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.     

Fine‐resolution conservation planning with limited climate‐change information

Climate‐change induced uncertainties in future spatial patterns of conservation‐related outcomes make it difficult to implement standard conservation‐planning paradigms. A recent study translates Markowitz's risk‐diversification strategy from finance to conservation settings, enabling conservation agents to use this diversification strategy for allocating conservation and restoration investments across space to minimize the risk associated with such uncertainty. However, this method is information intensive and requires a large number of forecasts of ecological outcomes associated with possible climate‐change scenarios for carrying out fine‐resolution conservation planning. We developed a technique for iterative, spatial portfolio analysis that can be used to allocate scarce conservation resources across a desired level of subregions in a planning landscape in the absence of a sufficient number of ecological forecasts. We applied our technique to the Prairie Pothole Region in central North America. A lack of sufficient future climate information prevented attainment of the most efficient risk‐return conservation outcomes in the Prairie Pothole Region. The difference in expected conservation returns between conservation planning with limited climate‐change information and full climate‐change information was as large as 30% for the Prairie Pothole Region even when the most efficient iterative approach was used. However, our iterative approach allowed finer resolution portfolio allocation with limited climate‐change forecasts such that the best possible risk‐return combinations were obtained. With our most efficient iterative approach, the expected loss in conservation outcomes owing to limited climate‐change information could be reduced by 17% relative to other iterative approaches.     

Ethanol mediated As(III) adsorption onto Zn-loaded pinecone biochar: Experimental investigation, modeling, and optimization using hybrid artificial neural network-genetic algorithm approach

Organic matters (OMs) and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the ethanol (EtOH)-mediated As(III) adsorption onto Zn-loaded pinecone (PC) biochar through batch experiments conducted under Box–Behnken design. The effect of EtOH on As(III) adsorption mechanism was quantitatively elucidated by fitting the experimental data using artificial neural network and quadratic modeling approaches. The quadratic model could describe the limiting nature of EtOH and pH on As(III) adsorption, whereas neural network revealed the stronger influence of EtOH (64.5%) followed by pH (20.75%) and As(III) concentration (14.75%) on the adsorption phenomena. Besides, the interaction among process variables indicated that EtOH enhances As(III) adsorption over a pH range of 2 to 7, possibly due to facilitation of ligand–metal(Zn) binding complexation mechanism. Eventually, hybrid response surface model–genetic algorithm (RSM–GA) approach predicted a better optimal solution than RSM, i.e., the adsorptive removal of As(III) (10.47 μg/g) is facilitated at 30.22 mg C/L of EtOH with initial As(III) concentration of 196.77 μg/L at pH 5.8. The implication of this investigation might help in understanding the application of biochar for removal of various As(III) species in the presence of OM.     

生物滴滤塔装置脱硝效率优化改进实验

针对生物滴滤塔系统脱硝效率偏低的问题,在原实验室装置基础上,对滴滤塔系统进行改进,并模拟燃煤烟气的特性,考察了重金属离子、氧气含量以及强化剂对净化效果的影响,并探究了生物滴滤塔系统对锅炉非稳定运行工况的适应性。结果显示:烟气内的重金属离子对微生物具有较强的抑制作用,其中As离子和Pb离子对体系微生物组成的毒性影响最大,Zn离子的影响最小;氧气含量对脱硝效率影响的研究结果表明,系统含氧量越低,脱硝效率降低幅度越大;强化剂组合结果表明,亚硝酸钠+Fe(II)EDTA的组合,强化效果最为明显;生物滴滤塔脱硝系统对锅炉非稳定运行工况适应能力较差,系统停运再启动,停运1周之内,系统经过10 d可以恢复至原来的性能,如果停运12 d以上,系统恢复则长达30 d。根据研究结果,为中试研究提出优化及研究方向建议。     

基于非概率可靠性的离散变量结构优化设计

目的针对工程结构设计中因材料供应规范限制使得设计变量不能连续取值以及材料性能波动、外界载荷偏差等不确定性问题,探索研究非概率可靠性在离散变量结构优化设计中的应用。方法以桁架结构杆件截面直径为设计变量、结构质量极小化为目标,考虑材料强度极限与外部载荷的不确定性,建立具有非概率可靠性指标约束的离散变量结构可靠性优化模型,并采用遗传算法求解离散变量结构优化设计问题。结果求解获得了三杆、十杆超静定桁架杆件截面直径最优组合,得到的优化结果相较确定性优化结果更具鲁棒性。结论非概率可靠性模型对于解决工程中设计变量具有离散性和不确定性的优化设计问题具有可行性。     

