Hasil untuk "Sewage collection and disposal systems. Sewerage"

Liao Huafeng, Gao Lan, Ruan Yang et al.

【目的】国内深圳、上海、江苏、浙江等地已经在高品质供水上进行了大量探索，并相继发布了地方供水相关的强制性或鼓励性标准。技术路线上，主要以增设“臭氧+活性炭”深度处理单元，构建长流程处理工艺为主。较长流程往往意味着较大的投资和较高的运维成本，因此，探索采用强化常规工艺实现高品质供水更符合绿色、低碳的高质量发展理念。【方法】浙江省衢州市第四水厂是衢州市首座按照《浙江省城市供水现代化水厂评价标准》建设的水厂，设计采用运行安全可靠的“折板絮凝—平流沉淀—V型滤池”传统短流程工艺，并采取了多种强化措施，既满足了高标准出水水质要求，又可大幅降低投资和运行成本。【结果】该项目于2022年6月底正式并网对外供水，根据2022年7月—2022年12月运行数据，实际处理水量为5.0万～8.2万m 3 /d,实际出厂水质各项指标均达到浙江省现代化水厂出厂水优质标准，其中每月出厂水浑浊度为0.03～0.06 NTU,远优于设计目标，实现了高品质供水的核心控制指标要求。【结论】该项目作为浙江省重点工程，具有良好的示范效益，文章以该工程为例，从水厂原水水质、供水水质标准、工艺设计、总平面布置及实际运行效果等方面，介绍了该水厂采用多种措施强化常规工艺实现高品质供水的工程实践，为国内其他采用强化常规工艺高品质供水项目设计提供借鉴和参考。

Yin Shuangxing, Li Guiwei, Qi Tiantian et al.

【目的】北方某大型水库监测到位于该水库的引水隧洞水体中锰(Mn)浓度出现季节性升高现象。【方法】为防止管网发生Mn致“黄水”,基于前期研发的粉末活性炭(PAC)催化氯(Cl 2 )氧化去除Mn(Ⅱ)方法，以Mn(Ⅱ)质量浓度低于20μg/L为去除目标，本文利用水库原水进行不同条件下的应急氧化除Mn(Ⅱ)试验。【结果】在Mn(Ⅱ)初始质量浓度为400μg/L时，单独使用2 mg/L Cl 2 或10 mg/L煤基PAC在120 min内对Mn(Ⅱ)的去除率仅有2%左右。但当煤基或椰壳PAC结合Cl 2 同时使用时，Mn(Ⅱ)在200、400、800μg/L等初始质量浓度下，通过不同浓度的PAC和Cl 2 的投加搭配方案下，在60 min内，Mn(Ⅱ)的最终去除率均能够达到95%以上，而且椰壳PAC除Mn(Ⅱ)效果显著高于煤基PAC。【结论】在实际运用中，通过构建PAC催化Cl 2 氧化Mn(Ⅱ)反应体系，能够高效除Mn(Ⅱ),此种方式作为水厂紧急处理手段是可行的。

Sanket A. Salunkhe, George P. Kontoudis

Multi-robot systems require scalable and federated methods to model complex environments under computational and communication constraints. Gaussian Processes (GPs) offer robust probabilistic modeling, but suffer from cubic computational complexity, limiting their applicability in large-scale deployments. To address this challenge, we introduce the pxpGP, a novel distributed GP framework tailored for both centralized and decentralized large-scale multi-robot networks. Our approach leverages sparse variational inference to generate a local compact pseudo-representation. We introduce a sparse variational optimization scheme that bounds local pseudo-datasets and formulate a global scaled proximal-inexact consensus alternating direction method of multipliers (ADMM) with adaptive parameter updates and warm-start initialization. Experiments on synthetic and real-world datasets demonstrate that pxpGP and its decentralized variant, dec-pxpGP, outperform existing distributed GP methods in hyperparameter estimation and prediction accuracy, particularly in large-scale networks.

