Control Technology Research Articles

Leak and Burst Detection in Water Distribution Network Using Logic- and Machine Learning-Based Approaches

Urban water systems worldwide are confronted with the dual challenges of dwindling water resources and deteriorating infrastructure, emphasising the critical need to minimise water losses from leakage. Conventional methods for leak and burst detection often prove inadequate, leading to prolonged leak durations and heightened maintenance costs. This study investigates the efficacy of logic- and machine learning-based approaches in early leak detection and precise location identification within water distribution networks. By integrating hardware and software technologies, including sensor technology, data analysis, and study on the logic-based and machine learning algorithms, innovative solutions are proposed to optimise water distribution efficiency and minimise losses. In this research, we focus on a case study area in the Sunbury region of Victoria, Australia, evaluating a pumping main equipped with Supervisory Control and Data Acquisition (SCADA) sensor technology. We extract hydraulic characteristics from SCADA data and develop logic-based algorithms for leak and burst detection, alongside state-of-the-art machine learning techniques. These methodologies are applied to historical data initially and will be subsequently extended to live data, enabling the real-time detection of leaks and bursts. The findings underscore the complementary nature of logic-based and machine learning approaches. While logic-based algorithms excel in capturing straightforward anomalies based on predefined conditions, they may struggle with complex or evolving patterns. Machine learning algorithms enhance detection by learning from historical data, adapting to changing conditions, and capturing intricate patterns and outliers. The comparative analysis of machine learning models highlights the superiority of the local outlier factor (LOF) in anomaly detection, leading to its selection as the final model. Furthermore, a web-based platform has been developed for leak and burst detection using a selected machine learning model. The success of machine learning models over traditional logic-based approaches underscores the effectiveness of data-driven, probabilistic methods in handling complex data patterns and variations. Leveraging statistical and probabilistic techniques, machine learning models offer adaptability and superior performance in scenarios with intricate or dynamic relationships between variables. The findings demonstrate that the proposed methodology can significantly enhance the early detection of leaks and bursts, thereby minimising water loss and associated economic costs. The implications of this study are profound for the scientific community and stakeholders, as it provides a scalable and efficient solution for water pipeline monitoring. Implementing this approach can lead to more proactive maintenance strategies, ultimately contributing to the sustainability and resilience of urban water infrastructure systems.

On-Demand Transport Bubbles Adhering to Noncontiguous Patterned Superhydrophobic Surfaces Using a Superhydrophobic Tweezer.

Bubble transportation and related flotation are ubiquitous phenomena in nature and industry. Various surfaces with distinct morphologies and specific wettability properties have been engineered by organisms in nature and by humans to facilitate the targeted movement of bubbles. However, existing methods predominantly rely on continuous surfaces, limiting the ability of bubbles to deviate from their path before reaching their intended destination. Therefore, directional transportation of bubbles using noncontiguous surfaces still remains a significant challenge. Inspired by water spiders' ability to capture bubbles underwater using their hydrophobic surface for survival, we propose a novel transport strategy that utilizes patterned superhydrophobic surfaces (PSHSs) and a superhydrophobic tweezer. This strategy is implemented by switching between the hood mode and puncture mode of the moving three-phase contact lines to load and unload the bubble. To quantitatively evaluate the loss ratio of the bubble during transportation, a simple and exquisite bubble-weighing apparatus is devised. Our findings indicate that circular PSHSs demonstrate superior bubble adhesion and achieve the highest bubble transport ratio of 95.1%. In order to validate the promising application of this novel method, we employ the computer numerical control (CNC) technology to facilitate the autonomous loading and precise transportation of underwater bubbles, as well as the blending and ionization of combustible gas bubbles with air bubbles at different volume ratios.

