液化石油气脱硫

阐述了醇胺法液化石油气脱硫体系的特点。借助于大型通用流程模拟系统ＡＳＰＥＮＰＬＵＳ软件，开发了液—液平衡计算ＫＬＬ子程序，已成功地实现了与ＡＳＰＥＮＰＬＵＳ联机运行。所建模型适用于以ＭＤＥＡ为溶剂的液化石油气脱硫工艺过程计算，还给出了模拟计算结果。     

Model selection in demographic time series using VC-bounds

The problem of distinguishing density-independent (DI) from density-dependent (DD) demographic time series is important for understanding the mechanisms that regulate populations of animals and plants. We address this problem in a novel way by means of Statistical Learning Theory (SLT); SLT is built around the idea of VC-dimension, a complexity index for classes of parameterized functions. Though VC-dimensions of nonlinear models are generally unknown, in the linear case VC-dimension actually corresponds to the number of free parameters; this allows one to straightforwardly apply the model selection framework developed within SLT, and called Structural Risk Minimization (SRM). We generate noisy artificial time series, both DI and DD, and use SRM to recognize the model underlying the data, choosing among a suite of both density-dependent and independent demographies. We show that SRM significantly outperforms traditional model selection approaches, such as the Schwartz Information Criterion and Final Prediction Error in recognizing both density-dependence and independence.     

LPG leakage and explosion accident analysis based on a new SAA method

The petrochemical industry has inherently high safety risks. On 5^th June 2017, a liquefied petroleum gas tanker in the storage and transportation department of Linyi Jinyu Petrochemical Co., Ltd. leaked during unloading, causing a major explosion and fire. Ten people were killed and nine were injured. To prevent the recurrence of similar accidents, a retrospective accident analysis was conducted using the system accident analysis method known as ‘24Model’. This analysis proved the coupling relationship between accident causes involving multiple organisations and multiple factors. The analysis results show that the five modules of maintenance and design, that is, loading and unloading valves, emergency training, management of subsidiaries and contractors, multi-level supervision, and task arrangement need to be checked from top to bottom by relevant organisations.In order to verify the reliability of 24Model analysis results, three SAA methods, namely STAMP, FRAM, AcciMap, were used to analyse the accident, and results obtained by the four methods were evaluated qualitatively and quantitatively. The results show that the output of 24Model is reliable and has a more understandable framework, which can be used as reference for the development of other linear accident cause models and application of system accident analysis methods to real applications.     

Factors affecting pedestrian behaviors at signalized crosswalks: An empirical study

Introduction: Safety of pedestrians depends, among other factors, on their behavior while crossing the road. This study aims to assess behaviors of pedestrians at signalized crosswalks. Method: Following a literature review and a pilot study, 25 vital pedestrian crossing factors and behaviors were determined. Then data was randomly collected for 708 pedestrians at 10 lighted crossings in Sharjah (UAE), five at road intersections and five mid-block crossings. Results: Results indicated that 17.4% of pedestrians observed crossed partly or fully on red and that crossing speed was 1.22 m/s, on the average, which is slightly faster than most speeds recorded in the literature. Moreover, female pedestrians were more likely to cross while chatting with others, less likely to cross on red, and more likely to walk slower than male pedestrians. Results also showed that pedestrians who crossed at road intersections walked slower than those who crossed at mid-block crossings. It was also found that longer red pedestrian times and narrower roads tended to encourage pedestrians to cross on red and that the majority of pedestrians did not look around before crossing. Practical implications: Use of the Health Belief Model for pedestrian safety are discussed.     

基于B—P神经网络优化算法的城市环境空气中PM10浓度预测模型

将B—P神经网络应用于西安市环境空气中P10浓度预测,对网络结构和算法进行了优化,建立了PM10浓度预测模型。经验证模型精确度比较高,PM10日平均浓度绝对误差0．015～0．020mg／m^3,相对误差在-20％～20％范围内。     

Design of aquifer remediation systems: (1) describing hydraulic structure and NAPL architecture using tracers

Aquifer heterogeneity (structure) and NAPL distribution (architecture) are described based on tracer data. An inverse modelling approach that estimates the hydraulic structure and NAPL architecture based on a Lagrangian stochastic model where the hydraulic structure is described by one or more populations of lognormally distributed travel times and the NAPL architecture is selected from eight possible assumed distributions. Optimization of the model parameters for each tested realization is based on the minimization of the sum of the square residuals between the log of measured tracer data and model predictions for the same temporal observation. For a given NAPL architecture the error is reduced with each added population. Model selection was based on a fitness which penalized models for increasing complexity. The technique is demonstrated under a range of hydrologic and contaminant settings using data from three small field-scale tracer tests: the first implementation at an LNAPL site using a line-drive flow pattern, the second at a DNAPL site with an inverted five-spot flow pattern, and the third at the same DNAPL site using a vertical circulation flow pattern. The Lagrangian model was capable of accurately duplicating experimentally derived tracer breakthrough curves, with a correlation coefficient of 0.97 or better. Furthermore, the model estimate of the NAPL volume is similar to the estimates based on moment analysis of field data.     