Evangelia Gkaravela, Hao Chen

As human activities on the Moon expand through initiatives like NASA's Artemis program, the need for sustainable post-mission disposal strategies becomes critical to maintaining the lunar environment. This paper analyzes the logistics and environmental implications of waste products generated by In-Situ Resource Utilization technologies employed in oxygen production on the Moon. The study examines the inputs, generation of products, and the resulting byproducts from Molten Regolith Electrolysis, Soil/Water Extraction, and Direct Water Electrolysis systems. These technologies yield varied byproducts, including slag, metals and volatiles, each presenting unique challenges for disposal and recycling. The analysis assesses the economic and ecological impacts of In-Situ Resource Utilization activities on lunar operations using a multi-commodity flow model adapted from cislunar logistics frameworks. The results inform that ISRU-enabled missions achieve a significant threefold cost reduction. However, the management of byproducts remains a critical challenge, demanding innovative solutions to address their impact and support scalable and sustainable lunar exploration.

Tianyi Huang, Richard Xiong

This paper introduces a cost-effective robotic handwriting system designed to replicate human-like handwriting with high precision. Combining a Raspberry Pi Pico microcontroller, 3D-printed components, and a machine learning-based handwriting generation model implemented via TensorFlow, the system converts user-supplied text into realistic stroke trajectories. By leveraging lightweight 3D-printed materials and efficient mechanical designs, the system achieves a total hardware cost of approximately \$56, significantly undercutting commercial alternatives. Experimental evaluations demonstrate handwriting precision within $\pm$0.3 millimeters and a writing speed of approximately 200 mm/min, positioning the system as a viable solution for educational, research, and assistive applications. This study seeks to lower the barriers to personalized handwriting technologies, making them accessible to a broader audience.

Mostafaali Ayubirad, Hamid R. Ossareh

The prediction-based nonlinear reference governor (PRG) is an add-on algorithm to enforce constraints on pre-stabilized nonlinear systems by modifying, whenever necessary, the reference signal. The implementation of PRG carries a heavy computational burden, as it may require multiple numerical simulations of the plant model at each sample time. To this end, this paper proposes an alternative approach based on machine learning, where we first use a regression neural network (NN) to approximate the input-output map of the PRG from a set of training data. During the real-time operation, at each sample time, we use the trained NN to compute a nominal reference command, which may not be constraint admissible due to training errors and limited data. We adopt a novel sensitivity-based approach to minimally adjust the nominal reference while ensuring constraint enforcement. We thus refer to the resulting control strategy as the modified neural network reference governor (MNN-RG), which is significantly more computationally efficient than the PRG. The computational and theoretical properties of MNN-RG are presented. Finally, the effectiveness and limitations of the proposed method are studied by applying it as a load governor for constraint management in automotive fuel cell systems through simulation-based case studies.

Moh. Kamalul Wafi, Milad Siami

This paper addresses the challenge of network synchronization under limited communication, involving heterogeneous agents with different dynamics and various network topologies, to achieve consensus. We investigate the distributed adaptive control for interconnected unknown linear subsystems with a leader and followers, in the presence of input-output disturbance. We enhance the communication within multi-agent systems to achieve consensus under the leadership's guidance. While the measured variable is similar among the followers, the incoming measurements are weighted and constructed based on their proximity to the leader. We also explore the convergence rates across various balanced topologies (Star-like, Cyclic-like, Path, Random), featuring different numbers of agents, using three distributed algorithms, ranging from first- to high-order tuners to effectively address time-varying regressors. The mathematical foundation is rigorously presented from the network designs of the unknown agents following a leader, to the distributed methods. Moreover, we conduct several numerical simulations across various networks, agents and tuners to evaluate the effects of sparsity in the interaction between subsystems using the $L_2-$norm and $L_\infty-$norm. Some networks exhibit a trend where an increasing number of agents results in smaller errors, although this is not universally the case. Additionally, patterns observed at initial times may not reliably predict overall performance across different networks. Finally, we demonstrate that the proposed modified high-order tuner outperforms its counterparts, and we provide related insights along with our conclusions.

Ahmad Mohammad Saber, Amr Youssef, Davor Svetinovic et al.

