Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach

Prediction of sludge bulking is a matter of growing importance around the world. Sludge volume index (SVI) should be monitored to predict sludge bulking for a wastewater treatment plant. This study was an effort to develop hybrid artificial neural network-genetic algorithm models (MLPANN-GA and RBFANN-GA) to accurately predict SVI. Operating parameters, including MLVSS, pH, DO, temperature, TSS, COD and total nitrogen were the inputs of neural networks. Genetic algorithm was utilized in order to optimize weights and thresholds of the MLPANN and RFBANN models. Training procedures for SVI estimation were successful for both the MLPANN-GA and RBFANN-GA models. The training and validation models showed an almost perfect match between experimental and predicted values of SVI. The results indicated that with low experimental values of input data to train ANNs, the MLPANN-GA compared with the RBFANN-GA is more accurate due to higher coefficient of determination (R²) and lower root mean squared error (RMSE) values. The values of RMSE and R² for the optimal models approached 0 and 1, respectively. The mean average error for the ANN models did not exceed 3% of the input values of the measured SVI. The GA increased the accuracy of all the MLPANN and RBFANN models.     

The Integration of a Genetic Programming-Based Feature Optimizer With Fisher Criterion and Pattern Recognition Techniques to Non-Intrusive Load Monitoring for Load Identification

Identification of electricity energy consumption on individual household appliances used in a smart house is the first important step for making the use and conservation of electricity energy more efficient. In the past, Non-Intrusive Load Monitoring (NILM) techniques, which are part of smart grid techniques realized to improve electricity energy usage efficiency, have been developed to identify individual appliances with avoiding installing many smart meters for appliances in a field. In this paper, a new NILM technique that integrates an efficient Genetic Programming (GP)-based feature optimizer with pattern recognition techniques is proposed to identify which appliance is being turned on or off. The proposed GP-based feature optimizer with Fisher criterion is used to generate a more efficient feature than original potential transient features extracted from captured transient response of household appliances through analysis of NILM. The new feature generated by GP is used by pattern recognition techniques as load identifiers for load identification. The load identifiers used and compared in this paper include k-Nearest-Neighbor Rule, Back-Propagation Artificial Neural Network, and Learning Vector Quantization. Experiments are conducted under different single-load and multiple-load operation circumstances at different actual experimental environments with small disturbances. As shown from the experimental results, the proposed is confirmed to be feasible and usable.     

Maximum likelihood fitting of the Poisson lognormal distribution

In this paper some properties and analytic expressions regarding the Poisson lognormal distribution such as moments, maximum likelihood function and related derivatives are discussed. The author provides a sharp approximation of the integrals related to the Poisson lognormal probabilities and analyzes the choice of the initial values in the fitting procedure. Based on these he describes a new procedure for carrying out the maximum likelihood fitting of the truncated Poisson lognormal distribution. The method and results are illustrated on real data. The computer program for calculations is freely available.

环境空气质量指数计算方法与分级方案比较

环境空气质量指数(AQI)广泛应用于各国环境空气质量评价体系之中,旨在为公众提供简单明了的空气质量信息和健康指引。通过对不同国家和地区空气质量指数的名称、计算方法、分级方案进行深入分析和比较,系统分析了各种空气质量指数的优缺点,并针对我国当前的空气质量指数体系存在的问题提出了相应的改进建议。     

基于遗传算法的大气污染总量控制新方法

提出一种利用遗传算法进行大气污染总量控制的新方法,并以柳州市SO2总量控制为例说明这种方法的具体运用.遗传算法具备自适应全局搜索寻优特点,从控制点浓度推算源强分布,该源强分布满足总量控制的根本要求.该方法的具体实现是:将区域内各污染源的排放量编码为染色体,让染色体群体在模拟的进化环境下按生物进化规律进行优胜劣汰的自然选择,经过若干代的进化,最终得到的最优个体即代表最佳的源强分布.研究结果表明,这种方法是有效的和可行的.     

Kinship and aggression: do house sparrows spare their relatives?

Kin-selection theory predicts that relatedness may reduce the level of aggression among competing group members, leading to indirect fitness benefits for kin-favoring individuals. To test this hypothesis, we investigated whether relatedness affects aggressive behavior during social activities in captive house sparrow (Passer domesticus) flocks. We found that sparrows did not reduce their aggression towards kin, as neither the frequency nor the intensity of fights differed between close kin and unrelated flock-mates. Fighting success was also unrelated to kinship and the presence of relatives in the flock did not influence the birds’ dominance rank. These results suggest that the pay-offs of reduced aggression towards kin may be low in non-breeding flocks of sparrows, e.g. due to competition among relatives as predicted by a recent refinement of kin-selection theory. Our findings indicate that the significance of kin selection may be restricted in some social systems such as winter aggregations of birds. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

遗传算法在度约束最小生成树问题中的应用

遗传算法是一类借鉴自然界生物种群“自然选择、优胜劣汰”进化过程的启发式随机搜索优化算法,具通用性好、鲁棒性强等特点,被广泛地应用于通信网络设计领域,如度约束最小生成树问题．通过讨论遗传算法在度约束最小生成树问题中的应用,并对遗传算法在应用时存在的问题和挑战提出了见解．参17．     

Irreversible Investment in Wetlands Preservation: Optimal Ecosystem Restoration Under Uncertainty

The model, a stochastic dynamic program, is used to optimize the timing and type of protective structure under a range of management goals. A wetland can either be optimal for fish or optimal for mammals and waterfowl, but not both. Because credible estimates of the economic values of wetland services do not exist, we treat those values as parameters in a multiobjective analysis and show the decisions implied by alternative valuations. The model is applied to the case of Metzger Marsh, a Lake Erie coastal wetland near Toledo, Ohio, where the decision was made in 1993 to construct an open dike. We find that the optimal decision is robust with respect to varying assumptions about the formation of barrier beaches and the probability of climate change, but that the decision is not robust to assumptions concerning the health of an unprotected Metzger Marsh. The most important source of uncertainty is the biological health of an unprotected wetland.     

应用遗传算法优化氢气/空气燃烧反应系数

研究目的是优化氢气/空气简化反应机理的系数,使得简化机理在一定的误差范围内,能代替详细机理.通过计算氢气/空气详细反应机理得到的组分浓度作为遗传算法的适应度目标,对简化机理进行优化.结果显示,优化后的简化机理不仅在在计算组分浓度,而且在层流预混火焰速度和组分时空分布方面,更加接近详细机理所能达到的计算结果,比优化之前的简化反应机理计算结果更加准确.     

Impacts of organic carbon availability and recipient bacteria characteristics on the potential for TOL plasmid genetic bioaugmentation in soil slurries

The effectiveness of genetic bioaugmentation relies on efficient plasmid transfer between donor and recipient cells as well as the plasmid’s phenotype in the recipient cell. In the present study, the effects of varying organic carbon substrates, initial recipient-to-donor cell density ratios, and mixtures of known recipient bacterial strains on the conjugation and function of a TOL plasmid were tested in sterile soil slurry batch reactors. The presence of soil organic carbon was sufficient in ensuring TOL plasmid transconjugant occurrence (up to 2.1 ± 0.5%) for most recipient strains in soil slurry batch mating experiments. The addition of glucose had limited effects on transconjugant occurrence; however, glucose amendment increased the specific toluene degradation rates of some Enterobacteriaceae transconjugants in soil slurry. Initial cell density ratios and mixtures of recipient strains had smaller impacts on plasmid conjugation and resulting phenotype functionality. These observations suggest that genetic bioaugmentation may be improved by minimal altering of environmental conditions.