang, PL Sun, WY Yan, DS Shanghai Institute of Ceramics Chinese Academy of Sciences 1295 Ding Xi Road Shanghai 200050 China

The glass-forming region in the Sm-Si-Al-O-N system at 1700 degrees C was studied by using an artificial neural network (ANN). An artificial neural network (ANN) was trained and tested with fifty experimentally determined examples in the investigated system with a back-propagation algorithm. The results of all forty-two examples of the training set and the testing set of eight obtained from the ANN are in good agreement with experiments. The glass-forming boundaries on the planes of 25, 27.5, 30, 32.5, 35 and 40 eq% N were predicted with the trained network, which indicates the tendency of contraction towards the Si-rich region with increasing nitrogen content. The approach shows a considerable promise for applications to determination of nonlinear boundaries of glass-forming regions in complex material systems, in cases where thermodynamic calculations of phase equilibria are not effective.

关键词： oxynitride glass glass-forming region Sm-Si-Al-O-N system artificial neural network back-propagation algorithm prediction

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A neural network model for environmental predication: Case study for China

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COMPUTERS & INDUSTRIAL ENGINEERING 1996年第3-4期31卷 879-883页

作者： Sun, LY Wang, Y Management School of Man Jiaotong University Xian Shaanxi P.R.China 710049

In this paper, the popular training method back-propagation (BP) of Nueral Networks (NN) is modified by introducing two parameters into the procedure in order to speed learning and improve the convergence. The modified algorithm is named as BPGM and is successfully used in forecasting the future pollution generation and the investment demand for the environment protection in China. Based on the predication of NN model, the future situation of environment of China is analyzed and the investment in environment protection is compared with the average level of the world. Some policies suggestions are proposed.

关键词： neural network back-propagation algorithm environmental predication

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Artificial neural network for predicting nuclear power plant dynamic behaviors

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NUCLEAR ENGINEERING AND TECHNOLOGY 2021年第10期53卷 3275-3285页

作者： El-Sefy, M. Yosri, A. El-Dakhakhni, W. Nagasaki, S. Wiebe, L. McMaster Univ Dept Civil Engn NSERC CREATE Program Canadian Nucl Energy Infrastruct R Hamilton ON L8S 4L7 Canada McMaster Univ Dept Civil Engn Inst Multihazard Syst Risk Studies INTERFACE Hamilton ON L8S 4L7 Canada McMaster Univ Dept Civil Engn NSERC CaNRisk CREATE Program Hamilton ON L8S 4L7 Canada McMaster Univ Teh INTERFACE Inst Hamilton ON L8S 4L7 Canada McMaster Univ Dept Engn Phys Hamilton ON L8S 4L7 Canada

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different sys-tems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to pro -vide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require signifi-cant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Un-certainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies. (c) 2021 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC

关键词： Data-driven models Artificial intelligence Artificial neural network Nuclear power plant back-propagation algorithm

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An Efficient Quality Inspection of Food Products Using Neural Network Classification

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JOURNAL OF INTELLIGENT SYSTEMS 2020年第1期29卷 1425-1440页

作者： Ali, Syed Sumera Ershad Dildar, Sayyad Ajij Dr Babasaheb Ambedkar Marathwada Univ Chhatrapati Shahu Maharaj Shikshan Sanstha Chh Shahu Coll Engn Dept Elect & Telecommun Engn Aurangabad Maharashtra India MIT Coll Engn Dept Elect & Telecommun Engn Aurangabad Maharashtra India

Currently, there is a necessity for the expansion of precise, rapid, and intentional quality assurance with respect to the character of food and horticultural food items, because it is difficult to maintain and organize food products in an elevated quality and secure manner for the increasing population. In this article, we propose a procedure to resolve difficulties and to categorize food as either a broken or quality product. Therefore, the proposed process encompasses four segments, such as preprocessing, segmentation of broken division, feature extraction, and classification. At the first stage, the preprocessing method is used to remove all unnecessary noises. After that, modified region expansion-related segmentation is undertaken to segment the broken division of the food product. Then, feature extraction is used to remove the distinctive attributes of each food product to categorize their evaluation. Finally, the neural network classification procedure is used to examine the food quality. The proposed method is executed in the operational platform of MATLAB, and the consequences are examined by using obtainable methods.

