The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform

Dong-yu Liu - One of the best experts on this subject based on the ideXlab platform.

  • influence of the Dissolved Oxygen Content on corrosion of the ferritic martensitic steel p92 in supercritical water
    Corrosion Science, 2012
    Co-Authors: Naiqiang Zhang, Yang Bai, Dong-yu Liu
    Abstract:

    Abstract The corrosion of the ferritic–martensitic steel P92 exposed to supercritical water at 550 °C under 25 MPa with the Dissolved Oxygen Contents of 100, 300 and 2000 ppb was investigated. The results indicated that the weight gain increased with the Dissolved Oxygen Content. The oxide scale with a typical dual-layered structure including a Fe-rich outer magnetite layer and a Cr-rich inner layer was formed on all samples in spite of different Dissolved Oxygen. Finally, the possible explanations for the influence of the Dissolved Oxygen Content on the weight gain and exfoliation of oxide scale were given.

  • Influence of the Dissolved Oxygen Content on corrosion of the ferritic–martensitic steel P92 in supercritical water
    Corrosion Science, 2012
    Co-Authors: Naiqiang Zhang, Yang Bai, Dong-yu Liu
    Abstract:

    Abstract The corrosion of the ferritic–martensitic steel P92 exposed to supercritical water at 550 °C under 25 MPa with the Dissolved Oxygen Contents of 100, 300 and 2000 ppb was investigated. The results indicated that the weight gain increased with the Dissolved Oxygen Content. The oxide scale with a typical dual-layered structure including a Fe-rich outer magnetite layer and a Cr-rich inner layer was formed on all samples in spite of different Dissolved Oxygen. Finally, the possible explanations for the influence of the Dissolved Oxygen Content on the weight gain and exfoliation of oxide scale were given.

Naiqiang Zhang - One of the best experts on this subject based on the ideXlab platform.

  • influence of the Dissolved Oxygen Content on corrosion of the ferritic martensitic steel p92 in supercritical water
    Corrosion Science, 2012
    Co-Authors: Naiqiang Zhang, Yang Bai, Dong-yu Liu
    Abstract:

    Abstract The corrosion of the ferritic–martensitic steel P92 exposed to supercritical water at 550 °C under 25 MPa with the Dissolved Oxygen Contents of 100, 300 and 2000 ppb was investigated. The results indicated that the weight gain increased with the Dissolved Oxygen Content. The oxide scale with a typical dual-layered structure including a Fe-rich outer magnetite layer and a Cr-rich inner layer was formed on all samples in spite of different Dissolved Oxygen. Finally, the possible explanations for the influence of the Dissolved Oxygen Content on the weight gain and exfoliation of oxide scale were given.

  • Influence of the Dissolved Oxygen Content on corrosion of the ferritic–martensitic steel P92 in supercritical water
    Corrosion Science, 2012
    Co-Authors: Naiqiang Zhang, Yang Bai, Dong-yu Liu
    Abstract:

    Abstract The corrosion of the ferritic–martensitic steel P92 exposed to supercritical water at 550 °C under 25 MPa with the Dissolved Oxygen Contents of 100, 300 and 2000 ppb was investigated. The results indicated that the weight gain increased with the Dissolved Oxygen Content. The oxide scale with a typical dual-layered structure including a Fe-rich outer magnetite layer and a Cr-rich inner layer was formed on all samples in spite of different Dissolved Oxygen. Finally, the possible explanations for the influence of the Dissolved Oxygen Content on the weight gain and exfoliation of oxide scale were given.

Chen Yingyi - One of the best experts on this subject based on the ideXlab platform.

  • Dissolved Oxygen Content prediction in crab culture using a hybrid intelligent method.
    Scientific reports, 2016
    Co-Authors: Chen Yingyi, Shahbazgul Hassan
    Abstract:

    A precise predictive model is needed to obtain a clear understanding of the changing Dissolved Oxygen Content in outdoor crab ponds, to assess how to reduce risk and to optimize water quality management. The uncertainties in the data from multiple sensors are a significant factor when building a Dissolved Oxygen Content prediction model. To increase prediction accuracy, a new hybrid Dissolved Oxygen Content forecasting model based on the radial basis function neural networks (RBFNN) data fusion method and a least squares support vector machine (LSSVM) with an optimal improved particle swarm optimization(IPSO) is developed. In the modelling process, the RBFNN data fusion method is used to improve information accuracy and provide more trustworthy training samples for the IPSO-LSSVM prediction model. The LSSVM is a powerful tool for achieving nonlinear Dissolved Oxygen Content forecasting. In addition, an improved particle swarm optimization algorithm is developed to determine the optimal parameters for the LSSVM with high accuracy and generalizability. In this study, the comparison of the prediction results of different traditional models validates the effectiveness and accuracy of the proposed hybrid RBFNN-IPSO-LSSVM model for Dissolved Oxygen Content prediction in outdoor crab ponds.

