污水厂污泥与厨余垃圾厌氧/混合厌氧消化研究进展

本文主要对国内外城市污水厂污泥与厨余垃圾混合厌氧消化的研究进行了综述,介绍了厌氧消化技术在污水厂污泥和厨余垃圾处理处置中的应用,对两种废物单独厌氧消化和混合厌氧消化技术进行了比较,分析了城市污水厂污泥与厨余垃圾混合厌氧消化的可行性以及工艺参数对混合厌氧消化的影响,并对城市污水厂污泥与厨余垃圾的混合厌氧消化技术的研究和应用提出了展望。     

城市生活垃圾中可生化单基质的厌氧消化

在厌氧消化系统中，发酵细菌最主要的利用基质是淀粉、纤维素、脂肪和蛋白质等。本试验分别选取米饭、黄豆、芹菜和肥肉为上述四种基质的代表物质，通过对各物质进行的厌氧消化试验，探讨了它们的厌氧消化性能。结果表明。米饭在发酵初期降解速率最快，酸化也最明显。同时将消化初始阶段的pH控制在6．5时能使消化进入产甲烷阶段，没控制的只能停留在水解产酸阶段。将黄豆厌氧消化初期的pH控制在6．5并不能使消化顺利进行。将芹菜厌氧消化的初始pH值控制在6．5时可以加快它的降解速率。肥肉的厌氧消化也只有在对其消化进程控制pH值时才能顺利被降解。同时肥肉在厌氧消化进程中表现出了高产甲烷性能，产甲烷阶段累积产气量达13758mL，占总产气量的93．59％．     

厌氧消化产气预测模型研究进展

综述了厌氧消化产气预测模型的研究现状,通过论述线性回归模型、动力学模型、人工神经网络模型的应用研究进展及存在问题,并重点介绍人工神经网络产气预测模型,对其发展趋势进行展望,以期为今后产气预测模型的选择提供指导,为模型在实际工程中的应用提供依据。     

污泥与餐厨垃圾联合厌氧技术的研究及应用

对国内外城市污水污泥与餐厨垃圾混合厌氧消化的研究进行了综述,介绍了厌氧消化技术在以上两种废弃物处理处置中的不足,提出了污水污泥与餐厨垃圾联合厌氧消化的可行性,分析了该技术的研究及应用现状,并对未来的研究热点及方向进行了展望。     

污泥高固体厌氧消化研究进展

厌氧消化是实现污泥的减量化、稳定化和资源化的重要手段,相对于传统的低浓度污泥厌氧消化工艺高固体污泥厌氧消化可以直接利用污水处理厂排放的脱水污泥,具有设施体积小、单位容积产气率较高和水耗及能耗较低等优势。本文综述了近年来污泥高固体消化的研究进展,从污泥高固体厌氧消化的基本特征出发,总结了污泥高固体厌氧消化的影响因素和对反应器的要求;同时对污泥高固体消化存在的搅拌不匀、传质传热困难、有机质降解率偏低、搅拌系统不成熟等问题作了简要分析,这些问题都还有待于深入研究解决。     

碱解处理对剩余污泥融胞效果及厌氧消化产气效果

主要研究了碱解处理的pH值对剩余污泥的融胞效果及厌氧消化产气效果的影响.通过观测碱处理污泥上清液中的SCOD以及溶解性蛋白质,发现两者变化规律相似,其融胞效果随pH值的升高,即碱投加量的增加而增加.此外随着pH值的升高,VS的去除率随之增加.当pH为11时,48h VS去除率可达将近22%.碱解处理后污泥的后续厌氧消化的特性可得到明显改善.碱解处理调节pH值分别为pH9、pH10、pH11的污泥进行厌氧消化30d总甲烷产量与未经碱解处理的污泥相比较,总甲烷产量分别提高了8%、23%、41%.其中经碱解处理至pH值为11的污泥的COD去除率为32%.     

