Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks

A sequencing batch reactor was modeled using multi-layer perceptron and radial basis function artificial neural networks (MLPANN and RBFANN). Then, the effects of influent concentration (IC), filling time (FT), reaction time (RT), aeration intensity (AI), SRT and MLVSS concentration were examined on the effluent concentrations of TSS, TP, COD and NH₄⁺-N. The results showed that the optimal removal efficiencies would be obtained at FT of 1 h, RT of 6 h, aeration intensity of 0.88 m³/min and SRT of 30 days. In addition, COD and TSS removal efficiencies decreased and TP and NH₄⁺-N removal efficiencies did not change significantly with increases of influent concentration. The TSS, TP, COD and NH₄⁺-N removal efficiencies were 86%, 79%, 94% and 93%, respectively. The training procedures of all contaminants were highly collaborated for both RBFANN and MLPANN models. The results of training and testing data sets showed an almost perfect match between the experimental and the simulated effluent of TSS, TP, COD and NH₄⁺-N. The results indicated that with low experimental values of input data to train ANNs the MLPANN models compared to RBFANN models are more precise due to their higher coefficient of determination (R²) and lower root mean squared errors (RMSE) values.     

A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis

IntroductionWith the development of industries and increased diversity of their associated hazards, the importance of identifying these hazards and controlling the Occupational Health and Safety (OHS) risks has also dramatically augmented. Currently, there is a serious need for a risk management system to identify and prioritize risks with the aim of providing corrective/preventive measures to minimize the negative consequences of OHS risks. In fact, this system can help the protection of employees’ health and reduction of organizational costs. Method: The present study proposes a hybrid decision-making approach based on the Failure Mode and Effect Analysis (FMEA), Fuzzy Cognitive Map (FCM), and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) for assessing and prioritizing OHS risks. After identifying the risks and determining the values of the risk assessment criteria via the FMEA technique, the attempt is made to determine the weights of criteria based on their causal relationships through FCM and the hybrid learning algorithm. Then, the risk prioritization is carried out using the MOORA method based on the decision matrix (the output of the FMEA) and the weights of the criteria (the output of the FCM). Results: The results from the implementation of the proposed approach in a manufacturing company reveal that the score at issue can overcome some of the drawbacks of the traditional Risk Priority Number (RPN) in the conventional FMEA, including lack of assignment the different relative importance to the assessment criteria, inability to take into account other important management criteria, lack of consideration of causal relationships among criteria, and high dependence of the prioritization on the experts’ opinions, which finally provides a full and distinct risk prioritization.     

发挥环境保护档案在突发性环境污染事件中的重要作用

环境保护档案是环境状况的历史记录,是环境保护成果及科技信息的载体。在环境形势十分严峻的今天,环境保护部门要充分发挥环保档案的信息潜能,变被动的、低档次、知识审查服务,为积极主动的、高层次、超前的知识配送服务,为政府及时处理突发性环境污染事件提供科学依据。     

森林火灾分区分类施治理论的灾害学基础

森林火灾是当今世界上八大自然灾害之一,不仅具有灾害学中所描述的关于自然灾害的一般特性,而且具有自身的特点.本文从自然灾害的属性、特点、基本研究内容和方法等几个方面,论述了灾害学为森林火灾分区分类施治理论提供的理论依据.     

RBF神经网络可靠度分析方法在边坡稳定性研究中的应用

针对边坡稳定性可靠度分析,当状态函数无法显式表达且传统计算方法求解复杂问题困难时,提出一种基于ABAQUS和粒子群优化径向基函数神经网络的可靠度分析方法。基于ABAQUS的强度折减方法计算所选随机变量对应的安全系数,利用径向基函数神经网络的数据拟合功能,建立模型并映射出安全系数和随机变量之间的关系,构造响应面功能函数;利用蒙特卡罗生成的大量随机样本代入功能函数得到相应的安全系数,进而计算边坡的失稳概率和可靠度指标来反映边坡稳定性。研究结果表明:相对于传统方法,本文方法计算效率更高、误差更小,适合实际工程应用。     

经济发展成本分析:可持续发展经济学的理论基础

可持续发展战略存在着理论与实践上的严重反差，其主要原因是可持续发展经济缺乏统一的理论基础，本文通过对经济学不同流派关于可持续发展问题研究的评析，认为可持续发展经济学需要建立一个统一的理论基础，并提出经济发展成本分析是可持续发展经济学的理论基础，围绕这一理论基础对可持续发展的概念进行了重新界定，并主张要把制度的激励与约束以及政策的引导纳入可持续发展经济途径研究的视野之中。     

论高职高专英语教学改革——我院英语分级教学探索

根据目前我院英语教学状况,英语公共课有必要改革"单一目标"的教学模式,采用多目标个性化分层教学模式,提高学生英语素质.文中具体探讨了多目标个性化分级教学模式的内涵、实施的前提条件、依据及策略.     

地震应急的影响因素分析

本文在理论分析、国内外历史震例和救灾案例研究的基础上,同时参照我国的相关政策和法律法规文件,对影响地震应急(包括震前准备和震后应急行动)的因素进行了初步分类,并系统讨论了这些因素影响地震应急的方式、途径和程度.以期为地震应急的理论研究和地震应急的实际工作提供部分补充和指导.     

种子发芽势在环境污染监测中作为生物指标的尝试

本文用测定种子发芽势的方法，对Ｃｄ２＋、Ｈｇ２＋Ａｓ３－的生物毒害效应进行了试验，结果表明，种子发芽指数能很好地反映不同浓度Ｃｄ２＋、Ｈｇ２＋、Ａｓ３－的生物毒害作用．种子发芽势试验也可用于多成分污染物的生物毒性判别，通过相关分析或复回归分析可以大致判别出多成分污染物中何种成分生物毒性大．     

Artificial neural network model for identifying taxi gross emitter from remote sensing data of vehicle emission

Vehicle emission has been the major source of air pollution in urban areas in the past two decades. This article proposes an artificial neural network model for identifying the taxi gross emitters based on the remote sensing data. After carrying out the field test in Guangzhou and analyzing various factors from the emission data, the artificial neural network modeling was proved to be an advisable method of identifying the gross emitters. On the basis of the principal component analysis and the selection of algorithm and architecture, the Back-Propagation neural network model with 8-17-1 architecture was established as the optimal approach for this purpose. It gave a percentage of hits of 93%. Our previous research result and the result from aggression analysis were compared, and they provided respectively the percentage of hits of 81.63% and 75%. This comparison demonstrates the potentiality and validity of the proposed method in the identification of taxi gross emitters.