Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks

A sequencing batch reactor was modeled using multi-layer perceptron and radial basis function artificial neural networks (MLPANN and RBFANN). Then, the effects of influent concentration (IC), filling time (FT), reaction time (RT), aeration intensity (AI), SRT and MLVSS concentration were examined on the effluent concentrations of TSS, TP, COD and NH₄⁺-N. The results showed that the optimal removal efficiencies would be obtained at FT of 1 h, RT of 6 h, aeration intensity of 0.88 m³/min and SRT of 30 days. In addition, COD and TSS removal efficiencies decreased and TP and NH₄⁺-N removal efficiencies did not change significantly with increases of influent concentration. The TSS, TP, COD and NH₄⁺-N removal efficiencies were 86%, 79%, 94% and 93%, respectively. The training procedures of all contaminants were highly collaborated for both RBFANN and MLPANN models. The results of training and testing data sets showed an almost perfect match between the experimental and the simulated effluent of TSS, TP, COD and NH₄⁺-N. The results indicated that with low experimental values of input data to train ANNs the MLPANN models compared to RBFANN models are more precise due to their higher coefficient of determination (R²) and lower root mean squared errors (RMSE) values.     

多级模糊综合评判方法在泥石流评价中的应用

介绍了多级模糊综合评判方法在泥石流评价中的应用, 指出了在进行模糊评判的时候,评判因子的选取以及隶属函数的构造应具体结合泥石流自身的特点来进行,并应用此方法对西南某拟建水电站坝址附近泥石流沟危险性做出了评价,评价结果与实际调查结果比较吻合.     

一种多介质层波形潜流人工湿地处理重污染河水试验

我国河流污染严重,城市黑臭水体的比率日益增加。人工湿地作为一种高效低耗能的绿色处理技术,已被广泛应用于污染河水的治理。通过对比分析分层装填复合填料的多介质层波形潜流人工湿地和单一砾石填料的普通人工湿地处理重污染河水的效果,研究了特种基质填料强化脱氮除磷作用以及不同水力负荷条件的影响。试验结果表明:在水力负荷为0.2 m~3/(m~2·d)时,普通湿地对COD、NH3-N、TN和TP的去除率分别达到55.95%、77.85%、39.80%和54.94%;多介质层湿地对COD、NH3-N、TN和TP的去除率分别达到58.52%、75.99%、67.08%和57.31%。在水力负荷为0.5 m~3/(m~2·d)时,普通湿地对COD、NH3-N、TN和TP的去除率分别达到68.46%、90.73%、42.13%和53.70%;多介质层湿地对COD、NH3-N、TN和TP的去除率分别达到69.61%、89.94%、77.58%和68.63%,其脱氮效果明显好于普通湿地。以复合填料包分层装填的波形潜流人工湿地系统具有良好的脱氮除磷效果,有很好的推广应用价值。     

多层填料生物转鼓去除NO废气的效能比较研究

生物转鼓过滤器能有效去除一氧化氮(NO)废气,为进一步提高生物转鼓过滤器的去除效能,实验改变了生物转鼓的填料结构,并与单层填料的生物转鼓进行了比较研究.结果表明,多层填料生物转鼓比单层填料生物转鼓更能有效去除NO,运行也更加稳定.8个月的连续运行实验表明,多层填料生物转鼓对NO去除率稳定在53.9％ ～93.4％之间,平均去除效率79.8％,而单层填料生物转鼓的平均去除率仅有68.7％;在相同实验条件下,空床停留时间(EBRT)可从单层填料生物转鼓的86.4 s降至多层填料生物转鼓的57.6 s.多层填料生物转鼓的最优工艺条件为,营养液量为1.3～3L,转速为0.75 r·min-1,在以葡萄糖为碳源时,TOC＞ 1250 mg·L-1后,去除效率增长幅度趋于平缓.     

