基于PCA透射光谱重构降噪的水体BOD含量模拟估算

生化需氧量(BOD)是能够直接体现水体有机物污染程度的重要指标,水体BOD的实时监测在水资源保护、水环境改善等相关领域具有重要意义。传统的BOD测量方法会消耗大量的人力物力资源,且测量周期较长,不能迅速的反映水体的变化状况,无法实现对突发水污染事件及时有效的预警。机器学习在水体监测领域已被广泛应用,为了解决机器学习模型输入变量获取困难,且存在缺失值的问题,进一步结合高光谱技术探索对水体BOD含量精准快速的估算。为此,采集十个不同浓度BOD标液的原始光谱数据,通过白板校正得到100组透射光谱数据。提出了一种基于主成分分析(PCA)透射光谱重构的降噪技术,利用PCA算法提取原始透射光谱的主成分特征向量,再利用累计方差贡献率达到一定百分比的前一部分主成分特征向量对整个数据集进行重构。采用了前2、前10和前15个主成分特征向量对透射光谱数据进行了重构,并与传统光谱数据降噪方法进行了对比。结合支持向量机(SVM)模型和反向传播神经网络(BPNN)模型建立了水体BOD含量估算模型。结果显示,BPNN模型在回归精度和拟合程度上优于SVM模型,且降噪效果更为显著。使用前2个特征向量重构降噪的模型未达预期拟合,可能是由于信息丢失。而以前10个特征向量重构降噪的BPNN模型表现最佳,RMSE为0.0406,R^(2)达到0.9803。前15个特征向量的重构并未提升降噪效果,可能因为超过10个的特征向量增加了冗余信息。实验验证了使用PCA重构透射光谱降噪的可行性,并为水体BOD含量估算提供了新的思路。

Multi-Step Clustering of Smart Meters Time Series:Application to Demand Flexibility Characterization of SME Customers

Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.

碘量法和BOD快速测定仪测定医疗出水BOD浓度的研究

通过碘量法和BOD快速测定仪测定雅安职业技术学院医疗出水BOD浓度,探究这2种方法的差异。采取碘量法测定6份平行样品的溶解氧BOD依次为6.89、6.67、6.73、6.62、7.03、6.76 mg·L^(-1),平均值AV为6.78 mg·L^(-1),标准偏差为0.15,相对标准偏差为0.02,具有统计学意义。采取快速测定法测定6份平行样品的溶解氧BOD依次为6.55、6.45、6.72、6.66、6.54、6.78 mg·L^(-1),平均值AV为6.62 mg·L^(-1),标准偏差为0.12,相对标准偏差为0.02,具有统计学意义。结合国家《医疗机构水污染物排放标准》(GB18466—2005)的排放标准,分别采用国标GB7489—87制定的碘量法和快速检测仪对医疗排放水进行BOD检测,符合污水排放标准。从标准差数据来看,碘量法的标准差为0.151,快速测定法的标准差为0.124,2种方法相比较,快速测定法比碘量法更精确。与快速测定仪的测定步骤相比较,碘量法在测定过程中容易受到环境温度、气压、压力、实验操作误差等因素的影响,相对来说,数据波动比较大。

基于优化特征选择的污水处理过程BOD神经网络软测量模型

针对污水处理过程中出水生化需氧量(BOD)难以在线准确测量的问题,提出一种基于随机森林(RF)重要性评估与改进海鸥算法(ISOA)优化长短期记忆神经网络(LSTM)相结合的软测量方法对出水BOD进行预测.利用随机森林算法对影响出水BOD的预测因子进行重要性评估,筛选出重要性评分较高的预测因子作为软测量模型的输入变量;设计一种基于改进海鸥优化算法(ISOA)优化LSTM网络的软测量模型,引入混沌映射与新的搜索机制克服海鸥优化算法多样性差、易陷入局部最优等问题,利用改进的海鸥优化算法对LSTM网络的迭代次数、隐含层节点数、初始学习率、学习率下降因子4个超参数进行优化.将软测量模型运用于实际污水处理过程,结果表明:经随机森林筛选变量以及改进海鸥算法优化之后,模型预测误差变小,预测精度有明显提高,能够实现对出水BOD的精准预测.