ABC算法优化SVR的磨损故障预测模型

目的为了提高故障预测的精度,针对支持向量回归SVR(Support vector machine for regression,SVR)参数选择困难的问题,提出一种采用人工蜂群(artificial bee colony,ABC)算法优化支持向量回归(SVR)的故障预测模型(ABC-SVR)。方法该模型先对样本数据进行重构,然后将故障预测误差(适应度)作为优化目标,通过ABC算法寻优找到最优的SVR参数,建立故障预测模型。最后通过实例仿真验证模型的优越性。结果采用ABC算法优化的SVR故障预测模型进行时间序列预测,能够较好地跟踪发动机滑油金属元素浓度的变化过程,并且能够提前2个取样时间预测异常情况的出现。结论 ABC-SVR模型有效解决了SVR参数选择难题,能够更加准确地表现故障变化规律,提高了故障预测精度。     

绿色会计视角下能源企业的成本核算优化研究

针对绿色会计视角下能源企业的成本核算优化,介绍了绿色会计视角下能源企业的成本核算,由于能源企业的发展,对环境产生了巨大危害,也导致能源企业由于本身成本过高,这才提出绿色会计,这样才能优化能源企业的成本,可以保证能源企业的可持续发展;同时,也保障了能源企业生态的稳定建设.基于绿色会计视角,探讨了能源企业的绿色成本优化研究,主要包括能源企业坚持绿色会计,确定绿色成本原则,能源企业绿色成本优化条件和能源企业加强绿色会计实践.     

城市建筑周边绿地景观格局优化方法研究

现有绿地景观格局优化方法,存在梯度变化不规律、边缘斑块密度不均等缺陷.针对该问题,提出基于窗口移动法的新型城市建筑周边绿地景观格局优化方法,通过确定研究区域与数据来源,以边缘斑块密度、梯度、绿地景观格局化指数为绿地景观格局化指数,确立研究方法.模拟研究环境设计实验结果表明,采用改进格局优化方法可改善城市建筑周边绿地景观格局梯度变化不规律问题;在特定空间分布状态下,还可避免边缘斑块密度不均的现象发生.     

西北干旱区煤炭基地生态蓄水模式研究

西北干旱区煤炭开采产生的矿井水大多就近排放,利用率低.同时,该地区水资源严重缺乏,尤其是生态用水紧缺,煤炭开采造成地下水资源浪费和生态用水紧缺的矛盾局面.为缓解这一矛盾,提出地面人工湿地及生态储水的设想,构建两种基于生态用水的地面处理储存矿井水的模式,该模式下矿井水通过下渗补给潜水含水层,进而供给地表植被;另一方面,利用干旱区蒸发量大的特点借助低空大气环流输送水份,增加局部降水量,从而实现改善生态环境的目的.     

摩擦清洗修复铅污染土壤的参数优化及清洗效率评价

采用摩擦清洗法修复铅污染场地土壤,主要目的是将细颗粒和铅污染物从砂粒表面去除.通过正交实验优化清洗参数,并对3份不同铅含量土壤进行摩擦清洗实验.优化的清洗参数为:水土比70%干物质、温度25℃、搅拌时间30 min、搅拌速率1 200 r.mim-1.摩擦清洗前土壤经过筛分化验分析表明,3份土壤铅主要富集在砂粒和粉黏粒中;铅的分布与有机质有极大相关性.结果表明,经过摩擦清洗后,3份原状铅污染土壤砂粒的清洗效率分别为:67.61%、31.71%、41.01%,摩擦清洗从砂粒表面去除一部分细粒土壤和铅污染物,使铅浓缩至粉黏粒中;扫描电镜(SEM)分析表明,摩擦清洗后砂粒表面变光滑.以上结论说明摩擦清洗对于铅污染场地的砂粒土壤具有较好的清洗效果.