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Modeling water treatment processes via neural networks and genetic algorithms.

机译：通过神经网络和遗传算法对水处理过程进行建模。

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Attempts to capture the complex physical and chemical relationships of water treatment processes by fitting pilot-scale study data to a mathematical formula have generally been unsuccessful. Water utilities, therefore, need a better modeling technique to model these processes. Neural nets (NNs) trained with a backpropagation (BP) algorithm have been widely used for this purpose due to several reasons: (1) they have been found in practice to generalize well, and (2) the BP algorithm can often find a good set of weights in a reasonable amount of time. Since the BP algorithm is based on calculating the gradient of error with respect to weights and differentiability, it is a very good algorithm for exploring local solutions, but, for the same reason, it can also cause failures in training NN because it can become trapped in local minima (Ham et al., 2000; Haykin, 1999). This problem may be overcome by applying the genetic algorithm (GA) to NN weight optimization. GA is a derivative-free stochastic optimization method based on the features of natural selection and biological evolution. GA is able to explore a large and complex search space and has the potential to produce a global solution, thereby avoiding local minima. In a GA based training NN (GANN), weights are updated according to the GA operators, such as ranking (fitness values), selection, and recombination (crossover and mutation).; The objectives of this study were twofold. The first objective was to apply and study the effectiveness of two different NN training algorithms (BP and GA) as NN predictive models. This comparison was made by using each algorithm to predict the coagulant dosage for two water treatment plants (WTPs) in middle Tennessee and for predicting the formation of disinfection byproducts (DBP, i.e., THM4 and HAA5) using the data from the USEPA's Information Collection Rule (ICR). The second objective was to document the NN modeling procedures used in this project so that they could be used as a basis for developing site-specific NN models for the water industry. The results illustrated that GANN can avoid local minima and effectively produce a global solution. The BPNN produced better correlation coefficients (r) for both the coagulant dosage and DBP models once they successfully avoided the local minima. The average r-value was approximately 0.89 for both WTPs, and 0.94 and 0.88 for the THM4 and HAA5 models, respectively. The lower r-value (0.88) from the HAA5 model was found mainly due to the uncertainty errors that were in the HAA5 testing data subset. Overall, Both BPNN and GANN successfully captured the complex nonlinear relationships involved in predicting coagulant dosages and DBP formation.
机译：尝试通过将中试规模的研究数据拟合为数学公式来捕获水处理过程中复杂的物理和化学关系的尝试通常是不成功的。因此，自来水公司需要更好的建模技术来对这些过程进行建模。经过反向传播（BP）算法训练的神经网络（NN）由于以下原因而被广泛用于此目的：（1）在实践中发现它们能很好地泛化；（2）BP算法通常可以找到一个好的方法。在合理的时间内设置权重。由于BP算法基于权重和可微性的误差梯度计算，因此它是探索局部解的一种非常好的算法，但由于相同的原因，由于它可能会陷入陷阱，因此也会导致训练NN失败局部极小值（Ham et al。，2000; Haykin，1999）。通过将遗传算法（GA）应用于NN权重优化，可以克服此问题。遗传算法是一种基于自然选择和生物进化特征的无导数随机优化方法。 Google Analytics（分析）可以探索庞大而复杂的搜寻空间，并有可能产生整体解决方案，从而避免出现局部最小值。在基于GA的训练NN（GANN）中，权重根据GA运算符进行更新，例如排名（适应度值），选择和重组（交叉和变异）。这项研究的目的是双重的。第一个目标是应用和研究两种不同的NN训练算法（BP和GA）作为NN预测模型的有效性。通过使用每种算法来预测田纳西州中部两个水处理厂（WTP）的凝结剂剂量以及使用USEPA信息收集规则中的数据预测消毒副产物（DBP，即THM4和HAA5）的形成，进行比较。 （ICR）。第二个目标是记录本项目中使用的NN建模程序，以便将它们用作开发针对水行业的特定于站点的NN模型的基础。结果表明，GANN可以避免局部极小值并有效地产生全局解。一旦成功避免了局部最小值，BPNN模型就可以为凝血剂量和DBP模型产生更好的相关系数（r）。两个WTP的平均r值分别约为0.89，而THM4和HAA5模型的r值分别约为0.94和0.88。发现来自HAA5模型的r值较低（0.88），主要是由于HAA5测试数据子集中的不确定性误差。总体而言，BPNN和GANN都成功地捕获了预测凝血剂量和DBP形成所涉及的复杂非线性关系。

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