Statistical regression modeling for energy consumption in wastewater treatment

Wastewater treatment is one of critical issues faced by water utilities, and receives more and more attentions recently. The energy consumption modeling in biochemical wastewater treatment was investigated in the study via a general and robust approach based on Bayesian semi-parametric quantile regression. The dataset was derived from a municipal wastewater treatment plant, where the energy consumption of unit chemical oxygen demand (COD) reduction was the response variable of interest. Via the proposed approach, the comprehensive regression pictures of the energy consumption and truly influencing factors, i.e., the regression relationships at lower, median and higher energy consumption levels were characterized respectively. Meanwhile, the proposals for energy saving in different cases were also facilitated specifically. First, the lower level of energy consumption was closely associated with the temperature of influent wastewater, and the chroma-rich wastewater also showed helpful in the execution of energy saving. Second, at median energy consumption level, the COD-rich wastewater played a determinative role in the reduction of energy consumption, while the higher quality of treated water led to slightly energy intensive. Third, the higher level of energy consumption was most likely to be attributed to the relatively high temperature of wastewater and total nitrogen (TN)-rich wastewater, and both of the factors were preferably to be avoided to alleviate the burden of energy consumption. The study provided an efficient approach to controlling the energy consumption of wastewater treatment in the perspective of statistical regression modeling, and offered valuable suggestions for the future energy saving.     

A method to estimate pre-exploitation population size

Antarctic fur seals (Arctocephalus gazella) were commercially exploited on the subantarctic island of South Georgia for over 100 years and nearly driven to extinction. Since the cessation of harvesting, however, their populations have rebounded, and they are now often considered a nuisance species whose impact on the terrestrial landscape should be mitigated. Any evaluation of their current population requires the context provided by their historic, pre-exploitation abundance, lest ecologists fall prey to shifting baseline syndrome in which their perspective on current abundance is compared only with an altered state resulting from past anthropogenic disturbance. Estimating pre-exploitation abundance is critical to defining species recovery and setting recovery targets, both of which are needed for the International Union for the Conservation of Nature's recent efforts to develop a green list of recovering species. To address this issue, we reconstructed the South Georgia fur seal harvest from 1786 to 1908 from ship logbooks and other historical records and interpolated missing harvest data as necessary with a generalized linear model fit to the historical record. Using an approximate Bayesian computation framework, harvest data, and a stochastic age-structured population model, we estimated the pre-exploitation abundance of Antarctic fur seals on South Georgia was 2.5 million females (95% CI 1.5–3.5 million). This estimate is similar to recent abundance estimates, and suggests current populations, and the ecological consequences of so many fur seals on the island, may be similar to conditions prior to human harvest. Although the historic archive on the fur sealing era is unavoidably patchy, the use of archival records is essential for reconstructing the past and, correspondingly, to understanding the present. Article impact statement: Defining species recovery requires an understanding of baseline population state, which can be estimated through statistical methods.     

Quantitative sensitivity and reliability analysis of sensor networks for well kick detection based on dynamic Bayesian networks and Markov chain

Kick is considered as an early warning sign to the blowout that is among the most undesired and feared accidents during drilling operations. Kick detection system is commonly used to timely identify the occurrence of a kick. The method commonly used for kick detection relies on the proper selection of monitoring indicators. A kick detection system should not only have very high accuracy but also maintain reliable over a long time. Different from the existing studies focusing on improving the detection accuracy, this paper presents a frame emphasizing on quantitatively analyzing and enhancing the reliability of the kick detection sensor networks. The dynamic Bayesian network (DBN) for the sensor networks is established that employs Markov chain to obtain the reliability degradation of measurement sensors over time. The proposed method is applied and evaluated by case studies to conduct reliability and sensitivity analysis for kick detection sensor networks. The reliability analysis results demonstrate that the proposed method can quantitatively analyze the reliability of a kick detection sensor networks consisting of various sensors over given time periods. The sensitivity analysis results indicate that the proposed method is effective in identifying the critical sensors that have the greatest effect on the reliability of one certain kick detection system. Based on the analysis results, optimized logical combination of sensors of a kick detection system can be achieved. An improved sensor network for the unreliable case was proposed and evaluated.     

