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The paper describes a smart model for palletising as main intra-logistics task for product-on-pallet distribution, and its development in Logistics 4.0 framework aligned to Industry 4.0. The model is developed in holonic paradigm as a 2-layer Holonic Logistics Execution System (HLES) in semiheterarchical topology. Scheduling of logistics activities and allocation to resources are optimised for global efficiency on compact time periods. The logistics model virtualises physical entities: resources, pallets and orders as holons implemented with digital twin software, and categories of task workloads: resource health monitoring, dispatching and tracking pallet orders, re-assigning jobs at resource failure, which grants reality-awareness and robustness. Global workload optimisation uses Constraint Programming as decision making technology with ILOG optimiser engine as situation-specific solver tool in SaaS cloud model, and delegate multi-agent system (MAS) technology for intelligence distribution. The optimised objective function is a combination of palletising cost weighted by robot speed limits and pallet storage cost in payable stocks. The main constructs are exemplified and validated on a real-life structure with multiple palletising resources in which programmable logic controllers (PLC) coordinate locally the parallel execution of order holons according to their globally optimised sequence. [ABSTRACT FROM AUTHOR]

With the escalating threat posed by network intrusions, the development of efficient intrusion detection systems (IDSs) has become imperative. This study focuses on improving detection performance in programmable logic controller (PLC) network security while addressing challenges related to data imbalance and long-tail distributions. A dataset containing five types of attacks targeting programmable logic controllers (PLCs) in industrial control systems (ICS) was first constructed. To address class imbalance and challenges posed by complex network traffic, Synthetic Minority Oversampling Technique (SMOTE) and Borderline-SMOTE were applied to oversample minority classes, thereby enhancing their diversity. This paper proposes a dual-channel feature extraction model that integrates a multi-scale one-dimensional convolutional neural network (MS1DCNN) and a Weight-Dropped Transformer (WDTransformer) for IDS. The MS1DCNN is designed to extract fine-grained temporal features from packet-level data, whereas the WDTransformer leverages self-attention mechanisms to capture long-range dependencies and incorporates regularization techniques to mitigate overfitting. To further enhance performance on long-tail distributions, a custom combined loss function was developed by integrating cross-entropy loss and focal loss to reduce misclassification in minority classes. Experimental validation on the constructed dataset demonstrated that the proposed model achieved an accuracy of 95.11% and an F1 score of 95.12%, significantly outperforming traditional machine learning and deep learning models. [ABSTRACT FROM AUTHOR]

Simple Summary: In pig farming, maintaining an optimal temperature for nursing piglets is critical for their health and growth. Traditional localized heating methods, such as heating lamps, often lack precise temperature control, leading to energy waste and suboptimal conditions for piglets as they grow. This study developed a Programmable Logic Controller-based intelligent thermal insulation box that uses a strip heater to automatically adjust its temperature based on the piglets' age and environmental conditions. We tested the system's effectiveness by comparing it to traditional heating in terms of temperature stability, energy consumption, and piglet growth. The new system significantly improved the temperature stability, reduced energy consumption by over 50%, and enhanced piglet growth. This intelligent thermal insulation box could provide an effective, energy-saving solution for improving piglet welfare and farming efficiency. Local heating of the activity area for nursing piglets is crucial for piglet health and the energy efficiency of barn climate control. Traditional heating methods using lamps or covers lack precise control, result in significant energy waste, and cannot be dynamically adjusted according to piglet age or changing environmental temperatures. To address these issues, this study designed a Programmable Logic Controller (PLC)-based thermal insulation box for nursing piglets, utilizing a strip heater instead of the conventional round heating lamp. The design incorporates a movable thermal insulation box that dynamically adjusts the heater's power based on the real-time monitoring of environmental temperatures and target temperatures specific to piglet age. First, in a controlled laboratory environment, the study tested and compared the spatial temperature uniformity, temporal stability, and power consumption of the new thermal insulation box versus traditional heating methods. Subsequently, animal trials were conducted in a farrowing barn using eight sows with similar farrowing dates as test subjects. The new thermal insulation box was installed in one group, and the traditional heating lamp in the control group. During the trial, ambient temperature, insulation area temperature, piglet behavior, growth performance, and power consumption were recorded. The results showed that compared to the control group, the new system reduced average temperature fluctuations in the insulation area by 31.6% and spatial temperature variation by 78.3%. During animal trials, the average temperatures directly under the heater for the new system versus the control in the insulation area were 39.7 ± 0.2 °C and 30.2 ± 1.4 °C in the first week, 40.9 ± 0.5 °C and 31.6 ± 0.7 °C in the second week, and 32.3 ± 1.5 °C and 28.6 ± 1.7 °C in the third week—significantly (p < 0.05) higher in the test group. The new system also reduced total energy consumption by 58.3%. The usage rate of the thermal insulation area by piglets in the test and control groups was 47.5 ± 5.3% and 42.1 ± 6.6%. The daily weight gain of piglets in the test group was 9.8% higher than that of the control group, also significantly (p < 0.05) higher. This intelligent thermal insulation box enables precise and dynamic temperature control, reducing heating energy consumption and supporting improved piglet health and welfare. [ABSTRACT FROM AUTHOR]

