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.     

Risk assessment tools incorporating human error probabilities in the Japanese small-sized establishment

Although it has been estimated that as many as 80% of all occupational accidents have human errors as a cause, no risk assessment tools incorporating human-related elements have been developed for small companies. Human error probability (HEP) and human error analysis (HEA) have been used for large-scale, safety-critical industries for last three decades, but these tools are not suitable for smaller, more general industries that comprise the majority of accident settings.Here, we describe and verify a risk assessment tool that includes human-related elements for small companies. The tool expands on traditional risk assessment methods, such as matrix, risk graph and numerical scoring method, by adding human-related elements. The tool is easy-to-use in occupational environments, and includes assessments of human behavior and potentially outdated machinery at work place.     

人-机系统事故预防理论研究

分析人-机系统事故发生原因,剖析经典以人失误为主因的事故致因模型存在的不足,在该模型基础上增加"刺激"形成的原因,构建了改进事故致因模型。对两模型进行比较研究,指出人机工程学与防止事故的关系,提出了人机工程学防止事故的方法,并给出人机界面合理性主观评价检查表。研究及论证表明:预防人-机系统事故的本质在于有效防止人失误的发生,除安全管理措施以外,最重要的是人机工程学问题,笔者提出的基于人机工程学的人-机系统事故预防理论,对人-机系统事故的预防起到积极的指导作用。     

基于人工神经网络理论的建筑物火灾安全评价研究

依据建筑物火灾危险性的影响因素,应用人工神经网络理论及系统安全方法,建立了建筑物火灾危险性的评价指标体系,该方法摆脱了评价过程中的随机性和参评人员主观上的不确定性及其认识上的模糊性等缺点,大大提高了准确性。为了验证评价模型的准确性,将该理论应用到某高校图书馆火灾危险性评价中,快速、准确地得到了安全评价结果,取得了满意效果,为建筑物防火设计以及安全管理提供了可行的依据。     

航空维修人为差错影响因素分析中的模糊层次分析法

航空维修中的人为差错影响因素分析对于预防事故发生至关重要,如何定量分析及辨识出主要影响因素是亟待解决的问题。为此,结合航空维修实际,提出运用群组模糊层次分析法对人为差错影响因素进行量化排序和分类,归纳细化了影响因素层次体系,并给出了分析计算流程。以一起由维修人为差错所引发的航空事故为例进行了实证分析,结果表明:该方法能够辨识出航空维修中人为差错产生的主要影响因素,进而对制订最优事故预防方案,控制和减少该类事故的发生有积极意义。同时该方法对其他行业中的人为差错主要影响因素辨识有着一定的参考价值。     

江苏省空气质量预报准确度及改进方法分析

介绍了江苏省重污染天气监测预报预警系统以及大气重污染预警会商流程,将2015年13个地级市的模式预报、人工预报结果分别与实际观测值进行比较。结果表明:人工预报更准确,PM_(2.5)日均值、臭氧日最大8 h平均值、AQI 3个指标人工预报和实况的相关性分别比模式预报高出12.8%、0.3%、11.4%,平均标准误差(MNE)分别低20.7%、3.1%、23.1%。依据国家空气质量预报技术指南评分办法,对各市2015年全年空气质量级别为"良"时进行评分。通过开展07∶00预报更新,使2015年上半年空气质量预报级别得分平均提高了0.9分,全年级别得分平均提高了2.6分;通过改进模式预报参数,使PM_(2.5)日均预报值、臭氧日最大8 h平均预报值、AQI预报值和实际观测值的相关性比上年同期分别提高26.0%、5.0%、33.9%,MNE分别降低3.6%、31.3%、7.6%。     

基于尾涡强度的无人机与民机纵向安全间隔评估研究

为了解决无人机频频入侵机场净空,与民航客机发生危险接近的问题,以碰撞风险为量化指标,对无人机与民机需要保持的纵向安全间隔进行评估研究。分析民机尾涡流场对无人机运行的影响,利用Crow近场涡强度消散理论,得到实际尾迹影响区改进Reich碰撞模板,据此建立CNS/GPS性能环境下的纵向间隔评估模型,通过Matlab进行间隔值求解。研究结果表明:模型可以较为准确地反映民机起飞初始爬升阶段无人机尾随运行时导航性能和尾涡迹对所需最小间隔值的影响,并得到给定安全目标水平的计算结果。     

Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills

Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed.This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables.The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills.Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies.     

Critical Steps for Continuing Advancement of Satellite Rainfall Applications for Surface Hydrology in the Nile River Basin1

Gebremichael, Mekonnen, Emmanouil N. Anagnostou, and Menberu M. Bitew, 2010. Critical Steps for Continuing Advancement of Satellite Rainfall Applications for Surface Hydrology in the Nile River Basin. Journal of the American Water Resources Association (JAWRA) 46(2):361-366. DOI: 10.1111/j.1752-1688.2010.00428.x. Abstract: Given the increasingly higher resolution and data accessibility, satellite precipitation products could be useful for hydrological application in the Nile River Basin, which is characterized by lack of reasonably dense hydrological in situ sensors and lack of access to the existing dataset. However, in the absence of both extreme caution and research results for the Nile basin, the satellite rainfall (SR) products may not be used, or may even be used erroneously. We identify two steps that are critical to enhance the value of SR products for hydrological applications in the Nile basin. The first step is to establish representative validation sites in the Nile basin. The validation site will help to quantify the errors in the different kinds of SR products, which will be used to select the best products for the Nile basin, include the errors in decision making, and design strategies to minimize the errors. Using rainfall measurements collected from the unprecedented high-density rain gauge network over a small region within the Nile basin, we indicate that SR estimates could be subject to significant errors, and quantification of estimation errors by way of establishing validation sites is critically important in order to use the SR products. The second step is to identify the degree of hydrologic model complexity required to obtain more accurate hydrologic simulation results for the Nile basin when using SR products as input. The level of model complexity may depend on basin size and SR algorithm, and further research is needed to spell out this dependence for the Nile basin.     

KALMAN FILTER CALCULATION OF SAMPLING FREQUENCY WHEN DETERMINING ANNUAL MEAN SOLUTE CONCENTRATIONS

ABSTRACT: A linear filter (Kalman filter) technique was used with a Streamflow-concentration model the minimize surface water quality sampling frequencies when determining annual mean solute concentrations with a predetermined allowable error. The Kalman filter technique used the stream discharge interval as a replacement for the more commonly used time interval. Using filter computations, the measurement error variance was minimized within the sample size constraints. The Kalman filter application proposed here is applicable only under several conditions including: monitoring is solely to estimate annual mean concentration; discharge measurement errors are negligible; the Streamflow-concentration model is valid; and monthly samples reflect the total variance of the solute in question.