DEVELOPMENT AND TESTING OF A SNOWMELT-RUNOFF FORECASTING TECHNIQUE1

ABSTRACT: The snowmelt-runoff model (SRM) was used to produce accurate simulations of streamfiow during the snowmelt period (April-September) for ten years on the Rio Grande Basin (3419 km²) near Del Norte, Colorado, U.S.A. In order to use SRM in the forecast situation, it was necessary to develop a family of snow cover depletion curves for each elevation zone based on accumulated snow water equivalent on April 1. Selection of an appropriate curve for a particular year from snow course measurements allows input of the daily snow cover extent to SRM for forecast purposes. Data from three years (1980, 1981, and 1985) were used as a quasi-forecast test of the procedure. In these years forecasted snow cover extent data were input to SRM, but observed temperature and precipitation data were used. The resulting six-month hydrographs were very similar to the hydrographs in the ten simulation years previously tested based on comparisons of performance evaluation criteria. Based on this result, the Soil Conservation Service (SCS) requested SRM forecasts for 1987 on the Rio Grande. Using the same procedure but with SCS estimated temperature and precipi-tation data, SRM produced a forecast hydrograph that had a r²= 0.82 and difference in seasonal volume of 4.4 percent. To approximate actual operational conditions, SRM computed daily flows were updated every seven days with measured flows. The resulting forecast hydrograph had a R²= 0.90 and a difference in volume of 3.5 percent. The method developed needs to be refined and tested on additional years and basins, but the approach appears to be applicable to operational runoff forecasting using remote sensing data.     

The importance of agricultural yield elasticity for indirect land use change: a Bayesian network analysis for robust uncertainty quantification

ABSTRACT

A major barrier to realising biofuels’ climate change mitigation potential is uncertainty concerning carbon emissions from indirect land use change (ILUC). Central to this uncertainty is the extent to which yields can respond dynamically to increased demand for agricultural commodities. This study examines the elasticity of soybean and corn yields in the USA for 1990–2017 using Bayesian network models to robustly quantify uncertainty. The central finding is that a single parameter value for yield elasticity does not adequately represent the effects of technology, policy and price pressures through time. The models demonstrate the limiting role of technological progress as well as farmers’ capital investment in response to system shocks. Results suggest evaluation of parameter uncertainty alone is unlikely to capture a full range of future ILUC scenarios and reiterate the need for ILUC studies to use probabilistic approaches as standard to robustly inform climate change mitigation policies.     

Isomorphic chain graphs for modeling spatial dependence in ecological data

Graphical models (alternatively, Bayesian belief networks, path analysis models) are increasingly used for modeling complex ecological systems (e.g., Lee, In: Ferson S, Burgman M(eds) Quantative methods for conservation biology. Springer, Berlin Heilin Heideslperk New York, pp.127–147, 2000; Borsuk et al., J Water Res Plann Manage 129:271–282, 2003). Their implementation in this context leverages their utility in modeling interrelationships in multivariate systems, and in a Bayesian implementation, their intuitive appeal of yielding easily interpretable posterior probability estimates. However, methods for incorporating correlational structure to account for observations collected through time and/or space—features of most ecological data—have not been widely studied; Haas et al. (AI Appl 8:15–27, 1994) is one exception. In this paper, an “isomorphic” chain graph (ICG) model is introduced to account for correlation between samples by linking site-specific Bayes network models. Several results show that the ICG preserves many of the Markov properties (conditional and marginal dependencies) of the site-specific models. The ICG model is compared with a model that does not account for spatial correlation. Data from several stream networks in the Willamette River valley, Oregon (USA) are used. Significant correlation between sites within the same stream network is shown with an ICG model.     

