Real-time Control Technology Research Articles

A Review On Revolutionizing Waste Water Collection and Recycling Processes with IoT

This paper provides an extensive review of IoT-driven solutions addressing critical challenges in sectors like wastewater treatment, agriculture, waste management, e-waste recycling, and water quality monitoring. Innovative models leverage IoT technology for real-time monitoring, control, and optimization. Examples include an industrial IoT cloud-based model for wastewater treatment, sensor integration in aquaculture systems for improved resource management, and intelligent waste collection systems in smart cities. The review identifies gaps and opportunities for advancements, highlighting the importance of real-time data collection, cloud-based analytics, and machine learning for informed decision- making in environmental and resource management

Blend uniformity monitoring in a continuous manufacturing mixing process for a low-dosage formulation using a stream sampler and near infrared spectroscopy

Continuous manufacturing has the potential to offer several benefits for the production of oral solid dosage forms, including reduced costs, low-scale equipment, and the application of process analytical technology (PAT) for real-time process control. This study focuses on the implementation of a stream sampler to develop a near infrared (NIR) calibration model for blend uniformity monitoring in a continuous manufacturing mixing process. Feeding and mixing characterizations were performed for three loss-in-weight feeders and a commercial continuous mixer to prepare powder blends of 2.5–7.5 % w/w ibuprofen DC 85 W with a total throughput of 33 kg/h. The NIR spectral acquisition was performed after the mixing stage using a stream sampler for flowing powders. A continuous mixer shaft speed of 250 RPM was selected to operate the mixing process based on a variability analysis developed with in-line spectral data acquired using the stream sampler at 6 RPM. A partial least squares regression (PLS-R) model was performed and evaluated, yielding a root-mean-square error of prediction (RMSEP) of 0.39 % w/w and a bias of 0.05 % w/w. An independent experimental run conducted two days later revealed that the continuous mixing process and the NIR calibration model presented low day-to-day variation. The minimum practical error (MPE) and sill values through variographic analysis showed low variance associated with the sampling process using the stream sampler. Results demonstrated the promising capacity of the stream sampler coupled to an NIR probe to be implemented within continuous manufacturing processes for the real-time determination of API concentration.

Correction Control Model of L-Index Based on VSC-OPF and BLS Method

With the advancement of artificial intelligence (AI) technology, the real-time measurement and control technology of power systems has also progressed. This paper proposes a correction control model for L-indexes based on voltage stability constrained optimal power flow (VSC-OPF) and a broad learning system (BLS) (BLS-VSC-OPF). This model aims to quickly assess the system’s voltage stability and accurately correct the operation mode when the voltage stability indexes are out of the security range. Firstly, the BLS is used to predict the L-index and to analyze the voltage stability of the power system. Secondly, the approximate first-order sensitivity of the L-index is calculated by the combination of the BLS and the perturbation method. This method solves the problem of the complex sensitivity derivation process in the modeling process of the VSC-OPF model. Meanwhile, when the L-index exceeds the threshold, the BLS and VSC-OPF models are combined to correct this operation mode. The feasibility of the proposed method is verified by the simulation of IEEE-30, IEEE-118, and 1047 bus systems. Finally, the BLS-VSC-OPF model is compared with the linear programming correction model based on BLS (BLS-LPC). The results show that the BLS-VSC-OPF model provides a better correction and control performance.

Bus Drivers’ Behavioral Intention to Comply with Real-Time Control Instructions: An Empirical Study from China

Developing intelligent bus control systems is crucial for fostering the sustainability of urban transportation. Control instructions are produced in real time by the bus control system; these are important technical commands to stabilize the order in which buses operate and improve service reliability. Understanding the behavioral intention of bus drivers to comply with these instructions will help improve the effectiveness of intelligent bus control system implementation. We have developed a psychological model that incorporates decomposed variables of the theory of planned behavior (TPB) and other influencing variables to explain the micromechanisms that determine bus drivers’ behavioral intention to comply with real-time control instructions during both peak and off-peak-hour scenarios. A total of 258 responses were obtained and verified for analysis. The results showed that the influential factors in the peak- and off-peak-hour scenarios were not identical. Female drivers had greater off-peak-hour behavior intention to comply than male drivers, and there were significant differences in peak-hour behavior intention among drivers of different ages. In both peak and off-peak-hour scenarios, perceived benefit positively and perceived risk negatively affected behavioral intention. Perceived controllability positively affected behavioral intention only during peak hours. Self-efficacy only negatively affected behavioral intention during off-peak hours. Three antecedent variables (i.e., trust, mental workload, and line infrastructure support) influenced drivers’ behavioral intentions indirectly via the decomposed variables of TPB. These results provide profound insights for the improvement and implementation of real-time control technology for bus services, thereby facilitating the development of smart and sustainable urban public transport systems.

