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.     

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

Ce-Sn-W-O_x催化剂的配伍优化及脱硝影响因素

采用共混热解法制备系列Ce-Sn-W-Ox复合氧化物,用于NH3选择性催化还原NO。通过正交实验优化CeSn-W-Ox配方,采用环境扫描电镜(ESEM)、X-射线衍射仪(XRD)等表征分析催化剂的微观形貌和固相结构,确立Ce-Sn-WOx最佳配比及结构形貌。结果表明,以粒度为5~8 mm的堇青石瓷片担载分散Ce-Sn-W-Ox,进行NH3-SCR脱除NO,当Ce/Sn/W元素摩尔比为1∶0.8∶0.6时,Ce Sn0.8W0.6Ox/堇青石NH3-SCR脱除NO效果最好。当空速为7 200 h-1,催化剂在252~426℃内脱除NO效率均大于94%。重点考察了反应空速(GHSV)、水蒸气(H2O)、SO2等对Ce Sn0.8W0.6Ox/堇青石NH3-SCR脱除NO活性的影响。研究表明,空速低于10 000 h-1时,催化剂脱硝活性受空速影响小;单独通入5%H2O对催化剂脱硝活性基本没有影响;单独通入429 mg/m3SO2导致催化剂活性略有降低;同时通入429 mg/m3SO2和5%H2O,催化剂脱硝活性下降至85.33%,除去SO2和H2O后,催化剂活性又能明显回升。     

Modelling Phosphorus Retention in Lakes and Reservoirs

Steady-state models for the prediction of P retention coefficient (R) in lakes were evaluated using data from 93 natural lakes and 119 reservoirs situated in the temperate zone. Most of the already existing models predicted R relatively successfully in lakes while it was seriously under-estimated in reservoirs. A statistical analysis indicated the main causes of differences in R between lakes and reservoirs: (a) distinct relationships between P sedimentation coefficient, depth, and water residence time; (b) existence of significant inflow–outflow P concentration gradients in reservoirs. Two new models of different complexity were developed for estimating R in reservoirs: , where τ is water residence time (year), was derived from the Vollenweider/Larsen and Mercier model by adding a calibrated parameter accounting for spatial P non-homogeneity in the water body, and is applicable for reservoirs but not lakes, and , where [P_in] is volume-weighted P concentration in all inputs to the water body (μg l⁻¹), was obtained by re-calibrating the OECD general equation, and is generally applicable for both lakes and reservoirs. These optimised models yield unbiased estimates over a large range of reservoir types.     

城市有机垃圾间歇厌氧消化pH控制动力学研究

对厌氧消化系统的物料及电离平衡进行分析,利用底物降解和微生物生长动力学建立城市有机垃圾间歇厌氧消化pH值控制模型,并研制开发了间歇厌氧消化过程pH值与产气量最优化计算机软件.运用该模型可预测不同厌氧消化过程的最佳pH值,从而通过控制厌氧系统的pH值使系统产气量达到最大,通过2组对比实验验证模型的有效性.结果表明,在相同的实验条件下厌氧系统的pH控制在最佳值时系统产气较未对pH值控制时稳定,且总产气量平均提高20%左右.     

生物表面活性剂产生菌犁头霉菌(Absidia orchidis)的筛选及发酵条件优化

采用植物油为唯一碳源,设计选择培养基,从饭店下水道污泥中筛选出生物表面活性剂产生菌.结果分离到12株菌,其中一株能使发酵液的表面张力值从68 mN·m-1降低到34.5 mN·m-1,具有开发潜力,被选出作进一步的研究.该菌株经鉴定为犁头霉菌(Absidia orchidis).通过正交试验对犁头霉菌的培养条件进行优化,其优化培养条件为:植物油3.6 g·L-1,KH2PO412.1 g·L-1,Na2HPO45 g·L-1,(NH4)2SO4 1 g·L-1,NaNO32 g·L-1,酵母浸膏0.1 g·L-1,MgSO4·7H2O 0.15 g·L-1,NaCl 5 g·L-1,CaCl2 0.1 g·L-1,EDTA 1 g·L-1,KI 0.83μg·L-1,H3PO4 0.01μg·L-1,CoCl2·6H2O 0.048μg·L-1,MnSO4·H2O 0.312μg·L-1,Na2 MoO4·2H2O 0.048μg·L-1,ZnCl2 0.287μg·L-1,CuSO4·5H2O 0.125μg·L-1,初始pH值8,接种量6%.发酵70h时可获得生物表面活性剂的最大收获量,此时发酵液中生物表面活性剂的相对浓度达402倍.     

考虑不同合作模式和多方奖惩的绿色信贷风险优化控制研究

绿色信贷风险的优化控制,有利于推动绿色信贷的稳定、可持续发展,实现环境效益、经济效益和社会效益的共赢。本研究将政府部门、环保部门、金融机构和企业的行为纳入同一个理论体系中,充分考虑影响参与主体最优努力水平和绿色信贷风险最优轨迹的环境因素、经济因素和社会因素。在此基础上,构建不同合作模式下的绿色信贷风险优化控制模型,分析不同情境下参与主体的努力水平,比较绿色信贷风险的最优轨迹,找出绿色信贷风险优化控制存在的问题及原因。研究结果表明：第一,四方合作模式对绿色信贷风险的优化控制具有明显的优势。第二,政府的参与能够有效带动其余合作者的积极性。第三,金融机构和企业之间,存在明显的利益共存和投入互动关系。第四,补贴和惩罚措施在合作过程中能够产生激励和约束作用,但在非合作模式下发挥的作用比较有限。第五,参与主体的协同水平越高,越有利于绿色信贷风险的优化控制。因此,需要灵活运用行政、经济或法律等多种干预手段,并对补贴用途进行考评和增强舆论监督,防止金融机构和企业将绿色补贴挪作他用。同时,建立符合国内绿色信贷发展特点的风险标准,统一绿色信贷风险的评判和管控依据。     

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.     

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%。     

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。