The rapid development of the industrial Internet of Things (IIoT) has transformed manufacturing processes, particularly in industries such as electronics and aerospace. While larger enterprises have successfully implemented IIoT applications to enhance productivity and facilitate smart manufacturing, small and medium-sized enterprises have faced challenges in adopting these technologies owing to financial constraints and limitations in their existing equipment. Therefore, in this paper, we propose to use Raspberry Pi as the hardware basis and the Cheng Shiu University laboratory as the research target. The approach presented in this paper involves integrating open-source packages to design both the programmable logic controller (PLC) publishing pattern and the PLC subscribing pattern. The proposed approach uses a message queuing telemetry transport (MQTT) communication protocol for transmitting Modbus transmission control protocol (TCP) register data and bidirectional data exchange with a remote monitoring system. An MQTT Broker is established to act as a bridge between the monitoring platform and the control system. A Node-RED-based IoT platform is set up to subscribe to topics from the PLC publisher for data collection. Additionally, the human–machine interface (HMI) in the monitoring system utilizes MQTT communication to publish PLC control commands, and a database is implemented for the historical analysis of monitoring data. In this study, we aim to establish a web-based cross-platform monitoring and control system using lowcost integration through Raspberry Pi 4B embedded systems and various communication protocols. The design facilitates the IoT transformation of PLC control systems. This integrated system has broad applications in construction, production equipment, and power system monitoring to achieve power monitoring and energy conservation. [ABSTRACT FROM AUTHOR]

This study designed and validated a power control algorithm tailored to the unique characteristics of a 100 kW wind turbine equipped with an independent generator torque control system within the Power Conversion System (PCS). Unlike conventional power control methods based on the Programmable Logic Controller (PLC), the independent generator torque control in the PCS estimates the generator speed based on the voltage signal and performs the generator torque control without any interference from the PLC. The conventional power control algorithm in the PLC was modified so that the PLC could perform the power control by the blade pitch control only. Furthermore, a mode transition algorithm was designed to improve the shortcomings of the mode switch previously used for transitioning between the torque and pitch control modes. To verify the performance, a simulation environment similar to actual control conditions was established using a commercial analysis program, and dynamic simulations were conducted. Additionally, for the experimental validation, the proposed control algorithm was applied to a scaled wind turbine, and wind tunnel tests were performed. The results of the simulations and wind tunnel tests confirmed the operational performance of the PCS's torque control and the improved control transition logic. The proposed control algorithm could be specifically applied to medium-sized wind turbines employing a sensorless generator speed estimation method and independent generator torque control in the PCS. [ABSTRACT FROM AUTHOR]

The voltage control of synchronous generators, particularly under varying load conditions, remains a significant and complex challenge in the field of engineering. Although various control methods have been implemented for automatic voltage regulator (AVR) systems to control the terminal voltage of synchronous generators, the PID-based control method continues to be one of the most basic and widely used approaches. Determining the optimal values for the Kp, Ki, and Kd values is essential to ensuring efficient and rapid performance in a PID controller. This study presents PLC-based PID controller tuning using an adaptive artificial bee colony–fuzzy logic (aABC-FL) approach for voltage control in a micro-hydro power plant installed as an experimental setup. The real-time control and monitoring of the system was conducted using an S7-1200 programmable logic controller (PLC) integrated with a totally integrated automation (TIA) portal interface and a SCADA screen. The aABC-Fuzzy design was developed using the MATLAB/Simulink platform, with PLC-MATLAB communication established through OPC UA and the KEPServerEX interface. The results obtained from experiments conducted under different load conditions showed that the proposed aABC-FL PID significantly minimized settling time and overshoot compared to the classical PLC-PID. Additionally, the proposed method not only provided a good dynamic response but also proved to be robust and reliable for real physical AVR systems. [ABSTRACT FROM AUTHOR]