Line Current Differential Relays (LCDRs) are high-speed relays progressively used to protect critical transmission lines. However, LCDRs are vulnerable to cyberattacks. Fault-Masking Attacks (FMAs) are stealthy cyberattacks performed by manipulating the remote measurements of the targeted LCDR to disguise faults on the protected line. Hence, they remain undetected by this LCDR. In this paper, we propose a two-module framework to detect FMAs. The first module is a Mismatch Index (MI) developed from the protected transmission line's equivalent physical model. The MI is triggered only if there is a significant mismatch in the LCDR's local and remote measurements while the LCDR itself is untriggered, which indicates an FMA. After the MI is triggered, the second module, a neural network-based classifier, promptly confirms that the triggering event is a physical fault that lies on the line protected by the LCDR before declaring the occurrence of an FMA. The proposed framework is tested using the IEEE 39-bus benchmark system. Our simulation results confirm that the proposed framework can accurately detect FMAs on LCDRs and is not affected by normal system disturbances, variations, or measurement noise. Our experimental results using OPAL-RT's real-time simulator confirm the proposed solution's real-time performance capability.

Vittal Srinivasan, Nak-seung Patrick Hyun

In nature, different species of smaller animals produce ultra-fast movements to aid in their locomotion or protect themselves against predators. These ultra-fast impulsive motions are possible, as often times, there exist a small latch in the organism that could hold the potential energy of the system, and once released, generate an impulsive motion. These types of systems are classified as Latch Mediated Spring Actuated (LaMSA) systems, a multi-dimensional, multi-mode hybrid system that switches between a latched and an unlatched state. The LaMSA mechanism has been studied extensively in the field of biology and is observed in a wide range of animal species, such as the mantis shrimp, grasshoppers, and trap-jaw ants. In recent years, research has been done in mathematically modeling the LaMSA behavior with physical implementations of the mechanism. A significant focus is given to mimicking the physiological behavior of the species and following an end-to-end trajectory of impulsive motion. This paper introduces a foundational analysis of the theoretical dynamics of the contact latch-based LaMSA mechanism. The authors answer the question on what makes these small-scale systems impulsive, with a focus on the intrinsic properties of the system using bifurcations. Necessary and sufficient conditions are derived for the existence of the saddle fixed points. The authors propose a mathematical explanation for mediating the latch when a saddle node exists, and the impulsive behavior after the bifurcation happens.

Hyunki Seong, Chanyoung Chung, David Hyunchul Shim

In this letter, we propose a model parameter identification method via a hyperparameter optimization scheme (MI-HPO). Our method adopts an efficient explore-exploit strategy to identify the parameters of dynamic models in a data-driven optimization manner. We utilize our method for model parameter identification of the AV-21, a full-scaled autonomous race vehicle. We then incorporate the optimized parameters for the design of model-based planning and control systems of our platform. In experiments, MI-HPO exhibits more than 13 times faster convergence than traditional parameter identification methods. Furthermore, the parametric models learned via MI-HPO demonstrate good fitness to the given datasets and show generalization ability in unseen dynamic scenarios. We further conduct extensive field tests to validate our model-based system, demonstrating stable obstacle avoidance and high-speed driving up to 217 km/h at the Indianapolis Motor Speedway and Las Vegas Motor Speedway. The source code for our work and videos of the tests are available at https://github.com/hynkis/MI-HPO.

T. M. Saita, P. L. Natti, E. R. Cirilo et al.

This study presents numerical simulations of faecal coliforms dynamics in Luruaco lake, located in Atlántico Department, Colombia. The velocity field is obtained through a two-dimensional horizontal (2DH) model of Navier-Stokes equations system. The transport equation of faecal coliforms concentration is provided from a convective-diffusive-reactive equation. The lake's geometry is built through cubic spline and multi-block methods. The discretization method by Finite Differences and the First Order Upwind (FOU) are applied to the 2DH model. The Mark and Cell (MAC) method is used to determine numerically the velocity field of water flow. Numerical simulations are carried out for a 72-hour period in order to understand the influence of faecal coliforms injections from each tributary. From the qualitative and quantitative analysis of the factors that influence faecal coliforms dynamics, proposals are presented, which aim to reduce contamination in some regions of Lake Luruaco. The numerical simulations show that the best option to improve water quality in the lake is the implementation of two actions, the diversion of the Limón stream to the Negro stream and the installation of a sewage treatment plant at the mouth of the Negro stream. Other less expensive proposals are also presented.

Sze Kwan Cheah, Diganta Bhattacharjee, Maziar S. Hemati et al.