Multi-Mode Vector Light Field Generation Using Modified Off-Axis Interferometric Holography and Liquid Crystal Spatial Light Modulators

The increasing enhancement in the modulation accuracy of spatial light modulators has garnered significant attention towards real-time control technology for light fields based on these modulators. It has been demonstrated that this technology possesses a remarkable capability to generate vector beams with arbitrary complex amplitude distributions. Nevertheless, past studies indicate that the generation of only one vector beam at a time has been observed. The simultaneous generation of numerous vector light fields can give rise to several challenges, including compromised picture quality, limited single-mode operation, and intricate optical path configurations. In pursuit of this objective, we present a novel methodology that integrates the coding methodology of modified off-axis interferometric holography with the idea of optical superposition. This technique facilitates the concurrent generation of several vector beams. In this study, we present a demonstration of the simultaneous creation of twelve vector beams using a single spatial light modulator (SLM) as a proof of concept. Significantly, this technology has the ability to generate an unlimited quantity of vector light fields concurrently under the assumption that the resolution of the SLM does not impose any limitations. The findings indicate that the imaging quality achieved by this technology is of a high standard. Furthermore, it is possible to separately control the beam waist radius, topological charge, polarization order, and extra phase of each beam.

Drone-Based Decentralized Truck Platooning with UWB Sensing and Control

Truck platooning is a promising approach for reducing fuel consumption, improving road safety, and optimizing transport logistics. This paper presents a drone-based decentralized truck platooning system that leverages the advantages of Ultra-Wideband (UWB) technology for precise positioning, robust communication, and real-time control. Our approach integrates UWB sensors on both trucks and drones, creating a scalable and resilient platooning system that can handle dynamic traffic conditions and varying road environments. The decentralized nature of the proposed system allows for increased flexibility and adaptability compared to traditional centralized platooning approaches. The core platooning algorithm employs multi-objective optimization, taking into account fuel efficiency, travel time, and safety. We propose a strategy for the formation and management of platoons based on UWB sensor data with an emphasis on maintaining optimal inter-vehicle secure distances and compatibility between trucks. Simulation results demonstrate the effectiveness of our approach in achieving efficient and stable platooning while addressing the challenges posed by real-world traffic scenarios. The proposed drone-based decentralized platooning system with UWB technology paves the way for the next generation of intelligent transportation systems that are more efficient, safer, and environment friendly.

Assessing the benefits of real-time control to enhance rainwater harvesting at a building in Cape Town, South Africa

In the period 2015–2017, the City of Cape Town, South Africa, faced the possibility of taps running dry due to a prolonged drought. To mitigate the impacts of water scarcity, many households installed rainwater tanks to harvest water to use for non-potable purposes such as toilet flushing and washing. The installation of the rainwater tanks was mainly arbitrary, in response to perceived impact of water scarcity rather than a systematic needs assessment. This study was thus undertaken to determine the available opportunity to optimise the use of these rainwater tanks using real-time control (RTC) techniques. Many studies have demonstrated the potential of rainwater harvesting (RWH) systems to supplement potable water supply and minimize stormwater flows to downstream drainage networks. RTC technology can be used to enhance the performance of RWH systems in achieving these two objectives, by receiving a rainfall forecast and initiating pre-storm release in real time. In this study, RTC was applied on the RWH system at the New Engineering Building, University of Cape Town (UCT) to enhance water supply and increase rainwater retention period. The performance with RTC was compared with the conventional management of the RWH system. It was determined that RWH with RTC technology was generally superior in simultaneously achieving water supply and rainwater retention benefits compared to the conventional management approach. RTC provides an active operation which optimizes the performance of the system across varying conditions but requires an assiduous management process designed to meet set objectives. It was concluded that the active release mechanism employing RTC exhibited great potential; the system opens up the possibility of delivering a more robust and reliable system due to its ability to provide failure detection and centralised control. The system can readily be adapted to variation of local climatic conditions in the short and long term.

