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Experimental investigation and ANN modeling on improved performance of an innovative method of using

Srinivasan CHANDRASEKARAN, Arunachalam AMARKARTHIK, Karuppan SIVAKUMAR, Dhanasekaran SELVAMUTHUKUMARAN, Shaji SIDNEY

《能源前沿（英文）》 2013年第7卷第3期页码 279-287 doi: 10.1007/s11708-013-0268-4

摘要： To convert wave energy into usable forms of energy by utilizing heaving body, heaving bodies (buoys) which are buoyant in nature and float on the water surface are usually used. The wave exerts excess buoyancy force on the buoy, lifting it during the approach of wave crest while the gravity pulls it down during the wave trough. A hydraulic, direct or mechanical power takeoff is used to convert this up and down motion of the buoy to produce usable forms of energy. Though using a floating buoy for harnessing wave energy is conventional, this device faces many challenges in improving the overall conversion efficiency and survivability in extreme conditions. Up to the present, no studies have been done to harness ocean waves using a non-floating object and to find out the merits and demerits of the system. In the present paper, an innovative heaving body type of wave energy converter with a non-floating object was proposed to harness waves. It was also shown that the conversion efficiency and safety of the proposed device were significantly higher than any other device proposed with floating buoy. To demonstrate the improvements, experiments were conducted with non-floating body for different dimensions and the heave response was noted. Power generation was not considered in the experiment to observe the worst case response of the heaving body. The device was modeled in artificial neural network (ANN), the heave response for various parameters were predicted, and compared with the experimental results. It was found that the ANN model could predict the heave response with an accuracy of 99%.

关键词： ocean wave energy point absorbers heaving body non-floating object heave response ratio artificial neural network (ANN)

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Multi-objective optimization of process parameters in Electro-Discharge Diamond Face Grinding based on ANN-NSGA-II

Ravindra Nath YADAV, Vinod YADAVA, G.K. SINGH

《机械工程前沿（英文）》 2013年第8卷第3期页码 319-332 doi: 10.1007/s11465-013-0269-3

摘要：

The effective study of hybrid machining processes (HMPs), in terms of modeling and optimization has always been a challenge to the researchers. The combined approach of Artificial Neural Network (ANN) and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) has attracted attention of researchers for modeling and optimization of the complex machining processes. In this paper, a hybrid machining process of Electrical Discharge Face Grinding (EDFG) and Diamond Face Grinding (DFG) named as Electrical Discharge Diamond face Grinding (EDDFG) have been studied using a hybrid methodology of ANN-NSGA-II. In this study, ANN has been used for modeling while NSGA-II is used to optimize the control parameters of the EDDFG process. For observations of input-output relations, the experiments were conducted on a self developed face grinding setup, which is attached with the ram of EDM machine. During experimentation, the wheel speed, pulse current, pulse on-time and duty factor are taken as input parameters while output parameters are material removal rate (MRR) and average surface roughness (R_a). The results have shown that the developed ANN model is capable to predict the output responses within the acceptable limit for a given set of input parameters. It has also been found that hybrid approach of ANN-NSGA-II gives a set of optimal solutions for getting appropriate value of outputs with multiple objectives.

关键词： hybrid machining processes (HMPs) electrical discharge diamond grinding (EDDG) artificial neural network (ANN) genetic algorithm modeling and optimization

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Comparison of modeling methods for wind power prediction: a critical study

Rashmi P. SHETTY, A. SATHYABHAMA, P. Srinivasa PAI

《能源前沿（英文）》 2020年第14卷第2期页码 347-358 doi: 10.1007/s11708-018-0553-3

摘要： Prediction of power generation of a wind turbine is crucial, which calls for accurate and reliable models. In this work, six different models have been developed based on wind power equation, concept of power curve, response surface methodology (RSM) and artificial neural network (ANN), and the results have been compared. To develop the models based on the concept of power curve, the manufacturer’s power curve, and to develop RSM as well as ANN models, the data collected from supervisory control and data acquisition (SCADA) of a 1.5 MW turbine have been used. In addition to wind speed, the air density, blade pitch angle, rotor speed and wind direction have been considered as input variables for RSM and ANN models. Proper selection of input variables and capability of ANN to map input-output relationships have resulted in an accurate model for wind power prediction in comparison to other methods.