  • Three-Dimensional Short-Term Prediction Model of Dissolved Oxygen Content Based on PSO-BPANN Algorithm Coupled with Kriging Interpolation
    Mathematical Problems in Engineering, 2016
    Co-Authors: Chen Yingyi, Yu Huihui, Zhumi Zhen
    Abstract:

    Dissolved Oxygen (DO) Content is a significant aspect of water quality in aquaculture. Prediction of Dissolved Oxygen may timely avoid the financial loss caused by inappropriate Dissolved Oxygen Content and three-dimensional prediction can achieve more accurate and overall guidance. Therefore, this study presents a three-dimensional short-term prediction model of Dissolved Oxygen in crab aquaculture ponds based on back propagation artificial neural network (BPANN) optimized by particle swarm optimization (PSO), which coupled with Kriging method. In this model, wavelet analysis is adopted for denoising, BPANN optimized by PSO is utilized for data analysis and one-dimensional prediction, and Kriging method is used for three-dimensional prediction. Compared with traditional one-dimensional prediction model, three-dimensional model has more real reaction of Dissolved Oxygen Content in crab growth environment. In particular, the merits of PSO are evaluated against genetic algorithm (GA). The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) for PSO model are 0.136445, 0.90534, and 0.15384, respectively, while for the GA model the values are 2.04184, 1.18316, and 0.21014, respectively. Furthermore, results of cross validation experiment show that the average error of this model is 0.0705 (mg/L). Consequently, this study suggests that the prediction model operates in a satisfactory manner.

  • A hybrid WA-CPSO-LSSVR model for Dissolved Oxygen Content prediction in crab culture
    Engineering Applications of Artificial Intelligence, 2014
    Co-Authors: Shuangyin Liu, Yu Jiang, Chen Yingyi
    Abstract:

    To increase prediction accuracy, reduce aquaculture risks and optimize water quality management in intensive aquaculture ponds, this paper proposes a hybrid Dissolved Oxygen Content forecasting model based on wavelet analysis (WA) and least squares support vector regression (LSSVR) with an optimal improved Cauchy particle swarm optimization (CPSO) algorithm. In the modeling process, the original Dissolved Oxygen sequences were de-noised and decomposed into several resolution frequency signal subsets using the wavelet analysis method. Independent prediction models were developed using decomposed signals with wavelet analysis and least squares support vector regression. The independent prediction values were reconstructed to obtain the ultimate prediction results. In addition, because the kernel parameter @d and the regularization parameter @c in the LSSVR training procedure significantly influence forecasting accuracy, the Cauchy particle swarm optimization (CPSO) algorithm was used to select optimum parameter combinations for LSSVR. The proposed hybrid model was applied to predict Dissolved Oxygen in river crab culture ponds. Compared with traditional models, the test results of the hybrid WA-CPSO-LSSVR model demonstrate that de-noising and capturing non-stationary characteristics of Dissolved Oxygen signals after WA comprise a very powerful and reliable method for predicting Dissolved Oxygen Content in intensive aquaculture accurately and quickly.

  • CCTA (2) - Dissolved Oxygen Prediction Model Which Based on Fuzzy Neural Network
    Computer and Computing Technologies in Agriculture VII, 2014
    Co-Authors: Liu Yalin, Wei Yaoguang, Chen Yingyi
    Abstract:

    In crab ponds, Dissolved Oxygen is the foundation for pond cultivation’s survival. The changes of Dissolved Oxygen Content are influenced by multiple factors. Higher levels of Dissolved Oxygen Content are crucial to maintaining healthy growth of crab breeding. Affected by physic-chemical process of aquatic water, the changes of Dissolved Oxygen Content have a large lag. In order to solve the problem of Dissolved Oxygen forecast, the prediction model which based on fuzzy neural network has been proposed in this paper. It integrated the characteristic of learning fuzzy logic and neural networks optimized performance to realize the Dissolved Oxygen prediction. The prediction results have shown it more suitable for Dissolved Oxygen prediction than grey neural network method. The prediction accuracy can meet the need of Dissolved control.