基于BP-Adaboost强预测器的瓦斯涌出量预测

为了提高传统BP神经网络瓦斯涌出量预测模型精度,避免BP网络容易陷入局部极值、收敛速度慢等问题,将BP神经网络和Adaboost算法相结合,提出了一种BP-Adaboost强预测器模型.将该模型用于实际瓦斯涌出量预测,并进行了40次仿真实验.结果表明:该模型预测精度高于传统的BP神经网络,且收敛速度快,具有较强的鲁棒性,预测精度能满足实际工程需要,为瓦斯涌出量预测提供了一种新的途径.     

垃圾场渗出污水厌氧处理的可行性研究

采用血清瓶静态试验法研究了垃圾渗出污水的厌氧处理的可行性。试验表明:垃圾渗出污水对厌氧发酵微生物无抑制作用,有良好的厌氧降解性,产气率为0.321/g·COD,污水中的大部份有机物都能被厌氧消化,COD_cr去除率为78.2%,发酵过程中还能获得能源——沼气。具有良好的应用前景。     

餐厨垃圾厌氧消化系统酸化调控研究进展

介绍了餐厨垃圾的特点，针对餐厨垃圾在厌氧消化过程中容易出现的酸化抑制，梳理了目前常用的缓冲溶液添加、多元物料混合厌氧消化和两相厌氧消化等酸化调控技术，以及处于研发阶段的生物预处理、添加生物炭、驯化培养耐酸型产甲烷菌等酸化调控技术。比较了各种技术的优势与不足，提出未来的技术路径和研究方向，以期为餐厨垃圾厌氧消化工程的稳定运行提供技术支撑。     

PAM投加量对臭氧氧化厌氧消化耦合工艺污泥脱水性的影响

提高污泥的脱水性是降低污泥处理难度的必要前提.本文以传统厌氧消化工艺为参照,研究臭氧氧化厌氧消化耦合工艺对化学一级强化污泥脱水挂的影响,以及PAM投加量对厌氧消化污泥脱水性的影响.结果表明,与浓缩污泥相比,传统厌氧消化后污泥脱水性下降了11.8％,而臭氧氧化厌氧消化耦合工艺中污泥没有变化;随着PAM投加量的增加,污泥的脱水性能显著提高;当PAM梗加量为2.0 mg/g DS时,臭氧氧化厌氧消化耦合工艺中污泥的脱水性能达到最佳状态,而当PAM投加量为2.5 mg/g DS时,厌氧消化工艺中污泥的脱水性能才达到最佳状态.     

基于Adaboost集成BP网络原煤生产成本预测研究

为了提高传统BP神经网络预测模型精度,避免BP网络容易陷入局部极值、收敛速度慢等问题,将BP神经网络与Ada-boost算法相结合,提出了一种Adaboost集成BP神经网络模型.结合磁县观台煤矿原煤生产成本相关数据,建立了原煤生产成本预测的Adaboost集成BP神经网络模型,将该模型用于实际的原煤成本预测.结果表明：该模型预测精度高于传统的BP神经网络,收敛速度快,具有较强的鲁棒性,预测精度能满足实际预测需要,为原煤生产成本预测提供了一种新的途径,也为原煤生产成本控制提供了重要依据.     

修正的BP神经网络森林火灾成灾面积预测研究

以1997-2012年《中国林业统计年鉴》的全国森林火灾成灾面积为依据,应用BP神经网络模型对成灾面积进行了预测,对预测方法进行了检验.在此基础上,利用残差提出了修正的BP神经网络模型,并对预测方法进行了改进.研究结果表明,修正的BP神经网络预测精度高于BP神经网络,预测相对误差平均为7.2％,可应用于森林火灾成灾面积的预测.     

Bio-Hydrogen Synthesis from Wastewater by Anaerobic Fermentation Using Microflora

Biological hydrogen production was investigated using biomass in palm oil mill effluent (POME) and artificial wastewater containing 10g glucose under anaerobic fermentation in a batch process. Activated POME sludge and different types of composts were collected as sources of inocula for the study. The anaerobic microflora was found to yield significant amounts of hydrogen. The experimental results show that the gas composition contained hydrogen (66–68%) and carbon dioxide (32–34%). Through out the study, methane gas was not observed in the evolved gas. The hydrogen production was accompanied with the formation of acetate and butyrate. Furthermore, the cumulative hydrogen data were fitted to a simple model developed from Gompertz Equation, where the lag phase time, hydrogen production potential and hydrogen production rate at various conditions were quantitatively estimated.     

