Intelligent Control Research Articles

Smart IoT SCADA System for Hybrid Power Monitoring in Remote Natural Gas Pipeline Control Stations

A pipeline network is the most efficient and rapid way to transmit natural gas from source to destination. The smooth operation of natural gas pipeline control stations depends on electrical equipment such as data loggers, control systems, surveillance, and communication devices. Besides having a reliable and consistent power source, such control stations must also have cost-effective and intelligent monitoring and control systems. Distributed processes are monitored and controlled using supervisory control and data acquisition (SCADA) technology. This paper presents an Internet of Things (IoT)-based, open-source SCADA architecture designed to monitor a Hybrid Power System (HPS) at a remote natural gas pipeline control station, addressing the limitations of existing proprietary and non-configurable SCADA architectures. The proposed system comprises voltage and current sensors acting as Field Instrumentation Devices for required data collection, an ESP32-WROOM-32E microcontroller that functions as the Remote Terminal Unit (RTU) for processing sensor data, a Blynk IoT-based cloud server functioning as the Master Terminal Unit (MTU) for historical data storage and human–machine interactions (HMI), and a GSM SIM800L module and a local WiFi router for data communication between the RTU and MTU. Considering the remote locations of such control stations and the potential lack of 3G, 4G, or Wi-Fi networks, two configurations that use the GSM SIM800L and a local Wi-Fi router are proposed for hardware integration. The proposed system exhibited a low power consumption of 3.9 W and incurred an overall cost of 40.1 CAD, making it an extremely cost-effective solution for remote natural gas pipeline control stations.

Development of Composite Intelligent Services Operating in a Heterogeneous Environment

The paper discusses methods, approaches, tools and technologies for composite service development. The expediency of applying the considered principles to create software application of so-called composite artificial intelligence (AI) is noted in order to implement the latter within the "distributed artificial intelligence as a service" (DAIaaS) paradigm. With the participation of the authors, the IACPaaS (Intelligent Applications, Control and Platform as a Service) cloud platform has been created and continues to be actively developed. It is intended for the creation, use and maintenance of multi-agent intelligent services that process ontological knowledge graphs. Currently, the platform provides such cloud service delivery models as PaaS (platform as a service), SaaS (software as a service), DaaS (data as a service) and Desktop as a Service. One of development directions is to provide support for the DAIaaS paradigm. It consists in the designing and implementation of tools that make it possible to use the platform and its services as components of composite applications (including AI ones) operating in a heterogeneous distributed computing environment. As a result, language and software tools were created that allow the development of distributed intelligent systems, some of the components of which are located on the IACPaaS platform, and the rest are located elsewhere. Depending on where the leading component is placed, certain tools and technologies implemented on the platform are used. The article also contains examples of created services for several domains (medicine, transport modeling, laser additive manufacturing) and options for using the platform.

DeepWFFS: Enhancing Fog Computing Efficiency Through Multiqueue Architecture and Intelligent Controller for Task Prioritization

DeepWFFS: Enhancing Fog Computing Efficiency Through Multiqueue Architecture and Intelligent Controller for Task Prioritization

Development and application of closed-loop-oriented intelligent control system for blast furnace air volume addition or reduction

Adjusting the upper and lower parts is an important means to ensure stable operation of blast furnaces. It is a common operation method to adjust the smelting state of the blast furnace by adjusting the air volume. Most of the domestic blast furnace air volume increase and decrease operations are confirmed by manual experience, and it is difficult to realize intelligent automatic control. This paper presents an intelligent control system for regulating air volume in blast furnaces, based on operational logic, expert knowledge, fundamental theories, and other relevant information. The system enables automatic analysis of the blast furnace's state and intelligent reasoning of the foreman's operational methods, laying the foundation for achieving closed-loop control. The system unifies the control philosophy and stabilizes the control methods for regulating air volume in blast furnaces. This achieves standardization of operation and proceduralization of cause analysis. The system enhances adaptability by creating an information feedback mechanism, increasing acceptance among blast furnace operators, and continuously resolving various anomalies in practical applications. The system currently maintains a true hit rate of over 95%, enabling effective reduction of operational misjudgments, intelligent control of airflow adjustment, and promotion of stable and smooth blast furnace operation.