Simultaneous Determination of Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD5) in Wastewater by Near-Infrared Spectrometry

To rapidly determine the pollution extent of wastewater, the calibration models were established for deter-mination of Chemical Oxygen Demand and Biological Oxygen Demand in wastewater by partial least squares and near infrared spectrometry of 120 samples. Spectral data preprocessing and outliers’ diagnosis were also discussed. Correlation coefficients of the models were 0.9542 and 0.9652, and the root mean square error of prediction (RMSEP) were 25.24 mg?L-1 and 12.13 mg?L-1 in the predicted range of 28.40~528.0 mg?L-1 and 16.0~305.2 mg?L-1 for Chemical Oxygen Demand and Biological Oxygen Demand, respectively. By statistical significance test, the results of determination were compared with those of stan-dard methods with no significant difference at 0.05 level. The method has been applied to simultaneous de-termination of Chemical Oxygen Demand and Biological Oxygen Demand in wastewater with satisfactory results.

Measurement of Biological Oxygen Demand （BOD） in Sewage Wastewater Using Modified Inoculums

The objective of this studying was to accurate determination of Biological Oxygen Demand （BOD） through the use of two types of prepared inoculums, the natural activated sludge supplied from conventional wastewater treatment plant and the modified activated sludge prepared from activated sludge of wastewater treatment plant of refinery factory. Analytical method was used to measurement of BOD by preparing the standard curve of BOD in basal medium. The results showed to the large differences in BOD values in basal medium （30-300 mg/L） and conventional wastewater （80-320 mg/L） when they were inoculated with natural and modified activated sludge respectively. It was also found an ability of modified sludge to remove high concentrations of oil and greases.

基于FRBPSO-RBF神经网络的污水BOD5软测量方法

污水处理过程中污水BOD5难以实时准确测量,故软测量方法逐渐被用于污水BOD5的预测,其中RBF神经网络软测量方法应用广泛,但存在训练过程易陷入局部极值等问题。为提高RBF神经网络的预测精度,提出了基于适应度排名的粒子群算法(fitness ranking based particle swarm optimization,FRBPSO),根据适应度排名与迭代次数确定惯性权重的大小,并根据粒子个体历史最优值的排名与迭代次数确定自我学习因子与社会学习因子的大小,并将FRBPSO算法引入RBF神经网络的参数训练中。基于13个基准测试函数与其他3个粒子群优化算法对比,实验结果显示FRBPSO算法的寻优能力相对较强。再将基于FRBPSO算法的RBF神经网络用于构建污水BOD5软测量模型,仿真结果表明,在测试数据中,FRBPSO-RBF软测量模型的平均绝对误差比PSO-RBF软测量模型、DAIW-RBF软测量模型、SCVPSO-RBF软测量模型分别降低了0.7178、0.2402、0.5851,平均绝对百分比误差分别降低了0.47%、0.15%、0.33%,均方根误差分别降低了0.0034、0.0015、0.0039。与其他3个基于PSO算法的BOD5软测量模型相比,FRBPSO-RBF模型具有较高的BOD5预测精度。

生化需氧量(BOD)在线监测仪校准方法研究

本文介绍了基于紫外-可见光谱法原理的生化需氧量(BOD)在线监测仪的基本结构和工作原理,明确了监测仪的计量特性,确定了校准环境条件、测量标准、校准项目和校准方法,并对监测仪的关键性能指标的示值相对误差进行不确定度评定。同时,对不同监测仪进行试验验证。结果表明:该监测仪性能指标设置科学合理,且校准方法具有可操作性。

Research on Demand Response Potential of Adjustable Loads in Demand Response Scenarios

To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis.Firstly,based on the demand response process and demand response behavior,obtain demand response characteristics that characterize the process and behavior.Secondly,establish a feature extraction and prediction model based on data mining,including similar day clustering,time series decomposition,redundancy processing,and data prediction.The predicted values of each demand response feature on the response day are obtained.Thirdly,the predicted data of various characteristics on the response day are used as demand response potential evaluation indicators to represent different demand response scenarios and adjustable loads,and a demand response potential evaluation model based on subjective and objective weight allocation is established to calculate the demand response potential of different adjustable loads in different demand response scenarios.Finally,the effectiveness of the method proposed in the article is verified through examples,providing a reference for load aggregators to formulate demand response schemes.

A Combination Prediction Model for Short Term Travel Demand of Urban Taxi

This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.

Optimal dispatching strategy for residential demand response considering load participation

To facilitate the coordinated and large-scale participation of residential flexible loads in demand response(DR),a load aggregator(LA)can integrate these loads for scheduling.In this study,a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR.First,based on the operational characteristics of flexible loads such as electric vehicles,air conditioners,and dishwashers,their DR participation,the base to calculate the compensation price to users,was determined by considering these loads as virtual energy storage.It was quantified based on the state of virtual energy storage during each time slot.Second,flexible loads were clustered using the K-means algorithm,considering the typical operational and behavioral characteristics as the cluster centroid.Finally,the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load.The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys,thereby improving the load management performance.