Improving Predictive Models of In‐Stream Phosphorus Concentration Based on Nationally‐Available Spatial Data Coverages

Spatial data are playing an increasingly important role in watershed science and management. Large investments have been made by government agencies to provide nationally‐available spatial databases; however, their relevance and suitability for local watershed applications is largely unscrutinized. We investigated how goodness of fit and predictive accuracy of total phosphorus (TP) concentration models developed from nationally‐available spatial data could be improved by including local watershed‐specific data in the East Fork of the Little Miami River, Ohio, a 1,290 km² watershed. We also determined whether a spatial stream network (SSN) modeling approach improved on multiple linear regression (nonspatial) models. Goodness of fit and predictive accuracy were highest for the SSN model that included local covariates, and lowest for the nonspatial model developed from national data. Septic systems and point source TP loads were significant covariates in the local models. These local data not only improved the models but enabled a more explicit interpretation of the processes affecting TP concentrations than more generic national covariates. The results suggest SSN modeling greatly improves prediction and should be applied when using national covariates. Including local covariates further increases the accuracy of TP predictions throughout the studied watershed; such variables should be included in future national databases, particularly the locations of septic systems.     

Multiple regression models for the lower heating value of municipal solid waste in Taiwan

A multiple regression analysis was used to develop two predictive models of lower heating value (LHV) for municipal solid waste (MSW), using 180 samples gathered from cities and counties in Taiwan during 2001-2002. These models are referred to as the original proposed model (OPM) and the simplified model (SM). The coefficients of multiple determinations for the OPM and SM were 0.983 and 0.975, respectively. To verify the feasibility of the models, a demonstration program based on sampling of MSW in Kaohsiung City was conducted. As a result, the OPM showed superior precision in terms of relative percentage deviation (RPD) and mean absolute percentage error (MAPE), when compared to the conventional models based on the proximate analysis, physical composition and ultimate analysis. The SM was derived by neglecting the three minor physical components used in the OPM. The resulting SM was less precise when compared to the OPM, but it was still acceptable, with a precision level better than the conventional models. It was concluded that the predictability of empirical models could be improved significantly through selection of the appropriate physical components and multiple regression analysis.     

Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool

Traditionally, the multiple linear regression technique has been one of the most widely used models in simulating hydrological time series. However, when the nonlinear phenomenon is significant, the multiple linear will fail to develop an appropriate predictive model. Recently, neuro-fuzzy systems have gained much popularity for calibrating the nonlinear relationships. This study evaluated the potential of a neuro-fuzzy system as an alternative to the traditional statistical regression technique for the purpose of predicting flow from a local source in a river basin. The effectiveness of the proposed identification technique was demonstrated through a simulation study of the river flow time series of the Citarum River in Indonesia. Furthermore, in order to provide the uncertainty associated with the estimation of river flow, a Monte Carlo simulation was performed. As a comparison, a multiple linear regression analysis that was being used by the Citarum River Authority was also examined using various statistical indices. The simulation results using 95% confidence intervals indicated that the neuro-fuzzy model consistently underestimated the magnitude of high flow while the low and medium flow magnitudes were estimated closer to the observed data. The comparison of the prediction accuracy of the neuro-fuzzy and linear regression methods indicated that the neuro-fuzzy approach was more accurate in predicting river flow dynamics. The neuro-fuzzy model was able to improve the root mean square error (RMSE) and mean absolute percentage error (MAPE) values of the multiple linear regression forecasts by about 13.52% and 10.73%, respectively. Considering its simplicity and efficiency, the neuro-fuzzy model is recommended as an alternative tool for modeling of flow dynamics in the study area.     