关键词： power curve method of least squares cubic spline interpolation response surface methodology artificial neural network (ANN)

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Optimization of electrochemically synthesized Cu

Kasra Pirzadeh, Ali Asghar Ghoreyshi, Mostafa Rahimnejad, Maedeh Mohammadi

《化学科学与工程前沿（英文）》 2020年第14卷第2期页码 233-247 doi: 10.1007/s11705-019-1893-1

摘要： Cu (BTC) , a common type of metal organic framework (MOF), was synthesized through electrochemical route for CO capture and its separation from N . Taguchi method was employed for optimization of key parameters affecting the synthesis of Cu (BTC) . The results indicated that the optimum synthesis conditions with the highest CO selectivity can be obtained using 1 g of ligand, applied voltage of 25 V, synthesis time of 2 h, and electrode length of 3 cm. The single gas sorption capacity of the synthetized microstructure Cu (BTC) for CO (at 298 K and 1 bar) was a considerable value of 4.40 mmol·g . The isosteric heat of adsorption of both gases was calculated by inserting temperature-dependent form of Langmuir isotherm model in the Clausius-Clapeyron equation. The adsorption of CO /N binary mixture with a concentration ratio of 15/85 vol-% was also studied experimentally and the result was in a good agreement with the predicted value of IAST method. Moreover, Cu (BTC) showed no considerable loss in CO adsorption after six sequential cycles. In addition, artificial neural networks (ANNs) were also applied to predict the separation behavior of CO /N mixture by MOFs and the results revealed that ANNs could serve as an appropriate tool to predict the adsorptive selectivity of the binary gas mixture in the absence of experimental data.

关键词： Cu₃(BTC)₂ electrochemical synthesis CO₂ adsorption Taguchi optimization ANN modeling

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Predicting the capacity of perfobond rib shear connector using an ANN model and GSA method

Guorui SUN; Jun SHI; Yuang DENG

《结构与土木工程前沿（英文）》 2022年第16卷第10期页码 1233-1248 doi: 10.1007/s11709-022-0878-1

摘要： Due to recent advances in the field of artificial neural networks (ANN) and the global sensitivity analysis (GSA) method, the application of these techniques in structural analysis has become feasible. A connector is an important part of a composite beam, and its shear strength can have a significant impact on structural design. In this paper, the shear performance of perfobond rib shear connectors (PRSCs) is predicted based on the back propagation (BP) ANN model, the Genetic Algorithm (GA) method and GSA method. A database was created using push-out test test and related references, where the input variables were based on different empirical formulas and the output variables were the corresponding shear strengths. The results predicted by the ANN models and empirical equations were compared, and the factors affecting shear strength were examined by the GSA method. The results show that the use of ANN model optimization by GA method has fewer errors compared to the empirical equations. Furthermore, penetrating reinforcement has the greatest sensitivity to shear performance, while the bonding force between steel plate and concrete has the least sensitivity to shear strength.