关键词： Histogram equalization modified region growing segmentation color histogram features gray level co-variance matrix features artificial neural network classifier back-propagation algorithm

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Statistical and evolutionary optimisation of operating conditions for enhanced production of fungal L-asparaginase

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CHEMICAL PAPERS 2011年第6期65卷 798-804页

作者： Baskar, Gurunathan Renganathan, Sahadevan Anna Univ Alagappa Coll Technol Dept Chem Engn Madras 600025 Tamil Nadu India St Josephs Coll Engn Dept Biotechnol Madras 600119 Tamil Nadu India

A three-level central composite design of the Response Surface Methodology and the Artificial Neural Network-linked Genetic algorithm were applied to find the optimum operating conditions for enhanced production of L-asparaginase by the submerged fermentation of Aspergillus terreus MTCC 1782. The various effects of the operating conditions, including temperature, pH, inoculum concentration, agitation rate, and fermentation time on the experimental production of L-asparaginase, were fitted to a second-order polynomial model and non-linear models using Response Surface Methodology and the Artificial Neural Network, respectively. The Artificial Neural Network model fitted well, achieving a higher coefficient of determination (R-2 = 0.999) than the second-order polynomial model (R-2 = 0.962). The L-asparaginase activity (38.57 IU mL(-1)) predicted under the optimum conditions of 32.08 degrees C, pH of 5.85, inoculum concentration of 1 vol. %, agitation rate of 123.5 min(-1), and fermentation time of 55.1 h was obtained using the Artificial Neural Network-linked Genetic algorithm in very close agreement with the activity of 37.84 IU mL(- 1) achieved in confirmation experiments. (C) 2011 Institute of Chemistry, Slovak Academy of Sciences

关键词： L-asparaginase optimisation operating conditions response-surface methodology back-propagation algorithm genetic algorithm

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Calculation of octanol/water partition coefficients for pesticides. A comparative study

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SAR AND QSAR IN ENVIRONMENTAL RESEARCH 1999年第2-3期10卷 249-262页

作者： Devillers, J CTIS F-69140 Rillieux La Pape France

On the basis of a set of 593 experimental octanol;water partition coefficients (log P) for pesticides, the simulation performances of two models using computable descriptors are compared. The backpropagation neural network model designed from autocorrelation descriptors (SARQSAR Environ. Res. (1997), 7, 151 - 172) compares favorably with the AFC model (J. Pharm. Sci. (1995), 84, 83-92) using atom/fragment contributions and correction factors.

关键词： 1-octanol/water partition coefficient pesticides three-layer feedforward neural network back-propagation algorithm atom/fragment descriptors autocorrelation descriptors

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Prediction of processing parameters for injection moulding by using a hybrid neural network

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2001年第10期215卷 1465-1470页

作者： Yarlagadda, PKDV Queensland Univ Technol Fac Built Environm & Engn Sch Mech Mfg & Med Engn Brisbane Qld 4001 Australia

In the present work attempts have been made to determine the process parameters that could affect an injection moulding process based on governing equations of the mould-filling process. Focus is then directed to parameters that require the use of trial and error methods or other complex software to determine the process parameters. The two parameters that are predicted from the developed network are injection time and injection pressure. In this work, the training data are generated by simulation using C-MOLD flow simulation software. A total of 114 data points under different process conditions were collected out of which 94 data points were used to train the network using MATLAB and the remaining information was used for testing the network. Two algorithms are used during the training phase, namely the error back-propagation algorithm and the Levenberg-Marquardt approximation algorithm. Results showed that the latter algorithm is more suitable for this application since the Levenberg algorithm converged rapidly with fewer training cycles when compared with the error back-propagation algorithm. The accuracy of the developed network has been tested by predicting the injection pressure and injection time for few engineering components.

关键词： hybrid neural network back-propagation algorithm injection moulding Levenberg-Marquardt algorithm

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