Field Evaluation and Application of Intelligent Quality Control Systems

During road construction, the accuracy of compaction work is critical for the structural stability and maintenance of the road. Although the plate load test (PLT) is commonly used for quality inspections, it is impractical to test every section due to time and cost constraints. Therefore, simpler testing methods are being extensively developed. This study compared quality inspection results using the commonly used PLT, the relatively simple dynamic cone penetrometer test (DCPT), the lightweight deflectometer (LWD) test, and an intelligent quality control system equipped with accelerometer and global positioning system (GPS) sensors in intelligent compaction (IC) rollers. The results showed a strong correlation between the conventional tests (PLT, DCPT, and LWD) and the values obtained from the intelligent quality control system. The correlation analysis between the intelligent quality control system and PLT, LWDT, and DCPT yielded R-square values of 0.69, 0.91, and 0.95, respectively, indicating significantly high correlations. The implementation of intelligent quality management systems in earthwork construction projects will facilitate a thorough verification of the compaction quality throughout all construction segments, ensuring consistent compaction across the project. By enabling real-time data acquisition and analysis, these systems differ markedly from traditional methods, reducing the frequency and necessity of manual inspections. This approach not only streamlines construction processes, but also enhances operational efficiency. As a result, integrating these intelligent systems is anticipated to significantly increase productivity by optimizing the workflow and resource utilization in earthwork construction.

Intelligent underground garage drainage system based on informatized surveying technology

In recent years, with the impact of environmental factors such as global warming, floods and waterlogging disasters have occurred frequently in China. The traditional drainage devices in underground garages have poor water-blocking effects, which can easily result in sewage entering the garage along with vehicles during severe weather or sudden floods. To address the issue of water accumulation outside underground garages, which prevents vehicles from entering, we propose a set of drainage devices for underground garages to complement the shortcomings of existing market products. This device, controlled by an automated intelligent control system, can effectively control each device to achieve automatic water-blocking and drainage operations in underground garages, as well as car washing. During extremely heavy rainfalls, it can completely seal off the garage and enter flood prevention mode to protect vehicles inside from being submerged. Additionally, as the device is semi-underground, it can save a significant amount of above-ground space without affecting surface traffic or existing facilities, demonstrating practicality and having the potential for widespread adoption.

Bearing Faults Diagnosis by Current Envelope Analysis under Direct Torque Control Based on Neural Networks and Fuzzy Logic—A Comparative Study

Diagnosing bearing defects (BFs) in squirrel cage induction machines (SCIMs) is essential to ensure their proper functioning and avoid costly breakdowns. This paper presents an innovative approach that combines intelligent direct torque control (DTC) with the use of Hilbert transform (HT) to detect and classify these BFs. The intelligent DTC allows precise control of the electromagnetic torque of the asynchronous machine, thus providing a quick response to BFs. Using HT, stator current is analyzed to extract important features related to BFs. The HT provides the analytical signal of the current, thus facilitating the detection of anomalies associated with BFs. The approach presented incorporates an intelligent DTC that adapts to stator current variations and characteristics extracted via the HT. This intelligent control uses advanced algorithms such as neural networks (ANN-DTCs) and fuzzy logic (FL-DTCs). In this paper, a comparison between these two algorithms was performed in the MATLAB/Simulink environment for a three-phase asynchronous machine to evaluate their effectiveness under the proposed approach. The results obtained demonstrated a high ability to detect and classify BFs, confirming the effectiveness of each algorithm. In addition, this comparison highlighted the specific advantages and disadvantages of each approach. This information is valuable in choosing the most suitable algorithm according to the constraints and specific needs of the application.

Detection of Sybil & DDoS attacks in VANET using intelligent technique

ABSTRACT This paper proposes a novel fuzzy logic controller (FLC) based model referred to as FBM to detect both the DDoS and the Sybil attacks in VANET using an intelligent fuzzy logic controller. A VANET environment is created with different numbers of attackers to check the efficacy of the proposed model using veins, which is used for simulation with an event-based network simulator OMNeT++ and a road traffic simulator SUMO. Furthermore, the performance of the proposed model is compared with a few other machine learning techniques, such as logistic regression (LR), the decision tree (DT), random forest classifier (RF), and the support vector machine (SVM). The proposed FBM model yields better performance than the existing machine-learning techniques. The proposed FBM performs better than RF in accuracy by 0.10492%, precision by 0.08065%, recall by 0.02505%, and F1 score by 0.088243% for the DDoS scenario. It is also observed that the FBM performs better than DT in accuracy by 3.91486%, precision by 14.39994%, and F1 score by 3.819618% for the Sybil scenario. The margin of error for the proposed FBM is also estimated for 95% of the confidence interval and is found to be 0.021691 for the DDoS scenario.

