• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of green energy >DEVELOPMENT OF ANN-BASED MODELS TO PREDICT BIOGAS AND METHANE PRODUCTIONS IN ANAEROBIC TREATMENT OF MOLASSES WASTEWATER
【24h】

DEVELOPMENT OF ANN-BASED MODELS TO PREDICT BIOGAS AND METHANE PRODUCTIONS IN ANAEROBIC TREATMENT OF MOLASSES WASTEWATER

机译：基于ANN的厌氧处理糖蜜废水中沼气和甲烷产生模型的开发。

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Two three-layer artificial neural network (ANN) models were respectively developed to predict biogas and methane production rates in a pilot-scale mesophttic up-flow anaerobic sludge blanket (UASB) reactor treating molasses wastewater. Eight process-related variables such as volumetric organic loading rate (OLR), influent and effluent pH, operating temperature, influent and effluent alkalinity, effluent chemical oxygen demand (COD), and volatile fatty acid (VFA) concentrations were selected for the implementation of an artificial intelligence-based approach. A tangent sigmoid transfer function (tansig) at the hidden layer and a linear transfer function (purelin) at the output layer were conducted for the proposed ANN models. Several benchmark comparisons were conducted to obtain an optimal algorithm for training the network. After backpropagation training combined with principal component analysis (PCA), the scaled conjugate gradient algorithm (trainscg) was found as the best of the 11 training algorithms. The number of neurons in the hidden layer was optimized as nine and 12 with the minimum mean squared errors (MSE) of 0.06238 and 0.06488, respectively, for the estimation of biogas and methane production rates. ANN-predicted results were also compared to the outputs of two non-linear regression models by means of several statistical indicators, such as determination coefficient (R~2), unsystematic root mean-square error (RMSEu), index of agreement (IA), and fractional variance (FV). Computational results clearly demonstrated that, compared to the conventional multiple regression-based methodology, the proposed ANN-based models produced smaller deviations and exhibited superior predictive accuracy with satisfactory determination coefficients of about 0.93S and 0.924, respectively, for the forecasts of biogas and methane production rates.
机译：分别开发了两个三层人工神经网络（ANN）模型，以预测在中规模的中观向上流厌氧污泥层（UASB）反应器中处理糖蜜废水的沼气和甲烷产生速率。选择了八个与过程相关的变量，例如体积有机负荷率（OLR），进水和出水pH，操作温度，进水和出水碱度，出水化学需氧量（COD）和挥发性脂肪酸（VFA）浓度，以实施基于人工智能的方法。对于所提出的ANN模型，在隐藏层进行了切线S形传递函数（tansig），在输出层进行了线性传递函数（purelin）。进行了几次基准比较，以获得用于训练网络的最佳算法。经过反向传播训练与主成分分析（PCA）的结合，缩放比例共轭梯度算法（trainscg）被认为是11种训练算法中最好的。隐藏层中神经元的数量被优化为9个和12个，最小均方误差（MSE）分别为0.06238和0.06488，用于估算沼气和甲烷的产生速率。 ANN预测的结果还通过几个统计指标与两个非线性回归模型的输出进行比较，例如确定系数（R〜2），非系统性均方根误差（RMSEu），一致性指数（IA）以及分数方差（FV）。计算结果清楚地表明，与传统的基于多元回归的方法相比，所提出的基于ANN的模型产生的偏差较小，并且具有较高的预测精度，对于沼气和甲烷的预测系数分别约为0.93S和0.924。生产率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号