Phase Transformation of AlV55 Alloy at High Temperature

Vanadium–aluminum alloy is an important intermediate alloy for preparing aviation grade titanium alloys, and its product quality directly affects the finished product quality of titanium alloys. In this study, focusing on the problems of high powder content (19.8%) and low product yield in AlV55 alloy products, we conduct research on alloy quality control technology and implement a vanadium–aluminum alloy cooling crystallization control process. The research results indicate that there are three phases in AlV55 alloy, namely Al8V5, AlV, and Al2V3 phases. As the temperature decreases, the AlV phase gradually decomposes into Al8V5 phase and Al2V3 phase, and the proportion of Al8V5 phase is positively correlated with the fineness. Rapid cooling can reduce the formation of Al8V5 phase. The experimental results show that high-temperature water quenching can increase the proportion of vanadium–aluminum solid solution phase in the alloy from 19.03% to 31.76%, and reduce the fine powder rate to 13.2%, providing important product quality control means and technical support for the production of vanadium–aluminum alloys.

Climate and air quality impact of using ammonia as an alternative shipping fuel

As carbon-free fuel, ammonia has been proposed as an alternative fuel to facilitate maritime decarbonization. Deployment of ammonia-powered ships is proposed as soon as 2024. However, NO x , NH3 and N2O from ammonia combustion could impact air quality and climate. In this study, we assess whether and under what conditions switching to ammonia fuel might affect climate and air quality. We use a bottom–up approach combining ammonia engine experiment results and ship track data to estimate global tailpipe NO x , NH3 and N2O emissions from ammonia-powered ships with two possible engine technologies (NH3–H2 (high NO x , low NH3 emissions) vs pure NH3 (low NO x , very high NH3 emissions) combustion) under three emission regulation scenarios (with corresponding assumptions in emission control technologies), and simulate their air quality impacts using GEOS–Chem high performance global chemical transport model. We find that the tailpipe N2O emissions from ammonia-powered ships have climate impacts equivalent to 5.8% of current shipping CO2 emissions. Globally, switching to NH3–H2 engines avoids 16 900 mortalities from PM2.5 and 16 200 mortalities from O3 annually, while the unburnt NH3 emissions (82.0 Tg NH3 yr−1) from pure NH3 engines could lead to 668 100 additional mortalities from PM2.5 annually under current legislation. Requiring NH3 scrubbing within current emission control areas leads to smaller improvements in PM2.5-related mortalities (22 100 avoided mortalities for NH3–H2 and 623 900 additional mortalities for pure NH3 annually), while extending both Tier III NO x standard and NH3 scrubbing requirements globally leads to larger improvement in PM2.5-related mortalities associated with a switch to ammonia-powered ships (66 500 avoided mortalities for NH3–H2 and 1200 additional mortalities for pure NH3 annually). Our findings suggest that while switching to ammonia fuel would reduce tailpipe greenhouse gas emissions from shipping, stringent ammonia emission control is required to mitigate the potential adverse effects on air quality.

Archaeometric characterization of pottery from the Houjiazhai site (ca. 7.3-5.7 ka BP), Anhui Province, China.

A combination of polarized light microscopy (PLM), X-ray diffraction (XRD), X-ray fluorescence (XRF), neutron activation analysis (NAA) and thermogravimetry (TG) was used on the pottery from the Houjiazhai site in Anhui, China. The analytical data demonstrate that the clay raw material of the Houjiazhai pottery clay was ordinary fusible clay. Phase I pottery consisted of shell-tempered pottery (with a high CaO content), plant-tempered pottery (with a high P2O5 content), and sand-tempered pottery. The pottery body of Houjiazhai was composed of quartz, feldspars, and minor amounts of mica. In addition, the shell-tempered pottery contains calcite. The results of the NAA analysis suggest that the samples from the two phases have the same place of production. The presence of calcite in the mixture indicates that the firing temperature for shell-tempered pottery from Phase I should not exceed 800 °C, while the thermal expansion curve shows that clay pottery from Phase II could reach 900 °C. Houjiazhai people have made progress in temperature control technology and can fire pottery with higher temperatures.