Real‐time acquisition of microgrid (MG) operation data and remote control play a crucial role in the safe and stable operation of MG. A design scheme of monitoring system is proposed for the wind/photovoltaic/energy storage islanded direct current MG. The core controllers used in direct current MG system are programmable logic controller and digital signal processor. The monitoring system mainly adopts Ethernet communication, together with serial port communication mode Recommend Standard 232 (RS232) and Recommended Standard 485 (RS485) communication modes. A heterogeneous networking is built with controllers and data display instruments. Different monitoring interfaces are designed, the important information can be real‐time detected, collected and displayed. The multi‐energy and multi‐load dispatching operation control are realized according to scheduling operation plan. The parameters of the inverter can be adjusted, and the output voltage waveform and sinusoidal pulse width modulation waveform of the inverter can be measured. The communication of the proposed monitoring system is reliable, flexible and expandable, and easy to realize remote operation control. Moreover, the detection and control functions can be extended further. Through experiments, the effective control and monitoring of the designed monitoring system is verified, and the intelligent management is realized. [ABSTRACT FROM AUTHOR]

In recent decades, standardisation has become crucial in Industrial Automation Systems, promoting interoperability among devices from different manufacturers. Nonetheless, the portability of manufacturing engineering tools continues to present a significant challenge. AutomationML (IEC 62714 -5) has played a role in formulating strategies that leverage O-PAS Standard Function Blocks. The O-PAS specification includes several standards, such as IEC 62443, IEC 61499, IEC 61131–3, OPC-UA (Open platform communications unified architecture), DCS (Distributed control system), and PLC (Programmable logic controller). This highlights the necessity of integrating new technologies with legacy systems to achieve enhanced interoperability. In this context, this work proposes the development of User-Defined Function Blocks (UDFB) that adhere to O-PAS standards and can be implemented across any compliant system. The proposed methodology is aligned with O-PAS standards, leading to the inclusion of UDFB information in XML format within the Function Block Library and AutomationML. [ABSTRACT FROM AUTHOR]

Ports worldwide seek for more efficient and sustainable operations, relying on critical and valuable assets as their cranes. Internet of Things (IoT) technologies enable a better understanding of critical assets' behaviour and lifecycle based on data, and development of asset management decision support tools. The Asset Health Index (AHI) is an asset condition score, designed to characterise the state of complex assets. This paper describes the process of developing a data-driven AHI. IoT sensors and Programmable Logic Controllers (PLC) in real operation time, together with information systems, and other more traditional sources of data are some of the data sources for the AHI. The index captures and quantifies dimensions affecting the asset lifecycle, allowing the comparison of the condition of assets and prioritise maintenance investments. The case study is conducted in Quay Cranes of the Port of Felixstowe (UK) as the object of this study and proof of concept. [ABSTRACT FROM AUTHOR]

In addressing the mounting challenges of industrial supervision systems grappling with intricate processes, this study pioneers a transformative paradigm centered on the SCIMAT cement factory. By seamlessly integrating Machine Learning and Fuzzy Logic, the primary aim is to revolutionize real-time control systems, with a keen focus on cement production. SVM integration into the supervision system, coupled with connectivity to a Programmable Logic Controller (PLC), is complemented by fuzzy real-time controllers' regression analysis. Rigorous testing and evaluation validate the proposed approach's reliability, showcasing its effectiveness in discerning optimal system functioning. The system's practical application within a PLC environment underscores its prowess in issuing commands to industrial equipment, thereby enhancing operational efficiency. Going beyond conventional methodologies, our approach amalgamates SVM classification, fuzzy controllers, and real-time regression analysis, delivering a multifaceted solution for industrial supervision. The system's standout achievement is an SVM classification accuracy surpassing 94% compared to other classifiers. The K-Nearest Neighbors (K-NN) model demonstrated an accuracy rate of approximately 93.83%. The decision tree model attained an accuracy of around 83.73%. The logistic regression model achieved an accuracy of 80.25%. These models are not only adept at distinguishing optimal functioning from faults but also adept at preserving the linguistic language used by operators. The study's novelty lies in the holistic integration of SVM and Fuzzy Logic, offering a practical and adaptable solution that not only advances classification accuracy but also significantly reduces maintenance costs, marking a substantial improvement over the traditional methods. This transformative model, validated through SVM classification and practical application, establishes a new standard for flexibility, cost reduction, and overall productivity enhancement in industrial processes. [ABSTRACT FROM AUTHOR]

