厌氧-准好氧联合型生物反应器填埋场渗滤液水质水量变化规律的研究

为了减少生物反应器填埋场投资和运行费用,实现填埋场的快速稳定化,将厌氧型生物反应器填埋场(ANBL)和准好氧矿化垃圾生物反应床(SAARB)串联,组成新型的厌氧-准好氧联合型生物反应器填埋场(AN-SABL).通过研究AN-SABL渗滤液水质水量变化规律,以期为其渗滤液的管理和处理提供理论依据.实验表明,AN-SABL具有较好的渗滤液削减作用,最高削减量可达到771 g·kg-1;而且AN-SABL渗滤液水质变化具有明显的阶段性特征,渗滤液中硝酸盐(包括亚硝酸盐)浓度和氨氮浓度分别受Eh值和pH值影响明显.AN-SABL能够有效去除渗滤液中的有机物,实验结束时,AN-SABL2号和AN-SABL3号渗滤液COD的去除率分别达到了98.49%和97.98%,二者的COD浓度仅为ANBL1号COD浓度的14.5%、21.1%;同时,AN-SABL有较好的脱氮作用,AN-SABL2号和AN-SABL3号渗滤液中氨氮浓度分别从最高值1 452 mg·L-1、1 409 mg·L-1下降至525 mg·L-1、459 mg·L-1,只有ANBL1号氨氮浓度的36.5%和31.9%.SAARB单元的脱氮除碳效果受TOC/TC值的影响明显,当渗滤液中TOC/TC下降到0.2左右后,SAARB单元对COD和TN的去除率分别从95%和93%以上降到了35.25%～69.56%和64.53%～77.45%之间.     

污水管网典型混流制区域水量水质波动特征解析

混流制管网区域的入流入渗等问题致使污水水量水质波动较大,这给城市污水系统运行管理带来极大挑战.以某市典型混流制区域为对象,建立基于物联网的多点同步监测平台,重点研究了混流制污水管网的运行特点,识别关键的特征指标和不同情景下的指标变化特征.结果表明,旱流正常液位情景下水量水质日变化规律明显、多点同步性规律较一致;旱流高液位情景下水量水质日变化规律依然存在,但水质多点同步性差异较大;旱流液位、电导率波动特征受季节降雨量变化影响显著,且其主分布区间与季节降雨显著相关.降雨过程对液位变化速率及特征时段内的电导率变化速率分布特征影响较大,高低液位两种情景下液位、电导率对雨量雨型的响应特征差异显著,低液位时电导率响应更灵敏,高液位时液位响应更灵敏.基于旱季雨季分布规律以及降雨过程水量水质变化特征,建立了基于多点同步监测的管网运行参数特征库,为雨污混流管网优化运行管理提供支持.     

基于MRMR-HK-SVM模型的PM2.5浓度预测

以赣州市2017年全年的空气质量和气象数据为研究对象,通过最大相关最小冗余算法（MRMR）提取出最优的特征子集,并将其作为预测模型的输入数据,同时构造混合核函数（HK）对传统的支持向量机模型（SVM）进行改进,最终建立MRMR-HK-SVM模型.实验结果表明,MRMR-HK-SVM模型有着更低的平均绝对误差（MAE）、平均绝对百分比误差（MAPE）和均方根误差（RMSE）,相较于传统SVM模型,预测结果平均绝对误差下降了26.9%,且能更加准确的追踪到PM_2.5浓度的突变时刻.可见,MRMR-HK-SVM模型具有更好的泛化能力,能够更加精确地预测PM_2.5浓度.     

双碳目标下电力系统转型对产业部门影响评估——以粤港澳大湾区为例

通过细化机组级燃煤发电财务状况建模,测算了提前退役、灵活性调整、限制和停止新增等情景下煤电搁浅资产风险,明确了不同情景下导致搁浅资产规模及时空分布情况.结果表明:存量煤电机组是引起搁浅资产的主体,控制新增煤电有助于降低搁浅资产风险,提前退役、灵活性调整情景下中国现存和新增煤电搁浅资产总规模分别为1.90万亿和3.98万亿元;不同转型情景导致煤电搁浅资产的年际分布差异明显,提前退役搁浅压力主要集中于2030~2040年间,灵活性调整情景下则集中于2021~2035年间;煤电搁浅资产空间分布极不均衡,山东、内蒙古、江苏等10个煤电大省搁浅资产规模占全国的67%和70%.因此,煤电低碳转型需审慎决策,重视提前退役造成的煤电资产搁浅,更要防范和控制灵活性调整导致的煤电资产减值,重点关注山东、内蒙古、新疆、江苏等重点省份,制定因地制宜的煤电转型策略,帮助电力相关企业及政府等进行减排政策选择.     

Thermodynamic coupling characteristics in hybrid (dry/wet) cooling system

A hybrid cooling system consisting of both a dry section and wet section is proposed in this paper as a means to conserving energy and water by combining the benefits of both dry and wet cooling modes. A new thermodynamic coupling characteristics computing model was established to identify the best combination of dry and wet cooling subsystems in the hybrid tower throughout year-round operation based on its air thermodynamic state under the “no plume” principle. A hybrid cooling tower in Inner Mongolia, China, consisting of an elliptical tube heat exchanger with rectangular fins and counter-flow wet packing, was designed as an example under the no plume principle. The minimum number of heat exchanger units in service and the corresponding thermodynamic operating parameters were obtained under a year-round operation. The tower exhibited notable advantages in regards to water conservation compared to the traditional evaporative cooling tower at an estimated yearly savings of 3.74 × 10⁷ kg water.     