Intelligent optimal control of furnace temperature for the municipal solid waste incineration process using multi-loop controller and particle swarm optimization

Furnace temperature (FT) is a key variable in municipal solid waste incineration (MSWI) processes, influenced by many manipulated variables and directly impacting pollutant concentrations in exhaust gas. Domain experts cannot achieve the optimal FT setpoint value under manual control, leading to abnormal pollutant emission concentrations. To address this, we propose an intelligent optimal control framework for FT aiming to minimize pollutant emission concentration. First, the FT controlled object model is established using the Tikhonov regularization-least regression decision tree (TR-LRDT) algorithm. Then, based on the experience of domain experts, a multi-loop controller is developed using an improved single neuron adaptive PID (ISNA-PID) algorithm to stabilize FT. Next, after establishing NOx and CO2 indicator models, the particle swarm optimization (PSO) algorithm is employed to determine the FT setpoint value in terms of minimum pollutant emission concentration. Finally, the FT intelligent optimal control framework is verified. Experimental results indicate that the optimal FT setpoint value can reduce NOx and CO2 emission concentrations by 19.93 % and 6.99 %, respectively.

Systematic Research and Optimization Design of Intelligent Electric Furnace Control System

Systematic Research and Optimization Design of Intelligent Electric Furnace Control System

An Intelligent Bait Delivery Control Method for Flight Vehicle Evasion Based on Reinforcement Learning

During aerial combat, when an aircraft is facing an infrared air-to-air missile strike, infrared baiting technology is an important means of penetration, and the strategy of effective delivery of infrared bait is critical. To address this issue, this study proposes an improved deep deterministic policy gradient (DDPG) algorithm-based intelligent bait-dropping control method. Firstly, by modeling the relative motion between aircraft, bait, and incoming missiles, the Markov decision process of aircraft-bait-missile infrared effect was constructed with visual distance and line of sight angle as states. Then, the DDPG algorithm was improved by means of pre-training and classification sampling. Significantly, the infrared bait-dropping decision network was trained through interaction with the environment and iterative learning, which led to the development of the bait-dropping strategy. Finally, the corresponding environment was transferred to the Nvidia Jetson TX2 embedded platform for comparative testing. The simulation results showed that the convergence speed of this method was 46.3% faster than the traditional DDPG algorithm. More importantly, it was able to generate an effective bait-throwing strategy, enabling the aircraft to successfully evade the attack of the incoming missile. The strategy instruction generation time is only about 2.5 ms, giving it the ability to make online decisions.

Organosilica with tunable pore structure and antibacterial activity for intelligent humidity control

The spontaneous sorption and release of moisture by sorbents is proposed to be an energy-saving strategy for air humidity control. However, rational design of sorbents with humidity control ability and wide applicability still faces challenges. This study demonstrates quaternary ammonium-functionalized organosilica capable of controlling humidity intelligently and tuning humidity range as required. The prepared organosilica exhibited step-shaped water sorption isotherm due to its highly ordered pore structure, which was capable of precisely maintaining humidity within a narrow range. Importantly, the step position could be broadly tuned by easily changing the pore size during synthesis. Series organosilica with different pore sizes could realize humidity maintenance in 20%-40%, 40%-60% and 55%-80%, respectively, making it suitable for diverse humidity control applications. Grand Canonical Monte Carlo simulations elucidated the underlying mechanism of different water sorption behaviors in different pore structures. Quaternary ammonium groups act as the initial nucleation sites for water vapor, followed by distinct water condensation mechanisms in micropores (pore filling) and mesopores (capillary condensation), respectively. Moreover, organosilica exhibits efficient antibacterial activity and self-cleaning capability due to the function of quaternary ammonium groups. The organosilica with tunable pore structure and antibacterial activity provides a new model for intelligent and low-energy humidity control.

Intelligent Control Strategy for Optimal Utilization of Energy in Smart Grid

Amidst the relentless surge in global energy demand, the optimal utilization of intelligent grid energy has emerged as a pivotal avenue for energy conservation, emission reduction, and sustainable development. The deployment of sophisticated control strategies within intelligent grids aims to elevate energy utilization rates and mitigate power losses. Drawing upon an extensive analysis of data, this study delves into the impact and significance of intelligent control strategies in the optimal utilization of smart grid energy. Our data analysis reveals that the implementation of intelligent control strategies enhances the energy utilization rate of smart grids by approximately 25%, significantly mitigating energy waste compared to traditional grids. Furthermore, power losses are effectively curbed and reduced by approximately 18%. This implies that, while fulfilling the same power demand, smart grids can reduce substantial energy losses and conserve vast resources for society. Furthermore, the intelligent control strategy boasts the capability to carry out real-time scheduling and control, tailored to the evolving energy demand and supply scenarios. This approach ensures the stability and reliability of power supply, subsequently elevating both the operational efficiency of the power system and the quality of power service delivered. Ultimately, the intelligent control strategy occupies a crucial position in optimizing the utilization of intelligent grid energy, serving as a catalyst for enhanced efficiency and sustainability within the global energy landscape. Our data analysis unequivocally demonstrates the remarkable impact achieved by sophisticated control strategies. Looking ahead, with the continuous advancement and refinement of technology, smart grids are poised to play an even more significant role in energy optimization, contributing meaningfully towards global energy conservation, emission reduction, and sustainable development.