This letter presents a framework for synthesizing a robust full-state feedback controller for systems with unknown nonlinearities. Our approach characterizes input-output behavior of the nonlinearities in terms of local norm bounds using available sampled data corresponding to a known region about an equilibrium point. A challenge in this approach is that if the nonlinearities have explicit dependence on the control inputs, an a priori selection of the control input sampling region is required to determine the local norm bounds. This leads to a "chicken and egg" problem, where the local norm bounds are required for controller synthesis, but the region of control inputs needed to be characterized cannot be known prior to synthesis of the controller. To tackle this issue, we constrain the closed-loop control inputs within the sampling region while synthesizing the controller. As the resulting synthesis problem is non-convex, three semi-definite programs (SDPs) are obtained through convex relaxations of the main problem, and an iterative algorithm is constructed using these SDPs for control synthesis. Two numerical examples are included to demonstrate the effectiveness of the proposed algorithm.

Farzad Vaziri Alamdarlo, Ghahraman Solookinejad, Fazel Zahakifar et al.

One of the most important environmental challenges in the world is environmental pollution with toxic and dangerous heavy metals, which causes various environmental effects. Lead is a heavy metal that can be removed in a variety of methods. In this study, graphene oxide adsorbent was prepared by modified Hummers method and functionalized with aminomethyl phosphonic acid and its application for the adsorption of lead ions from aqueous solutions in a batch sorption process was investigated. The effect of several batch adsorption parameters such as contact time, pH, adsorbent dose, initial concentration and temperature were investigated. The kinetic data were analyzed by Pseudo-first-order, Pseudo-second-order and Double- exponential kinetic models. The results showed that experimental data was fitted well by Pseudo-second-order kinetic model. The Freundlich and Langmuir isotherm models were applied to describe the equilibrium data. The maximum adsorption capacity of lead ions with graphene oxide and functionalized-graphene oxide adsorbents was found to be 187.80 and 209.41 mg/g at a pH of 2.0 and temperature of 45 ºC, respectively. Furthermore, the graphene oxide and functionalized-graphene oxide adsorbents were regenerated by HCl/HNO3 solution and the adsorption capacity did not change remarkably after seven adsorption-desorption cycles.

Gioele Zardini, Nicolas Lanzetti, Marco Pavone et al.

Challenged by urbanization and increasing travel needs, existing transportation systems need new mobility paradigms. In this article, we present the emerging concept of autonomous mobility-on-demand, whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers. We provide a comprehensive review of methods and tools to model and solve problems related to autonomous mobility-on-demand systems. Specifically, we first identify problem settings for their analysis and control, from both operational and planning perspectives. We then review modeling aspects, including transportation networks, transportation demand, congestion, operational constraints, and interactions with existing infrastructure. Thereafter, we provide a systematic analysis of existing solution methods and performance metrics, highlighting trends and trade-offs. Finally, we present various directions for further research.

Joseph Breeden, Kunal Garg, Dimitra Panagou

This paper presents conditions for ensuring forward invariance of safe sets under sampled-data system dynamics with piecewise-constant controllers and fixed time-steps. First, we introduce two different metrics to compare the conservativeness of sufficient conditions on forward invariance under piecewise-constant controllers. Then, we propose three approaches for guaranteeing forward invariance, two motivated by continuous-time barrier functions, and one motivated by discrete-time barrier functions. All proposed conditions are control affine, and thus can be incorporated into quadratic programs for control synthesis. We show that the proposed conditions are less conservative than those in earlier studies, and show via simulation how this enables the use of barrier functions that are impossible to implement with the desired time-step using existing methods.

Shankar A. Deka, Donggun Lee, Claire J. Tomlin

Collaboration between interconnected cyber-physical systems is becoming increasingly pervasive. Time-delays in communication channels between such systems are known to induce catastrophic failure modes, like high frequency oscillations in robotic manipulators in bilateral teleoperation or string instability in platoons of autonomous vehicles. This paper considers nonlinear time-delay systems representing coupled robotic agents, and proposes controllers that are robust to time-varying communication delays. We introduce approximations that allow the delays to be considered as implicit control inputs themselves, and formulate the problem as a zero-sum differential game between the stabilizing controllers and the delays acting adversarially. The ensuing optimal control law is finally compared to known results from Lyapunov-Krasovskii based approaches via numerical experiments.

