A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

● A novel VMD-IGOA-LSTM model has proposed for the prediction of water quality. ● Improved model quickly converges to the global optimal fitness and remains stable. ● The prediction accuracy of water quality parameters is significantly improved. Water quality prediction is vital for solving water pollution and protecting the water environment. In terms of the characteristics of nonlinearity, instability, and randomness of water quality parameters, a short-term water quality prediction model was proposed based on variational mode decomposition (VMD) and improved grasshopper optimization algorithm (IGOA), so as to optimize long short-term memory neural network (LSTM). First, VMD was adopted to decompose the water quality data into a series of relatively stable components, with the aim to reduce the instability of the original data and increase the predictability, then each component was input into the IGOA-LSTM model for prediction. Finally, each component was added to obtain the predicted values. In this study, the monitoring data from Dayangzhou Station and Shengmi Station of the Ganjiang River was used for training and prediction. The experimental results showed that the prediction accuracy of the VMD-IGOA-LSTM model proposed was higher than that of the integrated model of Ensemble Empirical Mode Decomposition (EEMD), the integrated model of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Nonlinear Autoregressive Network with Exogenous Inputs (NARX), Recurrent Neural Network (RNN), as well as other models, showing better performance in short-term prediction. The current study will provide a reliable solution for water quality prediction studies in other areas.     

Flocculation behaviors of a novel papermaking sludge-based flocculant in practical printing and dyeing wastewater treatment

•PSBF performed better than PAC and PAM in CODCr removals. •PSBF was more insensitive to changing pH than PAC and PAM. •PAC could remove humic acid-like pollutants and dye particles. •PSBF was efficient in removing tryptophan-like pollutants from PPDW. •A secondary coagulation-flocculation process (PAC→PSBF) is proposed here. In our previous studies, several papermaking sludge-based flocculants (PSBFs) were synthesized from wood pulp papermaking sludge. The structure-activity relationships of the PSBFs have been investigated in simulated dye wastewater treatment, but their efficiencies in practical printing and dyeing wastewater (PPDW) treatment are unknown. Herein, an PSBF was prepared, and its performance is discussed in comparison to polyaluminium chloride (PAC) and polyacrylamide (PAM) in PPDW treatment. The PSBF was used in three ways: as an independent flocculant, as a PAC aid, or used to treat the effluent of the PAC system. The results indicated that adding PSBF alone produced similar color and chemical oxygen demand (COD_Cr) removals as the PAC system alone, but PSBF performed better than PAC when the pH of PPDW was higher than 7.0. Adding PSBF as a PAC aid improved the color, COD_Cr and turbidity removals, but the elimination efficiencies were slightly lower than those of the PAC+ PAM system. However, when PSBF was used as a flocculant to treat the effluent of the PAC system (PAC→PSBF), the effluent qualities were enhanced. Compared with the PAC system, the color and COD_Cr removals of PAC→PSBF system increased by 16.21% and 13.26%, respectively. The excitation and emission matrix fluorescence results indicated that PSBF removed tryptophan-like pollutants more efficiently than PAC. Considering the pH requirements of the subsequent bioreactor treatment in practice, the PAC→PSBF system were also investigated at the PPDW pH level of 7.0. Its maximum removal efficiencies of color, COD_Cr and turbidity were 90.17%, 32.60% and 82.50%, respectively.     

基于人工神经网络的城市用水需求组合预测

城市用水需求预测是涉及到诸多要素的复杂系统预测问题。为了减少简单外推法预测所带来的误差,通过在训练BP神经网络时自动调整学习步长和添加动量项修正神经单元之间的权重,既提高了神经网络的收敛速度,又抑制了神经网络限于局部极小现象的发生;然后使用改进的BP神经网络寻找多元回归预测、径向基函数(RBF)神经网络和改进BP神经网络3个单项预测的最佳组合,来综合各项独立预测所包含的信息,并以条件假设按照参考、高、低3个方案预测分析某城市的用水需求情况,说明这种基于人工神经网络的组合预测方法在预测城市用水需求量时是一个准确高效的方法。     

应用于水文预报的优化BP神经网络研究

利用广东省滨江流域的水文观测资料，建立了以前期降水量为预报因子、以水位为输出的BP人工神经网络水文预报模型。首先采用了合理的方法进行样本组织，进而利用最优子集回归技术进行输入因子的确定，然后进行了不同隐层节点数、不同转移函数、不同训练算法的组合试验，确定了应用于水文预报中的优化BP神经网络：网络结构为8-9-1；转移函数的组合方式为tansig-线性函数；训练算法为采用evenberg-Marquardt(Lm)算法。为便于精度分析，还采用了最优子集回归模型作了研究。结果表明，优化BP网络模型无论在拟合精度还是在预测精度上都高于最优子集模型。总的来说BP网络是一种精度较高的水文预测模型。     

Prediction of high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificial neural networks