Development of an Improved Communication Control System for ATV Electric Vehicles Using MRS Developers Studio

Transmission, energy management, and distribution systems are critical components of modern electric vehicles, encompassing all sectors of the power system through communication control technology. One widely used communication system in electric vehicles is the Controller Area Network (CAN). This research aims to investigate the development of CAN BUS technology, adapted from large trucks, to control the communication system within an ATV electric vehicle using a communication format similar to bus Communication. The communication control system includes several components: the engine switch, headlight, turn signal, emergency light, horn, forward/reverse gear, and accelerator. The system’s communication protocols were developed using MRS Developers Studio version 1.40 software to create the data transmission and reception formats for the vehicle’s components. The communication system employs three PLC 1.033.30B.00 type E control boxes, each with limited analog and digital input/output ports. The sequence of communication control begins with the engine start/stop operation, as the system will not function unless the engine is started first. The headlight operation is processed within the CAN BUS1 control box. Simultaneously, the turn signal and emergency light functions are controlled by CAN BUS1 and displayed on both the CAN BUS2 (front of the vehicle) and CAN BUS3 (rear of the vehicle) control boxes. Additionally, the accelerator function is managed within the CAN BUS2 control box and displayed on the CAN BUS3 control box. However, this operation is contingent upon the forward/reverse gear selection, managed by CAN BUS1 and processed by CAN BUS3. All system operations are designed within the software’s programming paths. The communication system operates using CAN-High and CAN-Low lines, and communication data fields can be monitored using the PCAN-View software version 4.2.1.533. This study demonstrates the feasibility and effectiveness of adapting CAN BUS technology for ATV electric vehicles, providing insights into the integration and control of various vehicular components within a unified communication framework.

Testing a novel haptic tram master controller technology via virtual reality: feasibility and user acceptance considerations

Testing a novel haptic tram master controller technology via virtual reality: feasibility and user acceptance considerations

Design of Ship Trajectory Control Method Integrating Self Disturbance Rejection and Neural Network

To reduce the ship trajectory control difficulty, a ship trajectory control method based on active disturbance rejection control and radial basis function neural network is designed. Firstly, a separated model structure is proposed to model the ship’s navigation system, followed by the introduction of active disturbance rejection control technology to control the ship’s trajectory, and the fusion of radial basis function to improve the parameter adjustment effect. It was found that on the AIS and MSSIS datasets when the system was iterated 25 and 22 times, respectively, the fitness values of the research method were as high as 99.46 and 99.51. In addition, when the accuracy of all algorithms was 0.900, the recall rate of the research method was significantly the highest, at 0.752. When the recall rate was 0.900, the accuracy of the research method was significantly the highest, reaching 0.869. In practical applications, when there was no external interference, the heading was a square wave signal, and when the time reached 90.11, the proposed method operated with a small difference between the planned heading and the actual heading. In addition, it was found that the control effect of the research method on ship operation remains highly stable when external interference is added during ship operation. The above results indicate that the designed method has significant advantages in ship trajectory control tasks and can effectively enhance the navigation safety and stability of ships.

Suppressive effects of potassium salt modified dry water material on hydrogen/methane mixture explosion

As a new type of energy, hydrogen-enriched methane mixed fuel has a high risk of deflagration in the application process. In order to better understand the deflagration and inhibition characteristics of hydrogen-enriched methane fuel, the effect of potassium salt modified dry water material (DW) on the deflagration characteristics of hydrogen-enriched methane fuel was studied by 20 L spherical explosive apparatus, combined with pyrolysis characteristics and kinetic model. The results show that when the addition of different modifier concentrations, it can be divided into three types: small curvature, medium curvature and large curvature according to the change of pressure rising rate. With the increase of the proportion of potassium salt modifiers, the number of areas with small curvature is gradually increasing, the number of large curvature is gradually decreasing, and the number of medium curvature is almost unchanged. When the proportion of potassium salt modifier is 15% and ϕ ≤ 0.7, all enter the area below the medium curvature. When the addition of different modifiers, 15%KCl-DW has the best inhibition effect on Pmax and (dP/dt) max, while CH3COOK-DW and K2CO3-DW have better inhibition effect on Δt2 and Δt2. In general, when the hydrogen addition ratio is less than 70%, the addition of inhibitors can significantly reduce the explosion intensity. In terms of inhibition mechanism, we established the decomposition model of potassium salt modified dry water and found that potassium salt modified dry water will produce a large amount of gaseous K and KOH in the explosion system. And gaseous K and KOH will catalyze each other and combine with H and OH to form stable H2O, which significantly reduces the mass fraction of H and OH radicals in the chain reaction. The results of this paper provide a theoretical basis for the safe use of hydrogen-enriched methane fuel and the research and development of explosion emergency prevention and control technology.