  • Dissolved Oxygen Prediction Model Which Based on Fuzzy Neural Network
    2013
    Co-Authors: Liu Yalin, Wei Yaoguang, Chen Yingyi
    Abstract:

    In crab ponds, Dissolved Oxygen is the foundation for pond cultivation’s survival. The changes of Dissolved Oxygen Content are influenced by multiple factors. Higher levels of Dissolved Oxygen Content are crucial to maintaining healthy growth of crab breeding. Affected by physic-chemical process of aquatic water, the changes of Dissolved Oxygen Content have a large lag. In order to solve the problem of Dissolved Oxygen forecast, the prediction model which based on fuzzy neural network has been proposed in this paper. It integrated the characteristic of learning fuzzy logic and neural networks optimized performance to realize the Dissolved Oxygen prediction. The prediction results have shown it more suitable for Dissolved Oxygen prediction than grey neural network method. The prediction accuracy can meet the need of Dissolved control.

Yang Bai - One of the best experts on this subject based on the ideXlab platform.

  • influence of the Dissolved Oxygen Content on corrosion of the ferritic martensitic steel p92 in supercritical water
    Corrosion Science, 2012
    Co-Authors: Naiqiang Zhang, Yang Bai, Dong-yu Liu
    Abstract:

    Abstract The corrosion of the ferritic–martensitic steel P92 exposed to supercritical water at 550 °C under 25 MPa with the Dissolved Oxygen Contents of 100, 300 and 2000 ppb was investigated. The results indicated that the weight gain increased with the Dissolved Oxygen Content. The oxide scale with a typical dual-layered structure including a Fe-rich outer magnetite layer and a Cr-rich inner layer was formed on all samples in spite of different Dissolved Oxygen. Finally, the possible explanations for the influence of the Dissolved Oxygen Content on the weight gain and exfoliation of oxide scale were given.

  • Influence of the Dissolved Oxygen Content on corrosion of the ferritic–martensitic steel P92 in supercritical water
    Corrosion Science, 2012
    Co-Authors: Naiqiang Zhang, Yang Bai, Dong-yu Liu
    Abstract:

    Abstract The corrosion of the ferritic–martensitic steel P92 exposed to supercritical water at 550 °C under 25 MPa with the Dissolved Oxygen Contents of 100, 300 and 2000 ppb was investigated. The results indicated that the weight gain increased with the Dissolved Oxygen Content. The oxide scale with a typical dual-layered structure including a Fe-rich outer magnetite layer and a Cr-rich inner layer was formed on all samples in spite of different Dissolved Oxygen. Finally, the possible explanations for the influence of the Dissolved Oxygen Content on the weight gain and exfoliation of oxide scale were given.

Cheng Qianqian - One of the best experts on this subject based on the ideXlab platform.

  • A hybrid intelligent method for three-dimensional short-term prediction of Dissolved Oxygen Content in aquaculture.
    PloS one, 2018
    Co-Authors: Yingyi Chen, Cheng Yanjun, Cheng Qianqian
    Abstract:

    A precise predictive model is important for obtaining a clear understanding of the changes in Dissolved Oxygen Content in crab ponds. Highly accurate interval forecasting of Dissolved Oxygen Content is fundamental to reduce risk, and three-dimensional prediction can provide more accurate results and overall guidance. In this study, a hybrid three-dimensional (3D) Dissolved Oxygen Content prediction model based on a radial basis function (RBF) neural network, K-means and subtractive clustering was developed and named the subtractive clustering (SC)-K-means-RBF model. In this modeling process, K-means and subtractive clustering methods were employed to enhance the hyperparameters required in the RBF neural network model. The comparison of the predicted results of different traditional models validated the effectiveness and accuracy of the proposed hybrid SC-K-means-RBF model for three-dimensional prediction of Dissolved Oxygen Content. Consequently, the proposed model can effectively display the three-dimensional distribution of Dissolved Oxygen Content and serve as a guide for feeding and future studies.