碱液预处理玉米秸秆的条件优化及添加剂的选择

利用稀NaOH溶液预处理玉米秸秆,以去除木质素,减少半纤维素、纤维素的损失为目的,采用响应曲面法对预处理条件进行优化,得到的最佳预处理条件为:NaOH溶液浓度0.77%(w/w),预处理时间16 h,温度45.6℃。并且,进一步研究了不同添加剂(聚乙二醇、吐温-80和尿素)与NaOH溶液协同作用下秸秆中木质素的去除效果,结果表明尿素与NaOH的混合溶液对木质素去除效果最显著。秸秆在45.6℃,0.77%NaOH和0.2%尿素混合溶液中浸泡16 h后,纤维素回收率为86.33%,半纤维素回收率为69.89%,木质素去除率为64.93%;与原秸秆相比,纤维素含量提高了31.70%,半纤维素含量提高了6.62%,木质素含量减少了46.48%。     

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。     

基于改进粒子群算法和元胞自动机的城市扩张模拟——以南京为例

为合理利用多智能体算法解决城市扩张动态模拟问题,基于地理学理论和社会学规律对粒子群算法进行有针对性的改进,提出分段式粒子群算法(SPSO),并结合元胞自动机模拟复杂时空过程的能力,构建出适用于城市扩张模拟的地理元胞自动机SPSO-CA。在SPSO-CA中我们利用多时像的土地利用数据、交通路网数据和地形数据,挖掘出1995~2000年南京城市扩张的土地转换规则。再由此规则实现1995~2008年的南京市城市扩张过程的动态模拟。最后对比SPSO-CA、PSOCA及NULL模型结果得:SPSO-CA总精度86.3%,Kappa系数为0.792,Moran’s I为0.078,PSO-CA总精度83.6%,Kappa系数为0.755,Moran’s I为0.054,NULL模型总精度81.9%,Kappa系数为0.741,真实的Moran’s I为0.072。这表明无论是总精度还是空间一致性,SPSO-CA都优于PSO-CA和NULL模型,即用SPSO-CA模拟城市扩张是可行的。     

泸州市降水监测布点优化研究

在泸州市2016—2020年大气降水监测数据的基础上，借助聚类分析、多元方差分析等统计分析手段，对原有降水监测点位进行优化研究，并验证优化结果。在遵循《酸沉降监测技术规范》(HJ/T 165—2004)点位布设要求的前提下，建立了降水监测点位评价体系，从原有7个降水监测点位中优化筛选出3个。优化后的点位对监测结果的影响显著性均>0.05,表明优化前后全市降水数据无显著性差异，该优化方案不会影响区域整体代表性。     

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.     

Benefits of integrating complementarity into priority threat management

Conservation decision tools based on cost‐effectiveness analysis are used to assess threat management strategies for improving species persistence. These approaches rank alternative strategies by their benefit to cost ratio but may fail to identify the optimal sets of strategies to implement under limited budgets because they do not account for redundancies. We devised a multiobjective optimization approach in which the complementarity principle is applied to identify the sets of threat management strategies that protect the most species for any budget. We used our approach to prioritize threat management strategies for 53 species of conservation concern in the Pilbara, Australia. We followed a structured elicitation approach to collect information on the benefits and costs of implementing 17 different conservation strategies during a 3‐day workshop with 49 stakeholders and experts in the biodiversity, conservation, and management of the Pilbara. We compared the performance of our complementarity priority threat management approach with a current cost‐effectiveness ranking approach. A complementary set of 3 strategies: domestic herbivore management, fire management and research, and sanctuaries provided all species with >50% chance of persistence for $4.7 million/year over 20 years. Achieving the same result cost almost twice as much ($9.71 million/year) when strategies were selected by their cost‐effectiveness ranks alone. Our results show that complementarity of management benefits has the potential to double the impact of priority threat management approaches.     

Subcritical water oxidation of propham by H2O2 using response surface methodology (RSM)

This study was conducted to investigate the degradation of propham, which is a compound that pollutes water and seriously threatens human health, by subcritical water oxidation and using H₂O₂ as an oxidising agent. The maximum total organic carbon removal rate of propham was obtained as 73.65% at 40 min of treatment time and 60 mM of H₂O₂ concentration and 373 K of temperature. In addition, response surface method based on the Box-Behnken design was applied to design the degradation experiments of propham for determination of the combined effects of process variables, namely temperature, concentration of oxidising agent and treatment time. The proposed quadratic model of propham degradation, which was examined with the analysis of variance, was used for navigating the design space. The R² and adjusted R² values of the model were determined as 0.9921 and 0.9819 respectively. It was shown that propham was effectively degraded, thus could be removed from the water by using an environmentally friendly method.     

Scenario-based assessment and multi-objective optimization of urban development plan with carrying capacity of water system

• Impact of urban development on water system is assessed with carrying capacity. • Impacts on both water resource quantity and environmental quality are involved. • Multi-objective optimization revealing system trade-off facilitate the regulation. • Efficiency, scale and structure of urban development are regulated in two stages. • A roadmap approaching more sustainable development is provided for the case city. Environmental impact assessments and subsequent regulation measures of urban development plans are critical to human progress toward sustainability, since these plans set the scale and structure targets of future socioeconomic development. A three-step methodology for assessing and optimizing an urban development plan focusing on its impacts on the water system was developed. The methodology first predicted the pressure on the water system caused by implementation of the plan under distinct scenarios, then compared the pressure with the carrying capacity threshold to verify the system status; finally, a multi-objective optimization method was used to propose regulation solutions. The methodology enabled evaluation of the water system carrying state, taking socioeconomic development uncertainties into account, and multiple sets of improvement measures under different decisionmaker preferences were generated. The methodology was applied in the case of Zhoushan city in South-east China. The assessment results showed that overloading problems occurred in 11 out of the 13 zones in Zhoushan, with the potential pressure varying from 1.1 to 18.3 times the carrying capacity. As a basic regulation measure, an environmental efficiency upgrade could relieve the overloading in 4 zones and reduce 9%‒63% of the pressure. The optimization of industrial development showed that the pressure could be controlled under the carrying capacity threshold if the planned scale was reduced by 24% and the industrial structure was transformed. Various regulation schemes including a more suitable scale and structure with necessary efficiency standards are provided for decisionmakers that can help the case city approach a more sustainable development pattern.     

基于灾害风险评估的山地城市固定避难场所布局优化研究——以东川区为例

研究以各区域灾害风险评估系数作为权重系数,对山地小城市各区域避难场所进行优化布局。首先,通过风险值=危险性*脆弱性/抗灾救灾能力机理表达式构建灾害风险评估指标体系,并以模糊综合评价法得出灾害风险评估数值,在此基础上通过多目标选址优化模型来构建山地城市避难场所的优化模型,其中灾害风险评估系数作为影响布局的权重因素纳入其中。最后,以东川区为研究对象,对其固定避难场所进行优化布局研究,结果表明:东川区城区固定避灾场所数量可在接近最优解的情况下达到效果最优。     

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.