Neural Simulation of Actions for Serpentine Robots

The neural or mental simulation of actions is a powerful tool for allowing cognitive agents to develop Prospection Capabilities that are crucial for learning and memorizing key aspects of challenging skills. In previous studies, we developed an approach based on the animation of the redundant human body schema, based on the Passive Motion Paradigm (PMP). In this paper, we show that this approach can be easily extended to hyper-redundant serpentine robots as well as to hybrid configurations where the serpentine robot is functionally integrated with a traditional skeletal infrastructure. A simulation model is analyzed in detail, showing that it incorporates spatio-temporal features discovered in the biomechanical studies of biological hydrostats, such as the elephant trunk or octopus tentacles. It is proposed that such a generative internal model could be the basis for a cognitive architecture appropriate for serpentine robots, independent of the underlying design and control technologies. Although robotic hydrostats have received a lot of attention in recent decades, the great majority of research activities have been focused on the actuation/sensorial/material technologies that can support the design of hyper-redundant soft/serpentine robots, as well as the related control methodologies. The cognitive level of analysis has been limited to motion planning, without addressing synergy formation and mental time travel. This is what this paper is focused on.

Research on autonomous berthing control of MASS based on real time kinematic

Technologies for autonomous navigation of MASS (Maritime Autonomous Surface Ship) in open waters are advancing rapidly. However, the autonomous berthing technology, which represents the final phase of MASS achieving fully autonomous navigation, is still in the process of maturing. Consequently, the study of autonomous berthing control technology holds significant practical importance for the realization of fully autonomous navigation in MASS. This paper proposes an autonomous berthing control program for MASS, based on a MFAC (Model Free Adaptive Control) algorithm that integrates control technology and perception technology. The program encompasses the design of both the berthing logic algorithm and control algorithm. For the controllers, the Preview Deviation Yaw Control System and Speed Control System are designed using the CFDL-MFAC (Compact Form Dynamic Linearization based MFAC) algorithm. The RTK (Real Time Kinematic) data are utilized as inputs for the controller. The experiments are conducted in Lingshui Bay, Dalian, China. The results demonstrate that the lateral distance between the vessel's midship and the berth is less than 1.5 times the vessel's breadth, and the approaching angle is within the range of 0° to 10°, which shows that the proposed program could effectively help vessels achieve desirable berthing states.

Simulation and experimental study of gas-phase diffusion coefficient of selenium dioxide

Improving the removal effect of selenium in wet flue gas desulfurization system is a key way to reduce the emission of selenium pollutants from coal-fired power plants. In order to clarify the removal mechanism of selenium pollutants in the desulfurization tower, it is necessary to obtain accurate selenium gas-phase diffusion coefficient. In this paper, molecular dynamics simulations were used to carry out theoretical calculations of gas-phase diffusion coefficients of SeO2 (the main form of selenium in coal combustion flue gas). The gas-phase diffusion coefficients of SeO2 in the range of 393 K–433 K were measured by a self-developed heavy metal gas diffusion coefficient testing device to verify the accuracy of the molecular dynamics calculations. Furthermore, the calculated gas-phase diffusion coefficients of SeO2 under typical binary and ternary components were obtained by correcting on the basis of Fuller's formula. Finally, a single-droplet absorption model for SeO2 was constructed and experiments were carried out to compare the effect of the gas-phase diffusion coefficient on the accuracy of the model calculations. The error of the model calculations was reduced from 8.09 % to 1.96 % after the correction. In this study, the gas-phase diffusion coefficient of SeO2 in the low-temperature range of coal-fired flue gas was obtained. This study can provide basic data for the development of selenium migration mechanism and control technology.