The importance of spatial autocorrelation,extent and resolution in predicting forest bird occurrence

Concerns about declines in forest biodiversity underscore the need for accurate estimates of the distribution and abundance of organisms at large scales and at resolutions that are fine enough to be appropriate for management. This paper addresses three major objectives: (i) to determine whether the resolution of typical air photo-derived forest inventory is sufficient for the accurate prediction of site occupancy by forest birds. We compared prediction success of habitat models using air photo variables to models with variables derived from finer resolution, ground-sampled vegetation plots. (ii) To test whether incorporating spatial autocorrelation into habitat models via autologistic regression increases prediction success. (iii) To determine whether landscape structure is an important factor in predicting bird distribution in forest-dominated landscapes. Models were tested locally (Greater Fundy Ecosystem [GFE]) using cross-validation, and regionally using an independent data set from an area located ca. 250 km to the northwest (Riley Brook [RB]). We found significant positive spatial autocorrelation in the residuals of at least one habitat model for 76% (16/21) of species examined. In these cases, the logistic regression assumption of spatially independent errors was violated. Logistic models that ignored spatial autocorrelation tended to overestimate habitat effects. Though overall prediction success was higher for autologistic models than logistic models in the GFE, the difference was only significantly improved for one species. Further, the inclusion of spatial covariates did little to improve model performance in the geographically discrete study area. For 62% (13/21) of species examined, landscape variables were significant predictors of forest bird occurrence even after statistically controlling for stand-level variability. However, broad spatial extents explained less variation than local factors. In the GFE, 76% (16/21) of air photo and 81% (17/21) of ground plot models were accurate enough to be of practical utility (AUC > 0.7). When applied to RB, both model types performed effectively for 55% (11/20) of the species examined. We did not detect an overall difference in prediction success between air photo and ground plot models in either study area. We conclude that air photo data are as effective as fine resolution vegetation data for predicting site occupancy for the majority of species in this study. These models will be of use to forest managers who are interested in mapping species distributions under various timber harvest scenarios, and to protected areas planners attempting to optimize reserve function.     

Calibration of the forest vegetation simulator (FVS) model for the main forest species of Ontario,Canada

The forest vegetation simulator (FVS) model was calibrated for use in Ontario, Canada, to predict the growth of forest stands. Using data from permanent sample plots originating from different regions of Ontario, new models were derived for dbh growth rate, survival rate, stem height and species group density index for large trees and height and dbh growth rate for small trees. The dataset included black spruce (Picea mariana (Mill.) B.S.P.) and jack pine (Pinus banksiana Lamb.) for the boreal region, sugar maple (Acer saccharum Marsh.), white pine (Pinus strobus L.), red pine (Pinus resinosa Ait.) and yellow birch (Betula alleghaniensis Britton) for the Great Lakes-St. Lawrence region, and balsam fir (Abies balsamea (L.) Mill.) and trembling aspen (Populus tremuloides Michx.) for both regions. These new models were validated against an independent dataset that consisted of permanent sample plots located in Quebec. The new models predicted biologically consistent growth patterns whereas some of the original models from the Lake States version of FVS occasionally did not. The new models also fitted the calibration (Ontario) data better than the original FVS models. The validation against independent data from Quebec showed that the new models generally had a lower prediction error than the original FVS models.     

Impact of aquacultures on the marine ecosystem: Modelling benthic carbon loading over variable depth

In order to be used within Environmental Impact Assessment study, we have developed a three-dimensional particle tracking model for prediction of benthic carbon loading (BCL) caused by fish farms. The model is based on stochastic differential equations for particle transport consistent with the well-known semi-empirical advection/diffusion equation. It requires only easily obtainable input data in the form of measured current record, the source location and a specification of local bathymetry. The model accounts for advection by long-term residual and tidal currents, turbulent diffusion, realistic bathymetry and variations in daily (monthly or yearly) emissions from fish farm.Here, we concentrate on the changes in sedimentation pattern caused by various bathymetric shapes. Examination of idealized cases reveals where and why we can expect the worst impact on benthic communities. For future reference, these results will be included into guidelines for fish farming.     

SEIB–DGVM: A new Dynamic Global Vegetation Model using a spatially explicit individual-based approach

We report the development of a new spatially explicit individual-based Dynamic Global Vegetation Model (SEIB–DGVM), the first DGVM that can simulate the local interactions among individual trees within a spatially explicit virtual forest. In the model, a sample plot is placed at each grid box, and then the growth, competition, and decay of each individual tree within each plot is calculated by considering the environmental conditions for that tree as it relates to the trees that surround it. Based on these parameters only, the model simulated time lags between climate change and vegetation change. This time lags elongated when original biome was forest, because existing trees prevent newly establish trees from receiving enough sunlight and space to quickly replace the original vegetation. This time lags also elongated when horizontal heterogeneity of sunlight distribution was ignored, indicating the potential importance of horizontal heterogeneity for predicting transitional behavior of vegetation under changing climate. On a local scale, the model reproduced climate zone-specific patterns of succession, carbon dynamics, and water flux, although on a global scale, simulations were not always in agreement with observations. Because the SEIB–DGVM was formulated to the scale at which field biologists work, the measurements of relevant parameters and data comparisons are relatively straightforward, and the model should enable more robust modeling of terrestrial ecosystems.