Strategic directions for agent-based modeling: avoiding the YAAWN syndrome

In this short communication, we examine how agent-based modeling has become common in land change science and is increasingly used to develop case studies for particular times and places. There is a danger that the research community is missing a prime opportunity to learn broader lessons from the use of agent-based modeling (ABM), or at the very least not sharing these lessons more widely. How do we find an appropriate balance between empirically rich, realistic models and simpler theoretically grounded models? What are appropriate and effective approaches to model evaluation in light of uncertainties not only in model parameters but also in model structure? How can we best explore hybrid model structures that enable us to better understand the dynamics of the systems under study, recognizing that no single approach is best suited to this task? Under what circumstances – in terms of model complexity, model evaluation, and model structure – can ABMs be used most effectively to lead to new insight for stakeholders? We explore these questions in the hope of helping the growing community of land change scientists using models in their research to move from ‘yet another model’ to doing better science with models.     

Nonlinear molecular based modeling of the flash point for application in inherently safer design

New chemical process design strategies utilizing computer-aided molecular design (CAMD) can provide significant improvements in process safety by designing chemicals with required target properties and the substitution of safer chemicals. An important aspect of this methodology concerns the prediction of properties given the molecular structure. This study utilizes one such emerging method for prediction of a hazardous property, flash point (FP), which is in the center of attention in safety studies. Using such a reliable data set comprising 1651 organic and inorganic chemicals, from 79 diverse material classes, and robust dynamic binary particle swarm optimization for the feature selection step resulted in the most efficient molecular features of the FP investigations. Apart from the simple yet precise five-parameter multivariate model, the FP nonlinear behavior was thoroughly investigated by a novel hybrid of particle swarm optimization and support vector regression. Besides, 195 missing experimental FPs of the DIPPR data set are predicted via the presented procedure.     

Estimation of distribution algorithm enhanced particle swarm optimization for water distribution network optimization

The optimization of a water distribution network (WDN) is a highly nonlinear, multi-modal, and constrained combinatorial problem. Particle swarm optimization (PSO) has been shown to be a fast converging algorithm for WDN optimization. An improved estimation of distribution algorithm (EDA) using historic best positions to construct a sample space is hybridized with PSO both in sequential and in parallel to improve population diversity control and avoid premature convergence. Two water distribution network benchmark examples from the literature are adopted to evaluate the performance of the proposed hybrid algorithms. The experimental results indicate that the proposed algorithms achieved the literature record minimum (6.081 M$) for the small size Hanoi network. For the large size Balerma network, the parallel hybrid achieved a slightly lower minimum (1.921M€) than the current literature reported best minimum (1.923M€). The average number of evaluations needed to achieve the minimum is one order smaller than most existing algorithms. With a fixed, small number of evaluations, the sequential hybrid outperforms the parallel hybrid showing its capability for fast convergence. The fitness and diversity of the populations were tracked for the proposed algorithms. The track record suggests that constructing an EDA sample space with historic best positions can improve diversity control significantly. Parallel hybridization also helps to improve diversity control yet its effect is relatively less significant.     

Multi-criteria PSO-based optimal design of grid-connected hybrid renewable energy systems

ABSTRACT

Human-induced climate change through the over liberation of greenhouse gases, resulting in devastating consequences to the environment, is a concern of considerable global significance which has fuelled the diversification to alternative renewable energy sources. The unpredictable nature of renewable resources is an impediment to developing renewable projects. More reliable, effective, and economically feasible renewable energy systems can be established by consolidating various renewable energy sources such as wind and solar into a hybrid system using batteries or back-up units like conventional energy generators or grids. The precise design of these systems is a critical step toward their effective deployment. An optimal sizing strategy was developed based on a heuristic particle swarm optimization (PSO) technique to determine the optimum number and configuration of PV panels, wind turbines, and battery units by minimizing the total system life-cycle cost while maximizing the reliability of the hybrid renewable energy system (HRES) in matching the electricity supply and demand. In addition, by constraining the amount of conventional electricity purchased from the grid, environmental concerns were also considered in the presented method. Various systems with different reliabilities and potential of reducing consumer’s CO₂ emissions were designed and the behavior of the proposed method was comprehensively investigated. An HRES may reduce the annualized cost of energy and carbon footprint significantly.     

Refrigeration waste heat utilization for drying applications: a review

ABSTRACT

Cold chain industry has a vast potential for waste heat recovery. It is a matter of importance for energy efficiency point of view, as global energy demand is increasing day by day. Ample amount of low-grade energy is either unutilized or underutilized. The heat rejected by a Heat pump or refrigeration system emerged as a promising solution for dehydration by utilizing low-grade waste heat despite higher investment. As compared to solar drying technology, heat pump drying evolved as a reliable method regarding better process control, energy efficiency, and quality of the product to be dried. Energy utilized through the refrigeration system’s waste/exhaust heat recovery in combination with or without renewable energy source enhances the overall efficiency of the system and also reduces the cost. This useful review investigated and compared the research findings of waste heat utilization through heat pump and from condenser of refrigeration system on laboratory, pilot as well as industrial scale for drying of various fruits, vegetables, and agro products. Various drying parameters like drying rate, moisture content, Specific Moisture Extraction Rate (SMER), Coefficient of Performance (COP), Exergy efficiency, and temperature as well as humidity conditions inside the drying chamber were also reviewed to promote the technological advancement of energy utilization by commercial cold storage waste heat recovery.