Pandemic Action Plan: Phase 3-Lessons Learned from Implementation. "What Did We Learn?"

The COVID-19 pandemic created critical challenges for hospitals and health care providers. Suddenly clinics were forced to close; elective procedures were delayed; scheduled visits were canceled; emergency rooms were overcrowded; hospital beds, equipment, and personal protective equipment (PPE) were in short supply; and staff were faced with rapidly changing circumstances, care protocols, trauma, and personal risk. To better address challenges of the ongoing COVID-19 pandemic and prepare for future pandemics, the National Telemedicine Technology Assessment Resource Center (TTAC) was asked to develop a Pandemic Response Action Plan that would allow its users to address critical issues with available telemedicine and related technologies. The project was constructed in 3 phases. Phase 1-Develop a Pandemic Response Action Plan and a Pandemic Response Action Plan Policy and Regulatory Summary, which identifies the regulatory challenges as well as policy recommendations. Phase 2-Publish the Action Plan and the Policy and Regulatory Summary. Phase 3-Look at health care providers who used the approaches, tools, and technology in the Pandemic Action Plan and document the results. This document represents Phase 3. This document is Phase 3. In this report we look back at health care providers who used the approaches in the Phase 1 Pandemic Response Action Plan as published in Phase 2. In this document we report on the challenges and results of implementing parts of the Pandemic Action Plan. It records the findings, conclusions, and recommendations resulting from the experience of health care providers and the professional experiences of the team and their organizations in implementing parts or all of the plan. Methods: The same multidisciplinary team that constructed Phase 1 and Phase 2 were engaged to develop this Phase 3 report. The members of the team represent leadership expertise and key stakeholders in health care delivery during a pandemic (administration, infection control, physicians, nurses, public health, contingency planning, disaster response, and information technology) as well as a facilitator. For Phase 3, the group used structured brainstorming to define the findings, issues, and results of their own organizations' digital health response to the pandemic. In addition, eight health care providers (hospitals) identified by the Telemedicine Resource Centers' (TRCs) organizations, who used the Pandemic response Plan (created in Phases 1 and 2), were interviewed. All interviews were conducted by the same facilitator with leaders (CEO, and leaders of the telemedicine programs) in each of the eight programs, using a standard questionnaire created by the team. Current literature references are included in this report to illustrate when findings are known to have broader applicability. Conclusions: The impact of the COVID-19 Pandemic was severe and identified multiple critical challenges and weaknesses. Applying the approaches, tools, and technology outlined in the Pandemic Response Action Plan proved to be effective in addressing critical provider challenges. However, implementing these tools during a crisis was difficult unless the organization had experience with the tools and necessary workflows in advance. Implementing these tools as part of standard workflows and everyday operations increased the capabilities and resilience of these organizations in the provision of care during this and for future pandemics.

Emission characteristics and control technology of heavy metals during collaborative treatment of municipal solid waste incineration fly ash in iron ore sintering process

Emission characteristics and control technology of heavy metals during collaborative treatment of municipal solid waste incineration fly ash in iron ore sintering process

Prediction of the Fatty Acid Profiles of Iberian Pig Products by Near Infrared Spectroscopy: A Comparison Between Multiple Regression Tools and Artificial Neural Networks

AbstractIn this study, the feasibility of predicting the lipid profiles of Iberian ham and shoulder samples by using near infrared (NIR) spectroscopy was evaluated. Gas chromatography analysis was the reference method used. The muscles analyzed and recorded by NIR spectroscopy were 76 Biceps femoris for Iberian hams and 72 Brachiocephalicus for Iberian shoulders. NIR calibrations were carried out by using two methods: modified partial least squares regression (MPLS) and artificial neural networks (ANN). With the MPLS method, it was possible to obtain equations with regression’s coefficients (RSQ) of > 0.5 for 5 individual fatty acids and 3 summations: polyunsaturated fatty acids, n3 and n6. The use of neural networks made it possible to find equations with RSQ of > 0.5 for 10 individual fatty acids, all of which are present in over 90% of the samples, and 5 summations of saturated, monounsaturated, and polyunsaturated fatty acids (SFA, MUFA, PUFA), n3 and n6, finding that the calibration curves of the fatty acids C18:1, C18:2n6, and C18:3n3 presented RSQ’s of > 0.7. The results obtained indicate that NIR spectroscopy could be a very useful technology for the quality control of cured products as it allows estimating the main fatty constituents quickly and without using reagents.