● Reducting the sampling frequency can enhance the modelling process. ● The pyrolysis of HDPE was investigated at three different heating rates. ● The average E_a and k₀ were calculated by Friedman, KAS, FWO, and CR methods. ● ANN was employed to predict the HDPE weight loss with the optimal MSE and R². Pyrolysis is considered an attractive option and a promising way to dispose waste plastics. The thermogravimetric experiments of high-density polyethylene (HDPE) were conducted from 105 °C to 900 °C at different heating rates (10 °C/min, 20 °C/min, and 30 °C/min) to investigate their thermal pyrolysis behavior. We investigated four methods including three model-free methods and one model-fitting method to estimate dynamic parameters. Additionally, an artificial neural network model was developed by providing the heating rates and temperatures to predict the weight loss (wt.%) of HDPE, and optimized via assessing mean squared error and determination coefficient on the test set. The optimal MSE (2.6297 × 10⁻²) and R² value (R² > 0.999) were obtained. Activation energy and pre-exponential factor obtained from four different models achieves the acceptable value between experimental and predicted results. The relative error of the model increased from 2.4 % to 6.8 % when the sampling frequency changed from 50 s to 60 s, but showed no significant difference when the sampling frequency was below 50 s. This result provides a promising approach to simplify the further modelling work and to reduce the required data storage space. This study revealed the possibility of simulating the HDPE pyrolysis process via machine learning with no significant accuracy loss of the kinetic parameters. It is hoped that this work could potentially benefit to the development of pyrolysis process modelling of HDPE and the other plastics.     

Impact of coal industries on the quality of Damodar river water

Increasing demand for water in domestic, agricultural, and industrial sectors necessitates exploitation of water either in the form of groundwater or from natural resources. To safeguard the long-term sustainability of water resources and their utilization, the quality of water has to be periodically monitored and determined for various characteristics, especially when the sources are polluted, such as Damodar river. Central Institute of Mining and Fuel Research (CIMFR), Dhanbad, is carrying out research work on coal and its utilization and associated environmental concerns. The blood stream of life for the whole Jharia Coalfield is none other than the river Damodar. CIMFR's campus also depends exclusively on river Damodar for meeting its demand of drinking water. This study is a general survey toward the characteristics of Damodar river water, with special emphasis on the pollutant parameters, and evaluation of the treatment process being carried out at the institute for potability. Damodar river water is indeed affected by the disposal of the wastes without any pre-treatment by different coal-based industries established in its basin. The quantity of dissolved and suspended solids, total hardness, chemical oxygen demand, and coliform bacterial count are higher in Damodar water due to the disposal of the waste/effluents from coal-washing plants, coke ovens, cement, and other industries, but well within the permissible limit which is probably attributable to the high-carrying capacity of the river. The river is still not that much affected as it is usually apprehended, and it can be well utilized for potable and domestic purposes after simple treatment.     

Short-term prediction of the influent quantity time series of wastewater treatment plant based on a chaos neural network model

By predicting influent quantity, a wastewater treatment plant (WWTP) can be well controlled. The nonlinear dynamic characteristic of WWTP influent quantity time series was analyzed, with the assumption that the series was predictable. Based on this, a short-term forecasting chaos neural network model of WWTP influent quantity was built by phase space reconstruction. Reasonable forecasting results were achieved using this method.     

Electroreduction of hexavalent chromium using a porous titanium flow-through electrode and intelligent prediction based on a back propagation neural network

● Titanium-based flow-through electrode achieved high Cr(VI) reduction efficiency. ● Flow-through pattern enhanced the mass transfer and reduced cathodic polarization. ● BPNN predicted the optimal electroreduction conditions of flow-through cell. Flow-through electrodes have been demonstrated to be effective for electroreduction of Cr(VI), but shortcomings are tedious preparation and short lifetimes. Herein, porous titanium available in the market was studied as a flow-through electrode for Cr(VI) electroreduction. In addition, the intelligent prediction of electrolytic performance based on a back propagation neural network (BPNN) was developed. Voltametric studies revealed that Cr(VI) electroreduction was a diffusion-controlled process. Use of the flow-through mode achieved a high limiting diffusion current as a result of enhanced mass transfer and favorable kinetics. Electroreduction of Cr(VI) in the flow-through system was 1.95 times higher than in a parallel-plate electrode system. When the influent (initial pH 2.0 and 106 mg/L Cr(VI)) was treated at 5.0 V and a flux of 51 L/(h·m²), a reduction efficiency of ~99.9% was obtained without cyclic electrolysis process. Sulfate served as the supporting electrolyte and pH regulator, as reactive CrSO₇²⁻ species were formed as a result of feeding HSO₄⁻. Cr(III) was confirmed as the final product due to the sequential three-electron transport or disproportionation of the intermediate. The developed BPNN model achieved good prediction accuracy with respect to Cr(VI) electroreduction with a high correlation coefficient (R² = 0.943). Additionally, the electroreduction efficiencies for various operating inputs were predicted based on the BPNN model, which demonstrates the evolutionary role of intelligent systems in future electrochemical technologies.     