Fertigation practices in soilless crop cultivation often rely on predetermined recipes, which may lead to suboptimal nutrient concentrations due to inherent human error or environmental fluctuations. To address this challenge, the integration of in situ real-time nutrient analyzers becomes imperative for ensuring the delivery of high-quality supply solutions. This study assesses the effectiveness of a real-time nitrate (NO3−) analyzer in optimizing the mineral composition of the nutrient solution for fertigating a decoupled aquaponic cucumber crop. The analyzer was integrated into the programmable logic controller of the greenhouse's hydroponic system. The NO3− analyzer was activated during solution preparation, dynamically adjusting the NO3− concentration based on real-time measurements from either the aquaculture or drainage solution by adding the necessary water or/and nutrients in order to prepare a solution to meet the needs of the crop. Four treatments were evaluated: hydroponics (HP), coupled aquaponics (CAP), decoupled aquaponics (DCAP) with EC adjustment, and decoupled aquaponics with NO3− adjustment (DCAP_N). Results indicated that the DCAP_N treatment, with NO3− adjustment, yielded the highest crop productivity, outperforming DCAP, HP, and CAP treatments by 7.4%, 21.2%, and 56.1%, respectively. Additionally, DCAP_N demonstrated superior water use efficiency (WUE) and fertilizer use efficiency (FUE), exhibiting a 21.5% and 52.5% increase over the HP treatment, respectively. These findings align with the European Green Deal's objectives by enhancing nutrient management practices, which are crucial for minimizing nutrient loss and ensuring the sustainable and efficient use of fertilizers. [ABSTRACT FROM AUTHOR]

In this paper, an improved 100 CMM regenerative thermal oxidizer (RTO) is implemented for low-emission combustion. The existing RTO system is a cylindrical drum structure that cyclically introduces and discharges VOC gas into and from the rotating disk, and which achieves excellent energy efficiency with a heat recovery rate of more than 95%. However, the drive shaft designed under the RTO combustion chamber increases wear around the rotating shaft due to the load of the combustion chamber and there is a problem that the untreated gas is simultaneously released through the outlet due to the channeling phenomenon of the combustion chamber and the drive shaft. In addition, the combustion chamber, used at a high temperature of 800 °C, may cause serious problems such as rotation stop or explosion due to pollutants, dust accumulation, and thermal expansion in the chamber. Particularly when treating VOCs harmful gasses, RTO performance may be degraded due to the burner's non-uniform temperature control and unstable combustion function. To solve this problem, first, the design of the combustion chamber rotating plate driving device is improved. Second, when treating high concentration VOC gas, the design of combustion chamber considers a temperature increase of up to 920 °C or more. For this, the diameter of the gas burner is 125 mm and the outlet dimension is set to 650 mm × 650 mm to effectively discharge high-temperature waste heat. Third, the heat storage material in the combustion chamber is composed of a ceramic block with a thickness of 250 mm, and the outer diameter and height of the combustion chamber are set to, 2530 mm and 1875 mm, respectively, to optimize gas residence time and heat insulation thickness. Fourth, we supplement safe operation by applying the trip control algorithm of the programmable logic controller (PLC) panel for failure prediction of RTO and the Edge-IoT-based intelligent algorithm for this. Finally, we evaluate the economic performance of 100 CMM RTO by conducting empirical experiments to analyze changes in VOCs removal efficiency, nitrogen oxide emission concentration, and total hydrocarbon (THC) concentration through 10 CMM design and implementation. [ABSTRACT FROM AUTHOR]