Bayesian areal wombling via adjacency modeling

Recently, public health professionals and other geostatistical researchers have shown increasing interest in boundary analysis, the detection or testing of zones or boundaries that reveal sharp changes in the values of spatially oriented variables. For areal data (i.e., data which consist only of sums or averages over geopolitical regions), Lu and Carlin (Geogr Anal 37: 265–285, 2005) suggested a fully model-based framework for areal wombling using Bayesian hierarchical models with posterior summaries computed using Markov chain Monte Carlo (MCMC) methods, and showed the approach to have advantages over existing non-stochastic alternatives. In this paper, we develop Bayesian areal boundary analysis methods that estimate the spatial neighborhood structure using the value of the process in each region and other variables that indicate how similar two regions are. Boundaries may then be determined by the posterior distribution of either this estimated neighborhood structure or the regional mean response differences themselves. Our methods do require several assumptions (including an appropriate prior distribution, a normal spatial random effect distribution, and a Bernoulli distribution for a set of spatial weights), but also deliver more in terms of full posterior inference for the boundary segments (e.g., direct probability statements regarding the probability that a particular border segment is part of the boundary). We illustrate three different remedies for the computing difficulties encountered in implementing our method. We use simulation to compare among existing purely algorithmic approaches, the Lu and Carlin (2005) method, and our new adjacency modeling methods. We also illustrate more practical modeling issues (e.g., covariate selection) in the context of a breast cancer late detection data set collected at the county level in the state of Minnesota.     

Impacts of Livestock Grazing and Tree Clearing on Birds of Woodland and Riparian Habitats

Abstract:  We investigated the impact of pastoral management on birds in subtropical grassy eucalypt woodland in southeastern Queensland, Australia, where the patterns of land management have made it possible to disentangle the effects of livestock grazing from those of tree clearing. We recorded changes in bird species composition, density, and relative abundance across two woodland habitat types (riparian and nonriparian) and two levels of clearing (wooded and nonwooded) and three levels of livestock grazing (low, moderate, and high) replicated over space (1000 km²) and time (2001–2002). We predicted that species that depend on understory vegetation would be most negatively affected by livestock grazing. A Bayesian generalized linear model showed that the level of grazing had the greatest effect when trees were present. When trees were absent, the impact of grazing was overshadowed by the effects of a lack of trees. Over 65% of species responded to different levels of grazing, and the abundance of 42% of species varied markedly with habitat and grazing. The most common response to grazing was high species relative abundance under low levels of grazing (28% of species), species absence at high levels of grazing (20%), and an increase in abundance with increasing grazing (18%). Despite having similar bird assemblages, the effect of grazing was stronger in riparian habitat than in adjacent woodland habitat. Our results suggest that any level of commercial livestock grazing is detrimental to some woodland birds, particularly the understory-dependant species, as predicted. Nevertheless, provided trees are not cleared, a rich and abundant bird fauna can coexist with moderate levels of grazing. Habitats with high levels of grazing, on the other hand, resulted in a species-poor bird assemblage dominated by birds that are increasing in abundance nationally .     

Modelling Ecological Presence–absence Data along an Environmental Gradient: Threshold Levels of the Environment

A methodology for estimating environmental thresholds of binary presence–absence data is presented where the level of the threshold is parameterised. Presence–absence data is fitted to three complementary different models: an independent null-model, a monotonically increasing or decreasing model, and an optimum model. The range of the three models is strictly between zero and one and the models are therefore well suited for modelling presence probabilities. The results of the three models may be combined by using Bayesian model selection methodologies. The proposed methodology is exemplified on observed binary presence–absence data of Bauera rubioides along an elevation gradient. Received: May 2005 / Revised: July 2005 An erratum to this article is available at.     

基于BN的锅炉超压爆炸事故原因推理与故障诊断

分析了锅炉爆炸的原因,讨论了事故树与贝叶斯网络的特点,编制了以"锅炉超压爆炸事故"为顶事件的事故树,将其转化为锅炉超压爆炸事故贝叶斯网络拓扑结构,利用贝叶斯网络的技术优势,针对锅炉超压爆炸事故贝叶斯网络拓扑结构分别进行了原因推理与诊断推理。实例应用表明,在原因推理中,根节点V_1(压力上升)发生时锅炉超压爆炸事故发生的可能性最高;在诊断推理中,当锅炉超压爆炸事故发生时,根节点V_1(压力上升)一定发生,其次为V_4(定压不准),V_(11)(未装疏水管)发生的可能性最小,为企业进行锅炉超压爆炸事故预测与故障诊断提供了理论依据。