规模化牛场废水灌溉对土壤水分和冬小麦产量品质的影响

通过田间小区试验,设置不同的牛场废水灌溉次数,研究了冬小麦牛场废水灌溉过程中土壤水分和冬小麦产量品质的变化特征,结果表明,灌溉牛场废水土壤水分迁移和土壤贮水量与灌溉清水无显著差别,水质对土壤水分变化影响很小;冬小麦生育期内分别灌溉牛场废水2、3和4次与正常施肥灌溉施肥相比,冬小麦产量和灌溉水生产效率提高,分别提高了4.61%、6.48%、6.63%,4次牛场废水灌溉冬小麦产量略有下降,这说明灌溉牛场废水次数过多会对冬小麦造成一定的负面影响;牛场废水灌溉次数越多冬小麦籽粒中蛋白质质量分数越高,分别提高了2.50%、5.83%、8.03%,而全磷质量分数则有降低趋势。综合考虑,冬小麦生育期内牛场废水灌溉次数不应高于3次。     

滤层厚度对慢滤池深度处理污水的性能影响

将慢滤池用于污水二级出水的深度处理,并利用小试装置研究了滤层厚度对慢滤池性能的影响。选取浊度、COD和色度三个指标,在滤层不同深度处多次取样,分析各指标沿滤层厚度的变化。结果表明,采用粒径为0．4～0．6mm,滤层厚度为800mm的石英砂做滤床时,慢滤池对二级出水具有较好的净化效果：当进水浊度、COD和色度分别为1.3～6．9NTU、30．4～70．0mg·L^-1和20．6°～57．6°时,平均去除率分别达到86．5％、45．0％和46．3％。从试验结果可以看出,慢滤池类似一个微缩的污水二级处理系统,滤层表面的粘性滤膜起到类似初沉池的作用,可以对各指标实现较好的去除,58．6％的浊度、52．7％的COD和45．7％的色度是在滤层上部去除的;慢滤池中部起到类似曝气池的作用,下部起到类似二沉池的作用,对水质指标也能实现一定的去除。     

邯郸市沁河水污染治理方案

本文对邯郸市沁河水污染现状进行了调查分析,总结了城市内河治理经验,通过方案比较,推荐“清淤,护坡,建坝,分质截污,邯钢废水混凝沉淀处理”为沁河治理方案。     

基于人工神经网络方法的河北省近海沉积物重金属污染综合评价

利用误差反相传播神经(BP)网络对河北省近海沉积物中的铅、镉、锌、汞、砷5种重金属元素的污染水平进行分析,利用自组织特征映射(SOFM)网络对上述重金属元素分布特征进行分类,通过分类与污染水平量化值的结合,进行综合评价。SOFM把52个沉积物样品分别划分为3、4、6类和9类。对比各种分类,分为3类的物理意义较明确,每个类别分别对应高中低不同的污染物浓度水平,差异显著、分类方式比较合理。通过此种分类可以判断河北省近海的沉积物重金属污染在不同海域存在一定的差别,整体上是离海岸越远,沉积物的重金属污染水平越高,距海岸较近的海域内,沉积物的重金属污染水平较低,但渤海湾内的重金属污染水平高于其他海域。     

Artificial neural network modeling of caste odor discrimination based on cuticular hydrocarbons in termites

Summary. Individuals in an insect colony need to identify one another according to caste. Nothing is known about the sensory process allowing nestmates to discriminate minute variations in the cuticular hydrocarbon mixture. The purpose of this study was to attempt to model caste odors discrimination in four species of Reticulitermes termites for the first time by a non-linear mathematical approach using an "artificial neural network" (ANN). Several rounds of testing were carried out using 1 – the whole hydrocarbon mixtures 2 – mixtures containing the hydrocarbons selected by principal component analysis (PCA) as the most implicated in caste discrimination. Discrimination between worker and soldier castes was tested in all four species. For two species we tested discrimination of four castes (workers, soldiers, nymphs, neotenics). To test cuticular pattern similarity in two sibling species (R. santonensis and R. flavipes), we performed two experiments using one species for training and the other for query. Using whole hydrocarbons mixtures, worker/soldier discrimination was always successful in all species. Network performance decreased with the number of hydrocarbons used as inputs. Four-caste discrimination was less successful. In the experiment with the sibling species, the ANN was able to distinguish soldiers but not workers. The results of this study suggest that non-linear mathematical analysis is a good tool for classification of castes based on cuticular hydrocarbon mixture. In addition this study confirms that hydrocarbon mixtures observed are real chemical entities and constitute a true chemical signature or odor. Whole mixtures are not always necessary for discrimination. Received 23 July 1998; accepted 9 October 1998.     

基于RBF神经网络的鄱阳湖表层水体总悬浮颗粒物浓度遥感反演

为进一步提高湖泊总悬浮颗粒物浓度遥感反演的准确性,引进适应复杂非线性映射的RBF神经网络模型,以鄱阳湖通江湖体为例进行了实证分析,根据实测水体悬浮颗粒物浓度和MODIS遥感数据,对遥感数据进行预处理,建立了RBF神经网络悬浮颗粒物浓度反演模型,神经元个数为8个,误差性能目标值为0.001,对悬浮颗粒物浓度进行反演。研究结果表明,验证样本相关系数R2=0.956 8,均方根误差RMSE=0.54。利用神经网络模型反演水悬浮颗粒物浓度是有效的,其反演结果优于非线性回归模型的结果。