This research paper presents a novel attempt of speed control for brushless direct current (BLDC) motor in low power/servo motor applications. The performance is measured based on the swiftness for the recovery of desired speed amidst in disturbances, sensitive to supply/motor load fluctuations. The proportional integral (PI) controller is competent only for linear time invariant systems. The state of art technology is, PI controller is used with metaheuristic optimization algorithms viz. Honeybee mating optimization (HBO), artificial immune system (AIS), and frog leaping guided algorithm (FLG), for fine tuning of gain coefficients. Earlier literature survey shows power quality and time domain specifications for separate applications. An innovative approach for the assessment of performance indicators like maximum overshoot, settling time, power factor (PF) and total harmonic distortion (THD) simultaneously in the optimized PI controller is suggested. By avoiding local optima trapping, this method gives better dynamic performance for various test conditions. MATLAB/Simulink 2021a software is utilized in the examination of performance in various load and speed scenarios, subsequently validated with hardware where cost effective Arduino controller replaced programmable interface controllers (PIC) microcontroller. [ABSTRACT FROM AUTHOR]

This paper presents the design, implementation, and testing of an advanced conveyor belt surface monitoring system, specifically engineered for harsh and complex industrial environments. The system integrates multiple cutting-edge technologies, including programmable logic controllers (PLC), laser scanning, industrial-grade cameras, and deep learning algorithms, particularly YOLOv7, to achieve real-time, high-precision monitoring of conveyor belt conditions. Key innovations include optimized detection location based on failure modes, advanced PLC integration for seamless automation, and intelligent dust-proof features to maintain accuracy in challenging conditions. Through strategic placement of detection devices and multi-mode control strategies (local, remote, and automatic), the system offers unparalleled adaptability and responsiveness. The system leverages robust data management for trend analysis and predictive maintenance, enhancing operational efficiency. The hardware architecture comprises PLC-based control systems, high-resolution industrial cameras, and laser emitters, while the software features a two-tier structure combining human-machine interaction (HMI) with real-time data processing capabilities. Experimental results show that the system is highly effective in detecting common belt defects such as foreign objects, tears, and shallow scratches, ensuring optimal operational efficiency and minimizing downtime. The system's scalability, robust data management, and adaptability to low-light and dusty conditions make it ideal for deployment in large-scale industrial operations, where continuous monitoring and early fault detection are critical to maintaining productivity and safety. [ABSTRACT FROM AUTHOR]

The paper is focused on the modeling of a digital twin (DT) through a circuit simulation and artificial intelligence (AI) analysis to determine the effects of disturbances and noise in optocoupler devices integrated into programmable logic controller (PLC) systems. Specifically, the DT analyzes the parametric and the predicted simulations about the sensitivity of the optocouplers versus noise and interference to provide possible corrective actions, compensating for the distortion of the output signal. The model is structured into two main data processing steps: the first is based on the circuit simulation of the optocoupler noise coupling by highlighting the time-domain sensitivity aspects and the frequency behavior of the coupled signals; the second one estimates the predicted disturbed signal by means of supervised random forest (RF) and unsupervised K-Means algorithms to provide further elements to prevent corrective solutions by means of risk maps. This work is suitable for Industry 5.0 scenarios involving machine control supported by AI-based DT platforms. The innovative elements of the proposed model are the DT features of scalability and modularity; the spatial multidimensionality, able to couple the effects of different undesired signals; and the possibility to simulate the whole PLC system, including its control circuits. [ABSTRACT FROM AUTHOR]