(Invited) Nanoarray Configured Monoliths for Self-Monitored Multi-Phase Reactive Processes

The incorporation of multiple functions synergistically into one device or platform can enable additions of application merits such as light weight, ease for system integration, reduced system complexity, cost-effectiveness, and programmability. This is especially true for usually complex energy, environmental, and biological systems with the integration need of many active or passive devices and components of various functionalities. In this work, we demonstrated a compact nanoarray configured monolith design with the ability to function as both a catalyst and a sensor to enable the self-monitoring of the complex multi-phase catalytic reaction processes during operation. A bimodular approach has been realized using both electrical and electrochemical interrogation modes for reactive gas and solid phase monitoring directly using the built-in catalysts. This new type of compact and built-in monolithic sensor configuration can lower the catalytic energetics and detect gas/solid composition semi-quantitatively at room temperature up to elevated temperature. Robust calibration curves of built-in sensor responses can be achieved with facile data processing and analyzing. The built-in monolith sensor shows good sensitivity and good selectivity sufficient for the self-monitoring of the reaction process. Besides, the nanoarray based monolith device has shown good environmental tolerance toward practical applications. Such compact monolithic device can serve as a self-monitored reactor, filter, and other function component that can potentially allow in-situ and real time monitoring and functional process control in complex and reactive physical and chemical environments. With further refining of the design and functional integration, this new type of smart monolithic device can provide necessary data and knowledge for future computation-driven situational awareness prediction and intelligent control of various complex dynamic processes and systems.

AI-based dynamic avoidance in deep-sea mining

Dynamic obstacle avoidance is key to deep-sea mining vehicles. To enhance the dynamic avoidance capability in unpredictable seabed environments, this study employed an Improved Deep Deterministic Policy Gradient (IDDPG) algorithm. By developing intelligent controllers through extensive IDDPG optimization, this research achieves a significant breakthrough in enabling deep-sea mining vehicles to safely navigate around dynamically moving obstacles, demonstrating the algorithm's wide applicability and reliability. A notable achievement of this work is the strategy developed to balance safety and operational efficiency in obstacle avoidance contexts, underscoring the potential for future advancements in multi-vehicle operations and AI-based navigational systems. This foundation paves the way for enhancing deep-sea mining safety and operational efficiency, with supplementary materials provided for a comprehensive understanding of the methodology and the level of performance achieved in the results.

Optimization Design of Automatic Tea Ceremony Teacup Equipment Based on TRIZ Principle

With the development of science and technology and the accelerated pace of society, people are increasingly pursuing a convenient way of life. The art of making tea in traditional Chinese tea culture, although rich in deep cultural heritage, is difficult to adapt its cumbersome steps to the modern fast-paced life. In order to solve this contradiction, this paper applies TRIZ theory to optimize the design of fully automatic tea cup equipment, aiming to realize the automation of the tea brewing process, while retaining the cultural essence of traditional tea art. Through the automation design of the steps of blancher, tea brewing and aroma shaking, combined with modern mechanical design and intelligent control technology, this study proposes an innovative fully automatic tea ceremony teacup equipment program. The solution not only improves the efficiency of tea brewing, but also ensures the quality of tea soup and the experience of traditional tea ceremony through careful design. Ultimately, the feasibility and effectiveness of the proposed scheme are verified through kinematic analysis, prototype testing and user experience evaluation. This study is of great practical significance for the modernization and transformation of the traditional tea ceremony, and also provides a reference for the automation of other traditional processes.

Future Prospects of the Assembly Model for MEP Systems in Chinese Buildings: A Whole Life Cycle Perspective

The assembly building M&E (Monitoring and Evaluation) system is a vital part of the transformation of China’s construction industry, featuring intelligent control, high efficiency, and high safety. The article provides a comprehensive review of research related to assembly M&E systems from the perspective of the whole life cycle of assembly, containing 125 journal articles from 1993 to 2024. The article analyzes some policies with updated iterations in the United States, Japan, Germany, Denmark, France, and the European Union. The literature review and semi-structured interviews with experts identified significant constraints limiting the various stages of the entire life cycle of assembled MEP (mechanical, electrical, and plumbing) systems. The absence of uniform design standards, personnel collaboration, prefabricated component testing, transportation, information utilization, intelligent testing, and recycling of disassemblability that can occur in the entire life cycle of assembled MEP systems are summarized. Finally, the article suggests that assembly M&E systems can be shared and marketed to improve the economic viability of assembly M&E systems and their wide application in the areas of technology, platform, and demand.