Research on dynamic perfusion control technology of 3D printed tissue vascular network

In order to solve the problem that the medium cannot be supplied accurately during the perfusion culture of the vascular network in large-volume 3D printing tissue, a perfusion control technology was studied by designing and building an in vitro perfusion and monitoring platform with feedback regulation. First, a large-volume vascular network of hydrogel material was fabricated by 3D bioprinting technology as the experimental object of perfusion. Then we designed and built the dynamic perfusion device and the monitoring equipment in the perfusion process independently and applied the real-time flow calibration control technology on this basis. Finally, the accuracy of the flow real-time calibration control technology was verified through the perfusion experiment of the large-volume vascular network. The results showed that the error rate between the actual flow rate and the preset flow rate increased gradually with the decrease of the preset flow rate of medium perfusion. When the preset flow is 3 mL/min, the error rate between them is as high as 19.33% without the application of flow calibration control technology. The dynamic perfusion device can reduce the average error rate between the two from 8.1% to 0.4% after applying real-time flow calibration control technology, which proves that the device can cultivate a large-volume vascular network with high-precision perfusion after the application of real-time flow calibration control technology.

Development of real-time crop recognition and control technology for weeding robot

Development of real-time crop recognition and control technology for weeding robot

Simulation Tools for Immersive Technology Services for People with Disabilities

The transfer of immersive technologies will soon be able to transform human life, allowing the implementation of H2M interactions to the fullest and with a new quality. People will be able to protect themselves from hazardous industries, learn faster, provide medical services to highly qualified specialists in the shortest possible time, and much more. The technology provides a paradigm shift towards wireless technology for real-time remote control. At the same time, the introduction of the Tactile Internet concept requires a revision of the principles for building existing and future communication networks. The article describes an experiment in the implementation of immersive technologies
 in fifth generation communication networks. In the article, a microservice application was created that simulates the movement of a human hand in space, which allows displaying sign language. Ways to reduce delays are also analyzed.

Real-Time Control Technology for a Bio-Liquor Circulation System in a Swine Barn with Slurry Pit: Pilot Scale Study.

The livestock industry, especially swine production, has been pressurized by vicinity complaints about odor in Korea. Therefore, a lot of effort has been undertaken regarding reducing the odor emissions from pigsties, widely carried out and the washing out manure in slurry pit by liquid-phase compost has particularly been spotlighted with outstanding performance of odor reduction. However, such a washing out manure called bio-liquor circulation system (BCS) has been controlled by a timer with designated reaction time, which cannot guarantee the system performance. This research proposes an effective real-time control technology for BCS, which circulates bio-liquor to the slurry pit of swine barns. The real-time control system was operated through accurate detection of the designated control points on the oxidation reduction potential (ORP) and pH time profiles for the nitrate knee point (NKP) and nitrogen break point (NBP) in anoxic and aerobic conditions with 100 and 99.6% performances, respectively. The duration of the anoxic and aerobic phases was also automated and noticeably lowered the concentration of nutrients in the manure in the slurry-pit, which served as a source of malodor. The real-time control strategy may be an innovative way to reduce odor and simultaneously produce liquid fertilizer, and provides a reference for the optimization of the industrial scale.

Real-time controlled rainwater harvesting systems can improve the performance of stormwater networks

Real-Time Control (RTC) technology is increasingly applied in Rainwater Harvesting (RWH) systems to optimise their performance related to water supply and flood mitigation. However, most studies to date have focussed on testing the benefits at an individual site scale, leaving the potential benefits for downstream stormwater networks largely untested. In this study, we developed a methodology to predict how at-source RTC RWH systems influence the behaviour of a stormwater network. Simulation was enabled by coupling the drainage model in SWMM with an RTC RWH model coded using the R software. We modelled two different RTC strategies across a range of system settings (e.g. storage size for RWH and proportion of storage to which RTC is applied) under two different climate scenarios—current and future climates. The simulations showed that RTC reduced flooding volume and peak flow of the stormwater network, leading to a potential mitigation of urban flooding risks, while also providing a decentralised supplementary water supply. Implementing RTC in more of RWH storages yielded greater benefits than simply increasing storage capacity, in both current and future climates. More importantly, the RTC systems are capable of more precisely managing the resultant flow regime in reducing the erosion and restoring the pre-development conditions in sensitive receiving waters. Our study suggests that RTC RWH storages distributed throughout a catchment can substantially improve the performance of existing drainage systems, potentially avoiding or deferring expensive network upgrades. Investments in real-time control technology would appear to be more promising than investments in detention volume alone.