As the market share of electric vehicles continues to rise, consumer demands for comfort within the vehicle interior have also increased. The noise generated by electric seats during operation has become one of the primary sources of in-cabin noise. However, the offline detection methods for electric seat noise severely limit production capacity. To address this issue, this paper presents an online quality inspection system for automotive electric seats, developed using LabVIEW. This system is capable of simultaneously detecting both the noise and electrical functions of electric seats, thereby resolving problems associated with multiple detection processes and low integration levels that affect production efficiency on the assembly line. The system employs NI boards (9250 + 9182) to collect noise data, while communication between LabVIEW and the Programmable Logic Controller (PLC) allows for programmed control of the seat motor to gather motor current. Additionally, a supervisory computer was developed to process the collected data, which includes generating frequency and time-domain graphs, conducting data analysis and evaluation, and performing database queries. By being co-located with the production line, the system features a highly integrated hardware and software design that facilitates the online synchronous detection of noise performance and electrical functions in automotive electric seats, effectively streamlining the detection process and enhancing overall integration. Practical verification results indicate that the system improves the production line cycle time by 34.84%, enabling rapid and accurate identification of non-conforming items in the seat motor, with a detection time of less than 86 s, thereby meeting the quality inspection needs for automotive electric seats. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) is a paradigm that enables the integration of cyber-physical systems in critical infrastructures, such as power grids, water distribution networks, and transportation systems. IIoT devices, such as sensors, actuators, and controllers, can provide various benefits, such as performance optimization, efficiency improvement, and remote management. However, these devices also pose new security risks and challenges, as they can be targeted by malicious actors to disrupt the normal operation of the infrastructures they are connected to or to cause physical damage or harm. Therefore, it is essential to develop effective and intelligent solutions to detect and prevent attacks on IIoT devices and to ensure the security and resilience of critical infrastructures. In this paper, we present a comprehensive analysis of the types and impacts of attacks on IIoT devices based on a literature review and a data analysis of real-world incidents. We classify the attacks into four categories: denial-of-service, data manipulation, device hijacking, and physical tampering. We also discuss the potential consequences of these attacks on the safety, reliability, and availability of critical infrastructures. We then propose an expert system that can detect and prevent attacks on IIoT devices using artificial intelligence techniques, such as rule-based reasoning, anomaly detection, and reinforcement learning. We describe the architecture and implementation of our system, which consists of three main components: a data collector, a data analyzer, and a data actuator. We also present a table that summarizes the main features and capabilities of our system compared to existing solutions. We evaluate the performance and effectiveness of our system on a testbed consisting of programmable logic controllers (PLCs) and IIoT protocols, such as Modbus and MQTT. We simulate various attacks on IIoT devices and measure the accuracy, latency, and overhead of our system. Our results show that our system can successfully detect and mitigate different types of attacks on IIoT devices with high accuracy and low latency and overhead. We also demonstrate that our system can enhance the security and resilience of critical infrastructures by preventing or minimizing the impacts of attacks on IIoT devices. [ABSTRACT FROM AUTHOR]

Various types of numerical control (NC) machine tools can be standardly operated and controlled based on NC data that can be easily generated using widespread CAD/CAM systems. On the other hand, the operation environments of industrial robots still depend on conventional teaching and playback systems provided by the makers, so it seems that they have not been standardized and unified like NC machine tools yet. Additionally, robotic functional extensions, e.g., the easy implementation of a machine learning model, such as a convolutional neural network (CNN), a visual feedback controller, cooperative control for multiple robots, and so on, has not been sufficiently realized yet. In this paper, a hyper cutter location source (HCLS)-data-based robotic interface is proposed to cope with the issues. Due to the HCLS-data-based robot interface, the robotic control sequence can be visually and unifiedly described as NC codes. In addition, a VGG19-based CNN model for defect detection, whose classification accuracy is over 99% and average time for forward calculation is 70 ms, can be systematically incorporated into a robotic control application that handles multiple robots. The effectiveness and validity of the proposed system are demonstrated through a cooperative pick and place task using three small-sized industrial robot MG400s and a peg-in-hole task while checking undesirable defects in workpieces with a CNN model without using any programmable logic controller (PLC). The specifications of the PC used for the experiments are CPU: Intel(R) Core(TM) i9-10850K CPU 3.60 GHz, GPU: NVIDIA GeForce RTX 3090, Main memory: 64 GB. [ABSTRACT FROM AUTHOR]

The present study investigates the design analysis and development of a framed gantry robot (FGR) for 3-D concrete printing. Finite element simulations are performed for design validation and parameter estimation to conduct detailed analysis of the concrete printer. The kinematic and dynamic models of FGR are used for workspace analysis, motion control study, and motor sizing for printer development. The motion trajectory of the printer nozzle tip is evaluated using MATLAB simulations. An integrated programmable logic controller (PLC) with an in-built human-machine interface (HMI) through control algorithms has been implemented for interactive, user-friendly, and precise motion control. A separate control system is developed to monitor the pumping power and feed rate of mortar mix during printing. The simulated control scheme is validated experimentally using trajectory tracking for various input printing paths through HMI, with a maximum tracking error of 1 mm. Successful concrete printing trials have been conducted using various mix-design, following extensive evaluation of different printer and material parameters at the laboratory scale. The proposed design concept, mechanisms, interactive control interface, and tracking accuracy have been validated through simulation and experimental results, for ensuring quality concrete prints by the developed printer. Material testing parameters have been evaluated experimentally for modeling various mix-designs. To further evaluate printing performance, simulations of layer deformation in printed structures are conducted using finite element analysis, incorporating non-linear material models and various contact layer interactions. The simulated results of printed layers of different shapes of structures show a close resemblance with the experimental concrete printing trials. [ABSTRACT FROM AUTHOR]