Stochastic programming based coordinated expansion planning of generation,transmission,demand side resources,and energy storage considering the DC transmission system

With the increasing penetration of wind and solar energies,the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems.A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations.Multiple types of system components,including demand response service entities,converter stations,DC transmission systems,cascade hydropower stations,and other traditional components,have been extensively modeled.Moreover,energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence.Demand-response service entities from the load side are used to reduce and move the demand during peak load periods.The uncertainties in wind,solar energy,and loads were simulated using stochastic programming.Finally,the effectiveness of the proposed model is verified through numerical simulations.

Demand-Responsive Transportation Vehicle Routing Optimization Based on Two-Stage Method

Demand-responsive transportation(DRT)is a flexible passenger service designed to enhance road efficiency,reduce peak-hour traffic,and boost passenger satisfaction.However,existing optimization methods for initial passenger requests fall short in addressing real-time passenger needs.Consequently,there is a need to develop realtime DRT route optimization methods that integrate both initial and real-time requests.This paper presents a twostage,multi-objective optimization model for DRT vehicle scheduling.The first stage involves an initial scheduling model aimed at minimizing vehicle configuration,and operational,and CO_(2)emission costs while ensuring passenger satisfaction.The second stage develops a real-time scheduling model to minimize additional operational costs,penalties for time window violations,and costs due to rejected passengers,thereby addressing real-time demands.Additionally,an enhanced genetic algorithm based on Non-dominated Sorting Genetic Algorithm-II(NSGA-II)is designed,incorporating multiple crossover points to accelerate convergence and improve solution efficiency.The proposed scheduling model is validated using a real network in Shanghai.Results indicate that realtime scheduling can serve more passengers,and improve vehicle utilization and occupancy rates,with only a minor increase in total operational costs.Compared to the traditional NSGA-II algorithm,the improved version enhances convergence speed by 31.7%and solution speed by 4.8%.The proposed model and algorithm offer both theoretical and practical guidance for real-world DRT scheduling.

Physics Guided Deep Learning-Based Model for Short-Term Origin–Destination Demand Prediction in Urban Rail Transit Systems Under Pandemic

Accurate origin–destination(OD)demand prediction is crucial for the efficient operation and management of urban rail transit(URT)systems,particularly during a pandemic.However,this task faces several limitations,including real-time availability,sparsity,and high-dimensionality issues,and the impact of the pandemic.Consequently,this study proposes a unified framework called the physics-guided adaptive graph spatial–temporal attention network(PAG-STAN)for metro OD demand prediction under pandemic conditions.Specifically,PAG-STAN introduces a real-time OD estimation module to estimate real-time complete OD demand matrices.Subsequently,a novel dynamic OD demand matrix compression module is proposed to generate dense real-time OD demand matrices.Thereafter,PAG-STAN leverages various heterogeneous data to learn the evolutionary trend of future OD ridership during the pandemic.Finally,a masked physics-guided loss function(MPG-loss function)incorporates the physical quantity information between the OD demand and inbound flow into the loss function to enhance model interpretability.PAG-STAN demonstrated favorable performance on two real-world metro OD demand datasets under the pandemic and conventional scenarios,highlighting its robustness and sensitivity for metro OD demand prediction.A series of ablation studies were conducted to verify the indispensability of each module in PAG-STAN.

Optimal Bidding Strategies of Microgrid with Demand Side Management for Economic Emission Dispatch Incorporating Uncertainty and Outage of Renewable Energy Sources

In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a smarter and more reliable electricity provider.DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines.Better bidding strategies,prepared by MG operators,decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources(RES).But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate.To solve these issues,this study suggests non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy sources.The uncertainty related to solar and wind units is modeled using lognormal and Weibull probability distributions.TOU-based DRP is used,especially considering the time of outages along with the time of peak loads and prices,to enhance the reliability of MG and reduce costs and emissions.