Predicting Debris Yield From Burned Watersheds: Comparison of Statistical and Artificial Neural Network Models1

Abstract: Alluvial fans in southern California are continuously being developed for residential, industrial, commercial, and agricultural purposes. Development and alteration of alluvial fans often require consideration of mud and debris flows from burned mountain watersheds. Accurate prediction of sediment (hyper‐concentrated sediment or debris) yield is essential for the design, operation, and maintenance of debris basins to safeguard properly the general population. This paper presents results based on a statistical model and Artificial Neural Network (ANN) models. The models predict sediment yield caused by storms following wildfire events in burned mountainous watersheds. Both sediment yield prediction models have been developed for use in relatively small watersheds (50‐800 ha) in the greater Los Angeles area. The statistical model was developed using multiple regression analysis on sediment yield data collected from 1938 to 1983. Following the multiple regression analysis, a method for multi‐sequence sediment yield prediction under burned watershed conditions was developed. The statistical model was then calibrated based on 17 years of sediment yield, fire, and precipitation data collected between 1984 and 2000. The present study also evaluated ANN models created to predict the sediment yields. The training of the ANN models utilized single storm event data generated for the 17‐year period between 1984 and 2000 as the training input data. Training patterns and neural network architectures were varied to further study the ANN performance. Results from these models were compared with the available field data obtained from several debris basins within Los Angeles County. Both predictive models were then applied for hind‐casting the sediment prediction of several post 2000 events. Both the statistical and ANN models yield remarkably consistent results when compared with the measured field data. The results show that these models are very useful tools for predicting sediment yield sequences. The results can be used for scheduling cleanout operation of debris basins. It can be of great help in the planning of emergency response for burned areas to minimize the damage to properties and lives.     

人工神经网络法预测城市用水量

城市用水量的预测结果,对于城市规划、供水系统的管理及改扩建有着重要的意义,寻求科学合理的预测模型是保障预测结果准确可靠的关键。针对这一问题,利用人工神经网络理论建立了BP（Back—Propagation,反向传播算法）网络预测模型,该模型考虑了反映社会、经济的两个影响因素人口和工业产值对用水量需求的影响,具备系统决策功能。通过实例证明该模型是一种行之有效的用水量预测模型。     

An evaluation of the predictive performance of distributional models for flora and fauna in north-east New South Wales.

To use models of species distributions effectively in conservation planning, it is important to determine the predictive accuracy of such models. Extensive modelling of the distribution of vascular plant and vertebrate fauna species within north-east New South Wales has been undertaken by linking field survey data to environmental and geographical predictors using logistic regression. These models have been used in the development of a comprehensive and adequate reserve system within the region. We evaluate the predictive accuracy of models for 153 small reptile, arboreal marsupial, diurnal bird and vascular plant species for which independent evaluation data were available. The predictive performance of each model was evaluated using the relative operating characteristic curve to measure discrimination capacity. Good discrimination ability implies that a model's predictions provide an acceptable index of species occurrence. The discrimination capacity of 89% of the models was significantly better than random, with 70% of the models providing high levels of discrimination. Predictions generated by this type of modelling therefore provide a reasonably sound basis for regional conservation planning. The discrimination ability of models was highest for the less mobile biological groups, particularly the vascular plants and small reptiles. In the case of diurnal birds, poor performing models tended to be for species which occur mainly within specific habitats not well sampled by either the model development or evaluation data, highly mobile species, species that are locally nomadic or those that display very broad habitat requirements. Particular care needs to be exercised when employing models for these types of species in conservation planning.     

Modelling and Prediction of Diesel Engine Performance using Relevance Vector Machine

Diesel engines are being increasingly adopted by many car manufacturers today, yet no exact mathematical diesel engine model exists due to its highly nonlinear nature. In the current literature, black-box identification has been widely used for diesel engine modelling and many artificial neural network (ANN) based models have been developed. However, ANN has many drawbacks such as multiple local minima, user burden on selection of optimal network structure, large training data size, and over-fitting risk. To overcome these drawbacks, this article proposes to apply an emerging machine learning technique, relevance vector machine (RVM), to model and predict the diesel engine performance. The property of global optimal solution of RVM allows the model to be trained using only a few experimental data sets. In this study, the inputs of the model are engine speed, load, and cooling water temperature, while the output parameters are the brake-specific fuel consumption and the amount of exhaust emissions like nitrogen oxides and carbon dioxide. Experimental results show that the model accuracy is satisfactory even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.