Evaluating the impact of uncertainty and variability on the value chain optimization of a forest biomass power plant using Monte Carlo Simulation

Mathematical programming models have been used to optimize the design and management of forest bioenergy supply chains. A deterministic mathematical model is beneficial for making optimum decisions; however, its applicability to real-world problems may be limited because it does not capture all the complexities, including uncertainties in the parameters, in the supply chain. In this paper, a combination of Monte Carlo Simulation and optimization model is used to evaluate the impact of uncertainty in biomass quality, availability and cost, and electricity prices on the supply chain of a forest biomass power plant. The optimization model is a deterministic mixed integer non-linear model with monthly time steps over a 1-year planning horizon. Variability in biomass quality, i.e. moisture content (MC) and higher heating value (HHV), based on the historical data of a real case study is studied in detail and fitted probability distributions are used in the model, while for electricity prices different scenarios are considered. The results show that the impact of variability in the MC on profit is higher than that of uncertainty in HHV. It is observed that the annual profit ranges between $13.3 million and $17.9 million in the presence of all possible uncertainties while its average is $15.5 million. Uncertainty in biomass availability and cost and electricity price results in the risks of having annual profit of less than $14 million and low monthly storage levels.     

Optimization-Based Atmospheric Plume Source Identification

This work applies optimization and an Eulerian inversion approach presented by Bagtzoglou and Baun in 2005 in order to reconstruct contaminant plume time histories and to identify the likely source of atmospheric contamination using data from a real test site for the first time. Present-day distribution of an atmospheric contaminant plume as well as data points reflecting the plume history allow the reconstruction and provide the plume velocity, distribution, and probable source. The method was tested to a hypothetical case and with data from the Forest Atmosphere Transfer and Storage (FACTS) experiment in the Duke experimental forest site. In the scenarios presented herein, as well as in numerous cases tested for verification purposes, the model conserved mass, successfully located the peak of the plume, and managed to capture the motion of the plume well but underestimated the contaminant peak.     

瓦斯抽采PC-CAS基封孔材料优化及其膨胀作用分析*

为了研制以硅酸盐/铝酸盐水泥、生石灰、生石膏为主的PC-CAS基封孔材料，采用正交试验优化材料配比、开展膨胀力研究，并用扫描电镜观测料-煤胶结形貌。研究结果表明:材料的最优配比为A2B2C4，其具备优良的流动性、强度及致密性，造浆量达2 100 mL/kg；PC-CAS基材料具有显著的膨胀性，其膨胀力随时间先增大而后趋于稳定，并且该膨胀力随着径向约束的提高而增大；相比于FP材料，PC-CAS基材料产生的膨胀力能够显著降低材料-煤岩界面的空隙并在一定程度上压密煤体内部的裂隙；该材料产生的膨胀力可压密封孔段围岩、提高封孔质量，并为后期“二次注浆”提供保压条件。工业试验结果表明，采用PC-CAS基材料进行封孔可显著改善抽采效果，将原有平均浓度10.11%提升至20.08%。     

Location selection of emergency supplies repositories for emergency logistics management: A variable weighted algorithm

With the development of the city, the number of establishments that are proposed or under construction is increasing year by year, and if they are industries that handle flammable, explosive, toxic, harmful, and dangerous substances, the public safety will face great threats, which will bring great challenges to emergency rescue work. Therefore, providing reasonable solutions to the problem of location selection of emergency supplies repositories are necessary for improving the emergency response efficiency in chemical industrial parks. A mathematical model for location selection of emergency supplies repositories in emergency logistics management are presented considering more actual factors. The optimization objectives of the model are to minimize total transport length and cost. And then a Variable Weighted Algorithm is designed to solve the model, where an auxiliary function was constructed with different methods of building weighting factors based on the theory and method of solving multi-objective optimization problems in operational research. Simulation results show the effectiveness and feasibility of the models and algorithms presented in this paper.     

基于人工神经网络的气体泄爆最大超压预测研究

为解决传统经验公式在预测气体泄爆中最大超压出现时的较大偏差或过于保守的问题，提出使用人工神经网络预测气体泄爆最大超压。基于124组实验数据，采用BP与RBF神经网络，通过优化算法计算与迭代循环对泄爆样本中的影响因素进行降维与选择，并确定2类神经网络本身在学习与计算气体泄爆样本时的相关参数。结果表明:PCA（主成分分析法）在当前样本条件下的降维效果较差，而通过迭代对比确认气体泄爆样本中的5类特征全部保留时神经网络的训练模拟效果最好；通过对124组实验数据进行随机挑选训练集与测试集的训练模拟结果发现，神经网络对气体泄爆中最大超压的预测效果较好；通过对比Molkov提出的和经Fakandu等改进的NFPA 68经验公式以及2类神经网络的预测结果表明，神经网络相比于传统气体泄爆经验公式具有明显优势。     

维修时间不确定性下配电网灾后维修队安排的鲁棒恢复研究*

为最小化灾后配电网损失量，准确描述完整维修队工作时间（分为路途时间与具体维修时间），依据台风路径对维修队所需路途时间进行分类，并利用期望概率描述具体维修时间的不确定性。建立2阶段分布式鲁棒优化模型，采用CCG算法分析国内某地区配电网算例发现:考虑维修时间不确定性可以有效减少配电网损失量。     

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.     

具有变频器的矿井低压漏电保护研究

为了解决移动变电站低压侧为变频器供电时漏电保护装置误动的问题，提出了基于傅式算法的附加直流原理漏电保护新方法。通过理论分析和MATLAB/Simulink仿真，研究了具有变频器的矿井低压供电系统附加直流原理漏电保护，提出由傅式算法分解出采样电压直流分量以反映电缆绝缘水平的保护方法；同时分析了其他因素对直流分量的影响。研究结果表明:变频器对传统附加直流保护整定值有较大影响，易造成漏电保护装置误动且无法实现电缆的在线绝缘监测；所提出的直流分量法能准确测量电缆对地绝缘电阻，且可消除电缆分布电容、变频器载波频率、变频器输出频率等其他因素的干扰；采用该漏电保护方法，可承受变频器投入时对采样电压造成的较大冲击，不必在启动期间解除漏电保护装置，能够消除在此期间存在的人身触电安全隐患。     

炼油废水污泥脱水工艺条件的优化

采用化学除油降黏—污泥调理—离心脱水工艺处理某炼油厂废水处理系统的混合污泥，并对工艺条件进行优化。实验结果表明，最佳的工艺条件为：化学除油降黏阶段处理体系的pH=4，反应温度35 ℃，H₂O₂加入量 2 g/L，m（H₂O₂）∶ m（Fe²⁺）=4，反应时间 60 min；污泥调理反应阶段的CaO加入量7.0 g/L；离心脱水阶段在分离因数为1 558时脱水5 min。在此条件下，得到的泥饼的含水率为70.0%~75.0%（w），含油率小于2%（w），污泥比阻约为3.0×10⁷ s²/g。     

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.