利用混合ANN-PSO技术优化多孔池圆形阶梯梯级曝气器的曝气性能

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY Information Processing in Agriculture Pub Date : 2022-12-01 DOI:10.1016/j.inpa.2021.09.002
Subha M. Roy, C.M. Pareek, Rajendra Machavaram, C.K. Mukherjee
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引用次数: 14

摘要

为了满足水生物种对氧气的需求,养殖池塘的人工曝气系统变得必不可少。曝气器的性能一般以标准曝气效率(SAE)来衡量,而标准曝气效率受曝气器几何参数和动态参数的不同影响较大。因此,为了提高曝气器的曝气性能,需要对这些参数进行优化。设计了一种多孔池型圆形阶梯梯级(PPCSC)曝气器,并采用ANN-PSO混合优化技术对其几何参数和动力学参数进行优化,使其曝气效率最大化。几何参数包括连续步宽比(Wi-1/Wi)和射孔直径与最底部半径比(d/Rb),动态参数包括水流速率(Q)。采用3-6-1人工神经网络模型结合粒子群优化(PSO)方法,得到了最大SAE对应的几何参数和动态参数的最优值。连续阶宽比(Wi-1/Wi)、射孔直径与最底半径比(d/Rb)和水流速(Q)的最佳值分别为1.15、0.0027和0.016 7 m3/s。交叉验证结果显示,预测值与实验值之间的偏差为3.07%,从而证实了所提出的混合ANN-PSO技术的充分性。
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Optimizing the aeration performance of a perforated pooled circular stepped cascade aerator using hybrid ANN-PSO technique

Artificial aeration system for aquaculture ponds becomes essential to meet the oxygen requirement posed by the aquatic species. The performance of an aerator is generally measured in terms of standard aeration efficiency (SAE), which is significantly affected by the different geometric and dynamic parameters of the aerator. Therefore, to enhance the aeration performance of an aerator, these parameters need to be optimized. In the present study, a perforated pooled circular stepped cascade (PPCSC) aerator was developed, and the geometric and dynamic parameters of the developed aerator were optimized using the hybrid ANN-PSO technique for maximizing its aeration efficiency. The geometric parameters include consecutive step width ratio (Wi-1/Wi) and the perforation diameter to the bottom-most radius ratio (d/Rb), whereas the dynamic parameter includes the water flow rate (Q). A 3–6-1 ANN model coupled with particle swarm optimization (PSO) approach was used to obtain the optimum values of geometric and dynamic parameters corresponding to the maximum SAE. The optimal values of the consecutive step width ratio (Wi-1/Wi), the perforation diameter to the bottom-most radius ratio (d/Rb), and the water flow rate (Q) for maximizing the SAE were found to be 1.15, 0.002 7 and 0.016 7 m3/s, respectively. The cross-validation results showed a deviation of 3.07 % between the predicted and experimental SAE values, thus confirming the adequacy of the proposed hybrid ANN-PSO technique.

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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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