Preparation of microencapsulated physicochemical composite retardant and study of the flame retardant mechanism

In order to solve the shortcomings of traditional inhibitors, which are easy to be interfered by the outside world during the inhibition process and the active ingredients are easy to be oxidized and invalidated, microencapsulated inhibitors were developed. In this study, we prepared microencapsulated inhibitors of varying wall-to-core ratios using a composite phase of polyethylene glycol 20,000 (PEG-20000) and octenyl amyl succinate (OSAS) as the wall material and a lauryl glucoside (APG-12) and butyl hydroxyanisole (BHA) phase as the core material. After thoroughly mixing the microcapsule inhibitor with coal samples, simultaneous thermal analyzer (TG/DSC) and a temperature-programmed system were used to evaluate the resisting qualities of the produced microencapsulated inhibitors. The results of the temperature programmed experiment showed that the overall CO release from the blocked coal samples was less than that of the original coal. According to thermogravimetric results, the most effective inhibition occurred when the wall/core ratio of the microcapsule inhibitor was 4:1. This also resulted in a reduction of the maximum coal weight loss rate from 1.34 to 0.96 %/min and an increase in the maximum coal weight loss temperature from 477.4 to 507.6 °C. The microencapsulated inhibitor melts in the wall at 57.4 °C, enabling temperature-sensitive intelligent control of core release. Following heating the raw and inhibited coal samples to 100 and 200 °C, respectively, Fourier infrared spectroscopy was conducted. At these temperatures, the amounts of ether bonds increased by 20.4 and 22.6 %, and the number of free hydroxyl groups decreased by 12.8 and 2.7 %, respectively, in the inhibited coal samples. Finally, molecular dynamics theory was applied to investigate the inhibitory mechanism. The prepared microcapsule retardant effectively inhibited the spontaneous combustion reaction of coal, suggesting this microcapsule inhibitor could be manufactured as a novel inhibitor for coal spontaneous combustion.

Zwitterionic-hydrogel-based sensing system enables real-time ROS monitoring for ultra-long hypothermic cell preservation

Hypothermic preservation (HP) is highly desired for the maintenance of the viability of living cell specimens, e.g. rare cells in whole-blood samples or therapeutic cells, in an unfrozen state. However, the extension of the viable preservation time is a challenge because of the multiple injuries suffered by hypothermically preserved cells. Here, based on a dynamic bond crosslinked zwitterionic hydrogel, we established a sensing preservation system that could monitor the levels of reactive oxygen species (ROS) via real-time electronic signals and intelligent control of antioxidant addition, to completely prevent an excess of ROS in the whole-cell specimen. Furthermore, the hydrogel-based system can counter the extracellular-matrix-loss-induced anoikis of living cells. Based on the design aimed at affording protection against two primary HP injuries (i.e. ROS overproduction and anoikis) to cells, this system extended the preservation time of cell specimens under refrigerated conditions to 24 days. After preservation, the use of a mild cell retrieval process guaranteed the activity of the preserved living cells. This work not only possesses the potential to facilitate intelligent cell-based clinical applications, but also paves the way for the preparation of living materials that can host programmed cells with long-term survival. Statement of significanceAn intelligent system based on a zwitterionic sensing hydrogel is established, which can afford ultra-long hypothermic cell-preservation times of up to 24 days. The system enables the real-time monitoring of ROS overproduction and intelligent antioxidant addition, because of the merging of the smart hydrogel with a computer intelligent detection and control system. Furthermore, the automatic addition of an antioxidant according to the ROS-signal changes produced by the ZBA hydrogel effectively prevented HP lesions, including ROS over-production and ECM loss, in the preserved living cells. Subsequently, the system could also be gently dissociated, to retrieve the preserved cells. This work provides a solution for the real-time monitoring and long-term HP of living specimens, which holds the promise of benefiting cell-based medicine and the development of genetically programmed cell-based living materials.

Intelligent Rice Field Weed Control in Precision Agriculture: From Weed Recognition to Variable Rate Spraying

Intelligent Rice Field Weed Control in Precision Agriculture: From Weed Recognition to Variable Rate Spraying