Application of Neural Networks and Regression Modelling to Enable Environmental Regulatory Compliance and Energy Optimisation in a Sequencing Batch Reactor

Real-time control of wastewater treatment plants (WWTPs) can have significant environmental and cost advantages. However, its application to small and decentralised WWTPs, which typically have highly varying influent characteristics, remains limited to date due to cost, reliability and technical restrictions. In this study, a methodology was developed using numerical models that can improve sustainability, in real time, by enhancing wastewater treatment whilst also optimising operational and energy efficiency. The methodology leverages neural network and regression modelling to determine a suitable soft sensor for the prediction of ammonium-nitrogen trends. This study is based on a case-study decentralised WWTP employing sequencing batch reactor (SBR) treatment and uses pH and oxidation-reduction potential sensors as proxies for ammonium-nitrogen sensors. In the proposed method, data were pre-processed into 15 input variables and analysed using multi-layer neural network (MLNN) and regression models, creating 176 soft sensors. Each soft sensor was then analysed and ranked to determine the most suitable soft sensor for the WWTP. It was determined that the most suitable soft sensor for this WWTP would achieve a 67% cycle-time saving and 51% electricity saving for each treatment cycle while meeting the criteria set for ammonium discharges. This proposed soft sensor selection methodology can be applied, in full or in part, to existing or new WWTPs, potentially increasing the adoption of real-time control technologies, thus enhancing their overall effluent quality and energy performance.

A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation

Water distribution system monitoring is currently carried out using advanced real-time control technologies to achieve a higher operational efficiency. Data analysis techniques can be implemented for condition estimation, which are crucial tools for managing, developing, and operating water networks using the monitored flow rate and pressure data at some network pipes and nodes. This work proposes a state estimation methodology that enables one to infer the hydraulic state of the operating speed of pumping systems from these pressure and flow measurements. The presented approach suggests using graph convolutional neural network theory linked to hydraulic models for generating a digital twin of the water system. It is validated on two benchmark hydraulic networks: the Patios-Villa del Rosario, Colombia, and the C-Town networks. The results show that the proposed model effectively predicts the state estimation in the two hydraulic networks used. The results of the evaluation metrics indicate low values of mean squared error and mean absolute error and high values of the coefficient of determination, reflecting high predictive ability and that the prediction results adequately represent the real data.

State-of-Art Review of Traffic Light Synchronization for Intelligent Vehicles: Current Status, Challenges, and Emerging Trends

The effective control and management of traffic at intersections is a challenging issue in the transportation system. Various traffic signal management systems have been developed to improve the real-time traffic flow at junctions, but none of them have resulted in a smooth and continuous traffic flow for dealing with congestion at road intersections. Notwithstanding, the procedure of synchronizing traffic signals at nearby intersections is complicated due to numerous borders. In traditional systems, the direction of movement of vehicles, the variation in automobile traffic over time, accidents, the passing of emergency vehicles, and pedestrian crossings are not considered. Therefore, synchronizing the signals over the specific route cannot be addressed. This article explores the key role of real-time traffic signal control (TSC) technology in managing congestion at road junctions within smart cities. In addition, this article provides an insightful discussion on several traffic light synchronization research papers to highlight the practicability of networking of traffic signals of an area. It examines the benefits of synchronizing the traffic signals on various busy routes for the smooth flow of traffic at intersections.

Smart wall by wireless sensor network toward building energy optimization

Buildings design and operation are responsible for occupant comfort. Buildings facades and walls can be engineered to control solar energy for photovoltaic electricity generation, daylighting, heating, ventilation, thermal insulation, and energy storage. Adaptive facades and intelligent walls integrate real-time control technologies to adapt to the occupant’s requirements and preferences. Data transmission and information control in the modern building are gaining increasing importance. Implementing wireless network systems based on wireless communication technologies and protocols with low energy demand is crucial. This paper presents the simulation of wireless networks based on Bluetooth Low Energy (BLE) transmission nodes with an energy harvesting system solution. The results showed the performance of the wireless sensor network model toward the solar energy harvesting solution.