Research on the influence and evaluation of manual operation performance of multifunctional operation panel considering interaction effect

AbstractManual operations remain crucial in critical human‐machine interactions due to limitations in automatic control algorithms and technologies. While ergonomic analysis and evaluation techniques for interactive interfaces are advancing, recent approaches emphasize integrating graphical interface experiences with intuitive controls. Conventional methods often lack precision or overlook the interaction effect of different influencing factors, leading to inadequate assessment of essential manual operations for intricate interfaces, such as multifunctional operation panels. To address these challenges, this study aimed to investigate the interaction effects of various factors on the manual operation performance of operators when using a multifunctional operation panel and aims to develop a more comprehensive and broadly applicable evaluation model for such panels. An experiment was designed to consider the type, size, layout of controls, and operational task type as the main factors affecting manual operation performance. Multivariate analysis of variance was used to identify significant interaction effects among the operation factors. The findings underscored the importance of these interactions in evaluating manual operation performance. Multivariate linear regression further examined the influence of these factors, enhancing the evaluation methodology. The study emphasizes the critical role of understanding interaction effects in assessing the manual operation performance of multifunctional operation panels, particularly in improving the design of the panel or operation tasks.

Study on Field Test of Deformation and Stability Control Technology for Shallow Unsymmetrical Loading Section of Super-Large-Span Tunnel Portal

This study focuses on monitoring the deformation of the shallow unsymmetrical section of a super-large-span tunnel portal relying on the newly built Shimentangshan Tunnel, and through numerical simulations, the construction sequence and drift ratios were optimized to address challenges related to the stability of surrounding rock and structure. The findings indicate that employing the double-side drift method results in a maximum settlement value of 107.0 mm and a maximum convergence value of 108.8 mm, leading to larger deformations. Excavating the shallow buried side first followed by the deep buried side proves beneficial for deformation control of the support structure and effectively limits damage to the surrounding rock. A drift ratio of 0.3 ensures optimal support structure security and stability. Considering both structural deformation and surrounding rock damage, a ratio between 0.25 and 0.35 for the drifts is recommended. Taking into account construction efficiency and economic benefits, a construction plan for the shallow buried unsymmetrical section at the portal of super-large-span tunnels is proposed.

Machine Learning & Conventional Approaches to Process Control & Optimization: Industrial Applications & Perspectives

Technologies based on Artificial Intelligence (AI) and Machine Learning (ML) concepts are advancing at a rapid pace. The new paradigms are challenging the status-quo of mature automation and control technologies in industry. Autonomous operation is a frequently stated goal of AI evangelists and technology providers. This white paper gives an overview of the current state of art of advanced process control and optimization technologies. It also provides a brief summary of the AI and ML-based approaches that address the closed-loop control and optimization space. Some results from industrial implementations are shared for both conventional and AI/ML-based approaches. Experience from four industrial applications is shared, covering rigorous model-based, machine learning and hybrid approaches to real-time optimization and control problems. The applications range from unit-based control & optimization to refinery & network wide optimization. A set of high-level requirements that need to be satisfied regardless of the underlying technology for closed-loop autonomous operations is reviewed. The article concludes with some future directions and perspectives highlighting areas where the emerging technologies may have significant impact in industry.

Non-thermal plasma technology for air pollution control and bacterial deactivation

Non-thermal plasma technology for air pollution control and bacterial deactivation

The development of precision postharvest technologies for citrus decay control

The development of precision postharvest technologies for citrus decay control