关键词： perfobond rib shear connector shear strength ANN model global sensitivity analysis

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An ANN-exhaustive-listing method for optimization of multiple building shapes and envelope properties

Yaolin LIN, Wei YANG

《能源前沿（英文）》 2021年第15卷第2期页码 550-563 doi: 10.1007/s11708-019-0607-1

摘要： With increasing awareness of sustainability, demands on optimized design of building shapes with a view to maximize its thermal performance have become stronger. Current research focuses more on building envelopes than shapes, and thermal comfort of building occupants has not been considered in maximizing thermal performance in building shape optimization. This paper attempts to develop an innovative ANN (artificial neural network)-exhaustive-listing method to optimize the building shapes and envelope physical properties in achieving maximum thermal performance as measured by both thermal load and comfort hour. After verified, the developed method is applied to four different building shapes in five different climate zones in China. It is found that the building shape needs to be treated separately to achieve sufficient accuracy of prediction of thermal performance and that the ANN is an accurate technique to develop models of discomfort hour with errors of less than 1.5%. It is also found that the optimal solutions favor the smallest window-to-external surface area with triple-layer low-E windows and insulation thickness of greater than 90 mm. The merit of the developed method is that it can rapidly reach the optimal solutions for most types of building shapes with more than two objective functions and large number of design variables.

关键词： ANN (artificial neural network) exhaustive-listing building shape optimization thermal load thermal comfort

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Optimal generation scheduling in power system using frequency prediction through ANN under ABT environment

Simarjit KAUR, Yajvender Pal VERMA, Sunil AGRAWAL

《能源前沿（英文）》 2013年第7卷第4期页码 468-478 doi: 10.1007/s11708-013-0282-6

摘要： In a competitive and deregulated power scenario, the utilities try to maintain their real electric power generation in balance with the load demand, which creates a need for the precise real time generation scheduling (GS). In this paper, the GS problem is solved to perform the unit commitment (UC) based on frequency prediction by using artificial neural network (ANN) with the objective to minimize the overall system cost of the state utility. The introduction of availability-based tariff (ABT) signifies the importance of frequency in GS. Under-prediction or over-prediction will result in an unnecessary commitment of generating units or buying power from central generating units at a higher cost. Therefore, an accurate frequency prediction is the first step toward optimal GS. The dependency of frequency on various parameters such as actual generation, load demand, wind power and power deficit has been considered in this paper. The proposed technique provides a reliable solution for the input parameter different from the one presented in the training data. The performance of the frequency predictor model has been evaluated based on the absolute percentage error (APE) and the mean absolute percentage error (MAPE). The proposed predicted frequency sensitive GS model is applied to the system of Indian state of Tamilnadu, which reduces the overall system cost of the state utility by keeping off the dearer units selected based on the predicted frequency.

关键词： artificial neural network (ANN) frequency prediction availability-based tariff (ABT) generation scheduling (GS)

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食品安全与健康

Martin Cole, Mary Ann Augustin

《工程（英文）》 2020年第6卷第4期页码 391-392 doi: 10.1016/j.eng.2020.01.010

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QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

《结构与土木工程前沿（英文）》 2023年第17卷第1期页码 25-36 doi: 10.1007/s11709-022-0908-z

摘要： In recent years, tunnel boring machines (TBMs) have been widely used in tunnel construction. However, the TBM control parameters set based on operator experience may not necessarily be suitable for certain geological conditions. Hence, a method to optimize TBM control parameters using an improved loss function-based artificial neural network (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein. The purpose of this method is to improve the TBM performance by optimizing the penetration and cutterhead rotation speeds. Inspired by the regularization technique, a custom artificial neural network (ANN) loss function based on the penetration rate and rock-breaking specific energy as TBM performance indicators is developed in the form of a penalty function to adjust the output of the network. In addition, to overcome the disadvantage of classical error backpropagation ANNs, i.e., the ease of falling into a local optimum, QPSO is adopted to train the ANN hyperparameters (weight and bias). Rock mass classes and tunneling parameters obtained in real time are used as the input of the QPSO-ILF-ANN, whereas the cutterhead rotation speed and penetration are specified as the output. The proposed method is validated using construction data from the Songhua River water conveyance tunnel project. Results show that, compared with the TBM operator and QPSO-ANN, the QPSO-ILF-ANN effectively increases the TBM penetration rate by 14.85% and 13.71%, respectively, and reduces the rock-breaking specific energy by 9.41% and 9.18%, respectively.