A new idea for developing closed-loop feedback electrical acupoint stimulation devices based on classic acupuncture theory

As one of the most widely used traditional Chinese medicine (TCM) treatment apparatus, the electric acu-point stimulation (such as electroacupuncture, transcutaneous acupoint electric stimulation, etc.) device plays an important role in improving the efficacy of acupuncture therapy and expanding its indications. The imbalance of human functional state has become an important factor affecting the health condition and quality of life. Acupuncture therapy has the advantage of regulating functional status of patients to restore the balance. Aiming at some problems of current acupuncture stimulation instruments in the stimulated depth, fuzzy range, lack of closed-loop feedback, insufficient combination with the theory of Chinese acupuncture-moxibustion, etc., we, in the present paper, put forward a new way of thinking that the classical theory of "Deqi" of acupuncture should be combined with the modern bio-electronic technology, multimode signal acquisition technology, real-time feedback control techno-logy, etc., in order to develop a series of effective, safe and comfortable integrated closed-loop feedback acupoint electrical stimulation devices. Interactions of multi-disciplines, reasonable coalesce between the medical treatment and theory, medical treatment and modern bioengineering techniques, as well as the integration of Chinese traditional and modern medicine will help promote the development of apparatuses of acupuncture-moxibustion for diagnosis and treatment of diseases. Such interdisciplinary efforts will make more contributions to the construction of healthy China and the health of all mankind.

Coarse Return Prediction in a Cement Industry’s Closed Grinding Circuit System through a Fully Connected Deep Neural Network (FCDNN) Model

Milling operations in various production processes are among the most important factors in determining the quality, stability, and consumption of energy. Optimizing and stabilizing the milling process is a non-linear multivariable control problem. In specific processes that deal with natural materials (e.g., cement, pulp and paper, beverage brewery and water/wastewater treatment industries). A novel data-driven approach utilizing real-time monitoring control technology is proposed for the purpose of optimizing the grinding of cement processing. A combined event modeling for feature extraction and the fully connected deep neural network model to predict the coarseness of cement particles is proposed. The resulting prediction allows a look ahead control strategy and corrective actions. The proposed solution has been deployed in a number of cement plants around the world. The resultant control strategy has enabled the operators to take corrective actions before the coarse return increases, both in autonomous and manual mode. The impact of the solution has improved efficiency resource use by 10% of resources, the plant stability, and the overall energy efficiency of the plant.

Real-time precise control technology of multidimensional and stereoscopic high-speed photogrammetry system

在飞行试验过程中，高速摄影测量站点呈现数量多、分布散且人机分离的特点，为了同时获取起飞着陆段2个分离区域的多目标实时测量数据，基于组播概念提出了一种分布式高速摄影网络系统，设计了一种高效的无关联信号高速像机触发判据算法，通过实时获取多种机载以及动平台数据，通过图像处理的方法完成多维立体化高速摄影测量系统的实时精准智能控制与海量数据的实时卸载，实现8台不同类型高速像机的测试影像智能获取，测量系统误触发率降低至0.76%，有效提高了工作效率，保证了飞行试验数据的准确性和高效性。

Combined Application of Real-Time Control and Green Technologies to Urban Drainage Systems

The increase in waterproof surfaces, a typical phenomenon of urbanization, on the one hand, reduces the volume of rainwater that naturally infiltrates the subsoil and, on the other, it determines the increase in speeds, flow rates, and outflow volume surface; at the same time, it causes a qualitative deterioration of the water. This study researched the optimal management of urban drainage systems via the combined application of real-time control and green technologies. A hydraulic model of the sewer system of the suburbs of Bologna (Italy) was set up using the Environmental Protection Agency (EPA) Storm Water Management Model (SWMM) to evaluate the reduction in water volume and the masses of pollutants discharged in water bodies. The combined application of these technologies allows significantly reducing both the pollutants released into the receiving water bodies and the overflow volumes, while optimizing the operation of the treatment plants. Green technologies cause an average reduction equal to 45% in volume and 53% of total suspended solids (TSS) sent to the receiver. The modeled cases represent only some of the possible configurations achievable on urban drainage systems; the combined use of different solutions could lead to further improvements in the overall functioning of the drainage system.