Anaerobic tapered fluidized bed reactor for starch wastewater treatment and modeling using multilayer perceptron neural network

Anaerobic treatability of synthetic sago wastewater was investigated in a laboratory anaerobic tapered fluidized bed reactor(ATFBR) with a mesoporous granular activated carbon(GAC)as a support material.The experimental protocol was defined to examine the effect of the maximum organic loading rate(OLR),hydraulic retention time(HRT),the efficiency of the reactor and to report on its steady- state performance.The reactor was subjected to a steady-state operation over a range of OLR up to 85.44 kg COD/(m~3.d).The COD removal efficiency was found to be 92% in the reactor while the biogas produced in the digester reached 25.38 m~3/(m~3·d) of the reactor. With the increase of OLR from 83.7 kg COD/(m~3.d),the COD removal efficiency decreased.Also an artificial neural network(ANN) model using multilayer perceptron(MLP)has been developed for a system of two input variable and five output dependent variables. For the training of the input-output data,the experimental values obtained have been used.The output parameters predicted have been found to be much closer to the corresponding experimental ones and the model was validated for 30% of the untrained data.The mean square error(MSE)was found to be only 0.0146.     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.     

物理气相沉积硬质耐腐蚀磨损防护涂层研究进展

叙述了用物理气相沉积技术制备的硬质耐腐蚀磨损防护涂层的研究进展。重点介绍了腐蚀磨损耦合工况特性和作用机理,单元、多元掺杂氮化物涂层,高熵氮化物涂层,多层、纳米多层、纳米复合、梯度氮化物涂层耐腐蚀磨损性能研究,非金属元素/金属元素掺杂DLC涂层、多层复合DLC涂层耐腐蚀磨损性能研究,并对涂层改性、强化机理和腐蚀磨损失效机制进行了总结。最后,提出了未来物理气相沉积硬质耐腐蚀磨损防护涂层重点发展的方向,应重点发展涂层组元/结构设计、力–电耦合损伤机理研究、高效率研发设计、涂层先进制备装备与评估体系等方面的能力。     

Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)

Leakage diagnosis of hydrocarbon pipelines can prevent environmental and financial losses. This work proposes a novel method that not only detects the occurrence of a leakage fault, but also suggests its location and severity. The OLGA software is employed to provide the pipeline inlet pressure and outlet flow rates as the training data for the Fault Detection and Isolation (FDI) system. The FDI system is comprised of a Multi-Layer Perceptron Neural Network (MLPNN) classifier with various feature extraction methods including the statistical techniques, wavelet transform, and a fusion of both methods. Once different leakage scenarios are considered and the preprocessing methods are done, the proposed FDI system is applied to a 20-km pipeline in southern Iran (Goldkari-Binak pipeline) and a promising severity and location detectability (a correct classification rate of 92%) and a low False Alarm Rate (FAR) were achieved.     

Progress in developing an ANN model for air pollution index forecast

An air pollution index (API) reporting system is introduced to selected cities of China for public communication on air quality data. Shanghai is the first city in China providing daily average API reports and forecasts. This paper describes the development of an artificial neural network (ANN) model for the API forecasting in Shanghai. It is a multiple layer perceptron (MLP) network, with meteorological forecasting data as the main input, to output the next day average API values. However, the initial version of the MLP model did not work well. To improve the model, a series of tests were conducted with respect to the training method and structure optimization. Based on the test results, the training algorithm was modified and a new model was built. The new model is now being used in Shanghai for API forecasting. Its performance is shown reasonably well in comparison with observation. The application of the old model was only weakly correlated with observation. In 1-year application, the correlation coefficients were 0.2314, 0.1022 and 0.1710 for TSP, SO₂ and NO_x, respectively. But for the new model, for over 8 months application, the correlation coefficients are raised to 0.6056, 0.6993 and 0.6300 for PM₁₀, SO₂, and NO₂. Further, the new algorithm does not rely on manpower intervention so that it is now being applied in several other Chinese cities with quite different meteorological conditions. The structure of the model and the application results are presented in this paper and also the problems to be further studied.     

海上突发灾难事件应急物资调度优化方法研究

为满足海上突发灾难事件救援对应急物资的需求,最大限度降低其带来的损失,根据海上突发灾难事件的特点,提出救助目标在受复杂气象、海况等因素影响发生漂移/扩散时的海上突发灾难事件应急物资调度方法。综合考虑海洋环境影响,引入救助资源包的概念。构建考虑时间、运力、物资需求等约束条件,以最大限度地满足物资供应可靠性为目标的海上突发灾难事件多级应急物资调度优化模型。通过算例对模型的应用进行验证。研究结果表明,事件形态的演变会影响应急物资集结点的选择,需要在预测事件形态时空变化的基础上合理确定海上应急物资调度方案。