关键词： tunnel boring machine control parameter optimization quantum particle swarm optimization artificial neural network tunneling energy efficiency

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基于RBF神经网络算法的连拱隧道围岩变形预测方法研究

肖智旺,钟登华

《中国工程科学》 2008年第10卷第7期页码 77-81

摘要：

利用径向基函数前馈式神经网络的特性，构建了连拱隧洞围岩变形的预测模型，并利用Matlab工具对模型进行求解。最后的工程实例对文章的方法进行了检验，其结果表明，此方法具有求解速度快，结果更为优化、预测效果更好等优点。

关键词：连拱隧洞围岩变形变形预测径向基函数(RBF) 神经网络

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基于GA-ANN的震灾风险预测模型研究

刘明广,郭章林

《中国工程科学》 2006年第8卷第3期页码 83-86

摘要：

对震灾的各种主要风险因素进行系统的辨识和分析，并建立了震灾风险预测的遗传神经网络模型，用实例证明了该模型的可行性与有效性，为决策部门提供一种有效的震灾风险预测方法。

关键词：地震灾害风险因素人工神经网络遗传算法预测

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思想的力量：重大工程技术挑战的国际影响力 Views & Comments

., Dame Ann Dowling, Ji Zhou

《工程（英文）》 2016年第2卷第1期页码 4-7 doi: 10.1016/J.ENG.2016.01.025

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姑山矿区多矿床矿产资源开发利用的综合优化模型研究

蔡嗣经,王文潇,郑明贵

《中国工程科学》 2011年第13卷第3期页码 56-62

摘要：

依据整体采矿学的理念，即从整体上全过程、多方位、系统和动态地研究一个矿区多矿床矿产资源的开采与利用，构建了人工神经网络专家系统综合优化模型，根据所构建的模型对姑山矿区的多矿床矿产资源开采与利用进行了综合优化研究，得出了姑山矿区的铁矿石推荐生产规模为540×10⁴ ~680×10⁴ t/a的结论。研究结论对加强姑山矿区的建设、提高经济社会效益以及矿区可持续发展均有较好的指导作用。

关键词：姑山矿区多矿床开采整体采矿学人工神经网络专家系统模型

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Delivering food safety

Kaye BASFORD,Richard BENNETT,Joanne DALY,Mary Ann AUGUSTIN,Snow BARLOW,Tony GREGSON,Alice LEE,Deli CHEN

《农业科学与工程前沿（英文）》 2017年第4卷第1期页码 1-4 doi: 10.15302/J-FASE-2016123

摘要： A delegation from the Australian Academy of Technological Sciences and Engineering traveled to Beijing in April 2016 to jointly run a workshop on technology advances in food safety with the Chinese Academy of Engineering. This brief summary from the Australian delegation identifies the pyramid of inter- locking issues which must be addressed to deliver food safety. Systems and technology provide the necessary base, on which culture and then trust can be built to facilitate the delivery of food safety now and in the future.

关键词： culture food safety systems technology trust

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风云三号A星微波湿度计数据处理与应用

何杰颖,张升伟

《中国工程科学》 2013年第15卷第10期页码 47-53

摘要：

主要介绍了风云三号A星（FY-3A）有效载荷之一——微波湿度计的结构、运行状态以及数据接收和数据处理的具体形式。利用神经网络算法建立反演模型，并与国外已经业务运行的先进微波探测单元B型（AMSU-B）比较，性能相当。反演北京地区2008年7—12月相对湿度和水汽密度廓线，对比探空数据，分析反演均方差。同时，分析了台风到来时不同通道的亮温显示结果，从而证明：风云三号微波湿度计不仅能全球范围内探测大气水汽等相关信息，同样在台风、热带气旋的检测和判断未来的走势中也发挥了重要作用。

关键词：微波湿度计风云三号A星神经网络算法水汽密度