A Novel Defender-Attacker-Defender Model for Resilient Distributed Generator Planning with Network Reconfiguration and Demand Response

To improve the resilience of a distribution system against extreme weather,a fuel-based distributed generator(DG)allocation model is proposed in this study.In this model,the DGs are placed at the planning stage.When an extreme event occurs,the controllable generators form temporary microgrids(MGs)to restore the load maximally.Simultaneously,a demand response program(DRP)mitigates the imbalance between the power supply and demand during extreme events.To cope with the fault uncertainty,a robust optimization(RO)method is applied to reduce the long-term investment and short-term operation costs.The optimization is formulated as a tri-level defenderattacker-defender(DAD)framework.At the first level,decision-makers work out the DG allocation scheme;at the second level,the attacker finds the optimal attack strategy with maximum damage;and at the third level,restoration measures,namely distribution network reconfiguration(DNR)and demand response are performed.The problem is solved by the nested column and constraint generation(NC&CG)method and the model is validated using an IEEE 33-node system.Case studies validate the effectiveness and superiority of the proposed model according to the enhanced resilience and reduced cost.

Vehicle routing optimization algorithm based on time windows and dynamic demand

To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,based on the ideas of pre-optimization and real-time optimization,a two-stage planning model of dynamic demand based vehicle routing problem with time windows was established.At the pre-optimization stage,an improved genetic algorithm was used to obtain the pre-optimized distribution route,a large-scale neighborhood search method was integrated into the mutation operation to improve the local optimization performance of the genetic algorithm,and a variety of operators were introduced to expand the search space of neighborhood solutions;At the real-time optimization stage,a periodic optimization strategy was adopted to transform a complex dynamic problem into several static problems,and four neighborhood search operators were used to quickly adjust the route.Two different scale examples were designed for experiments.It is proved that the algorithm can plan the better route,and adjust the distribution route in time under the real-time constraints.Therefore,the proposed algorithm can provide theoretical guidance for suppliers to solve the dynamic demand based vehicle routing problem.

BOD5荧光测定法与标准方法在地表水监测中的对比性研究

BOD5检测是一种基于微生物的污染负荷评估,有文献证明微生物代谢物类色氨酸的荧光强度与BOD5测量之间存在相关性.对长江流域常州段地表水水样的荧光强度与BOD5值之间的关系进行了探究,并通过考察浊度、pH、离子干扰和区域差异来评估荧光法应用于水质BOD5污染评价中的潜力.研究发现,地表水的pH和离子干扰对荧光法检测BOD5并不构成明显影响.区域差异对比结果t最大值为1.227,不构成显著性差异.荧光法水样检测结果与标准方法相比,相关性系数r为0.9099,具有明显相关性.结果表明,荧光法应用于地表水BOD5检测影响因素少、检测速度快、自动化程度高、结果稳定可靠,可以作为BOD5检测的替代方法,成为表征水中可生物降解污染物污染程度的主要手段.

Stochastic Air Traffic Flow Management for Demand and Capacity Balancing Under Capacity Uncertainty

This paper introduces an innovative approach to the synchronized demand-capacity balance with special focus on sector capacity uncertainty within a centrally controlled collaborative air traffic flow management(ATFM)framework.Further with previous study,the uncertainty in capacity is considered as a non-negligible issue regarding multiple reasons,like the impact of weather,the strike of air traffic controllers(ATCOs),the military use of airspace and the spatiotemporal distribution of nonscheduled flights,etc.These recessive factors affect the outcome of traffic flow optimization.In this research,the focus is placed on the impact of sector capacity uncertainty on demand and capacity balancing(DCB)optimization and ATFM,and multiple options,such as delay assignment and rerouting,are intended for regulating the traffic flow.A scenario optimization method for sector capacity in the presence of uncertainties is used to find the approximately optimal solution.The results show that the proposed approach can achieve better demand and capacity balancing and determine perfect integer solutions to ATFM problems,solving large-scale instances(24 h on seven capacity scenarios,with 6255 flights and 8949 trajectories)in 5-15 min.To the best of our knowledge,our experiment is the first to tackle large-scale instances of stochastic ATFM problems within the collaborative ATFM framework.

A Note on an Order Level Inventory Model with Varying Two-Phased Demand and Time-Proportional Deterioration

The main purpose of this paper is to generalize the effect of two-phased demand and variable deterioration within the EOQ (Economic Order Quantity) framework. The rate of deterioration is a linear function of time. The two-phased demand function states the constant function for a certain period and the quadratic function of time for the rest part of the cycle time. No shortages as well as partial backlogging are allowed to occur. The mathematical expressions are derived for determining the optimal cycle time, order quantity and total cost function. An easy-to-use working procedure is provided to calculate the above quantities. A couple of numerical examples are cited to explain the theoretical results and sensitivity analysis of some selected examples is carried out.