Bahram Marami, Atabak Reza Royaee

Every year, millions of pounds of medicines remain unused in the U.S. and are subject to an in-home disposal, i.e., kept in medicine cabinets, flushed in toilet or thrown in regular trash. In-home disposal, however, can negatively impact the environment and public health. The drug take-back programs (drug take-backs) sponsored by the Drug Enforcement Administration (DEA) and its state and industry partners collect unused consumer medications and provide the best alternative to in-home disposal of medicines. However, the drug take-backs are expensive to operate and not widely available. In this paper, we show that artificial intelligence (AI) can be applied to drug take-backs to render them operationally more efficient. Since identification of any waste is crucial to a proper disposal, we showed that it is possible to accurately identify loose consumer medications solely based on the physical features and visual appearance. We have developed an automatic technique that uses deep neural networks and computer vision to identify and segregate solid medicines. We applied the technique to images of about one thousand loose pills and succeeded in correctly identifying the pills with an accuracy of 0.912 and top-5 accuracy of 0.984. We also showed that hazardous pills could be distinguished from non-hazardous pills within the dataset with an accuracy of 0.984. We believe that the power of artificial intelligence could be harnessed in products that would facilitate the operation of the drug take-backs more efficiently and help them become widely available throughout the country.

Shihao Xie, Giovanni Russo, Richard Middleton

This paper is concerned with the study of scalability in nonlinear heterogeneous networks affected by communication delays and disturbances. After formalizing the notion of scalability, we give two sufficient conditions to assess this property. Our results can be used to study leader-follower and leaderless networks and also allow to consider the case when the desired configuration of the system changes over time. We show how our conditions can be turned into design guidelines to guarantee scalability and illustrate their effectiveness via numerical examples.

Mehrnaz Ghoochian, Soheil Sobhanardakani, Homayon Ahmad Panahi et al.

The drug residues as a contaminant in water resources can lead to risks for humans and other biologists. Therefore, removal of them from the effluents is essential for environmental protection. Therefore, this study was conducted with the aim of evaluation of the removal efficacy of tamoxifen from aqueous solutions using magnetite nanoparticles modified with PAMAM. In this study, magnetite nanoparticles modified with PAMAM were synthesized by co-precipitation method and used as an adsorbent for the removal of tamoxifen from aqueous solution. Magnetite nanoparticles modified with PAMAM characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), pHpzc, Fourier transform infrared spectroscopy (FTIR), and vibrating sample magnetometry (VSM) analysis methods. We used UV–visible spectrophotometer to determined tamoxifen in the solution at 236 nm. The results showed that removal efficiency increased until 0.03 g adsorbent, pH =7.0 and 40 min contact time. Also, the adsorption process followed the Freundlich adsorption isotherm and pseudo-second-order kinetic model. Based on the results it can be admitted that the magnetite nanoparticles modified with PAMAM can be used as an effective and available absorbent to remove tamoxifen from sewage and pharmaceutical wastewater.

Reyhaneh Rahnama, Maedeh Nabipour Sangroodi

In this paper, a new method for preconcentration and measurement of trace amounts of nickel in aqueous samples by magnetic solid phase extraction (MSPE) via magnetic carbon nanotubes (Mag-CNTs) was developed. In order to increase selectivity, α-Furildioxime was used as chelating agent. In order to do extraction, optimum amount of ligand was added to the nickel sample and pH was set on 9, then 7 ml. of adsorbent was added and stirred for 15 minutes. After that, aqueous phase and adsorbent were separated by a strong magnet. Finally, the absorption was measured via flame atomic absorption spectrometry by analyte elution from the absorbent with an appropriate solution. Parameters affecting the extraction and preconcentration of nickel were investigated and optimized. Under optimum conditions, the calibration curve was linear in concentration range from 2.5 to 375 µg L-1 and the detection limit was 0.8 µg L-1 of nickel. The method was applied for determination of nickel in aqueous samples. The relative efficiency values of nickel measurement in aqueous samples were from 98.7% to 102.1%.  Results indicated that Mag-CNTs can be used as an effective and inexpensive absorbent for preconcentration and extraction of nickel from actual samples.