The motivation for this work arose when noticing that definitions of honeypots' interaction level are mainly based on the information technology environment and do not reflect operational technology even if several honeypot projects approach this field. Within operational technology, programmable logic controllers (PLCs) have a main role, resulting in several honeypot researchers choosing to mimic this device at a certain interaction level. However, searching for an interaction level definition that approaches PLCs results in few studies. In this context, this work aims to explore how to adapt the information technology definition of the interaction level in order to encompass PLCs and their specific features. The method chosen to obtain inputs was a literature review where, in attempting to keep the connection with information technology, the features were based in terms of honey system, honey service, and honey token. The findings of this review provide a means to translate these terms when developing a PLC honeypot for a desired interaction level, resulting in a metrics proposal for low and high interaction. Summarizing the proposed metrics, the system of a PLC can be considered as the vendor specific firmware, its unique device banner, and a realistic network topology. For services, a PLC honeypot reflects the tasks performed by the real device, thus resulting in industrial communication protocols, network management protocols, appropriate response times, code-related interactions, dynamic input and output data processing, physical process simulation, and web interface. Lastly, a PLC honey token can be approached with the PLC program file, MIB file, and software license, among other elements. Based on these metrics, researchers can better evaluate how to design a programmable logic controller honeypot or select tools that match their target interaction level. [ABSTRACT FROM AUTHOR]

This paper describes the use of the nonlinear autoregressive with exogenous inputs neural network for the control of electro-hydraulic servo drive. The direct inverse controller was trained on a real object in an online way with the use of a programmable logic controller before starting work on a real object, and appropriate tests were performed in the MATLAB/Simulink environment in order to select the right structure of the neural network. Also, the paper includes various network learning algorithms: gradient descent, resilient backpropagation, and adaptive moment estimation, which belong to backpropagation algorithms. In order to compare the suitability of the direct inverse controller, the controller was implemented on a real electro-hudraulic test stand, and its performance was compared with the performance of a proportional-integral-derivative controller. [ABSTRACT FROM AUTHOR]

The three-point controller is one of the simplest controllers used in industry. Most environments with programmable logic controllers and distributed control systems allow such blocks in their design. The three-point controller consists of three switching states, i.e., one input and two outputs, depending on the input signal's positive or negative deviation from the desired value. It allows for an upper and lower limit of deviation where no switching takes place. The three-point step control system extends the step controller with a corrective feedback loop. This paper presents the implementation and parameter evaluation of the three-point step control system. Based on theoretical analysis and simulations, general comments are made on the impact of the controller parameters on the control system's performance. Finally, recommendations for tuning the three-point step controller are formulated. [ABSTRACT FROM AUTHOR]

Growth chambers are control devices used in plant research. However, their high costs limit their application to a single environment, which compromises their efficiency for the generation of experimental data. The objective of this research was to design, construct, and assess the performance of a multi-chamber environmental control system capable of regulating both air temperature (AT) and relative humidity (RH). The evaluation was carried out using Syngonium podophyllum Schott plants. Three constant target temperature values (18, 23, and 40 °C) were established, together with a target RH value of 60 %. AT and RH data were recorded every 4 min for three days. The system was built with 15 growth chambers and the following devices: output (AT, RH, and lighting control), input (sensors), and central control managed by two Arduino® Mega 2560 based microcontrollers, plus a programmable logic controller. Programming was done in open-source languages such as Arduino® and Python®. Results indicate that the absolute error (AE) and standard deviation (SD) in AT were higher when the target temperature was lower (AE = 0.59 °C and SD = 0.93 °C) compared to the other higher target temperatures (AE = 0.31 °C and SD = 0.43 °C at 23 °C, and AE = 0.25 °C and SD = 0.51 °C at 40 °C). On the other hand, the AE in RH increased as temperature increased, going from AE = 3.75 % and SD = 5.12 % at 18 °C, to AE = 9.23 % and SD = 10.77 % at 40 °C. The use of open-source technologies allowed the construction of a multi-chamber environmental control system capable of replicating controlled environments. [ABSTRACT FROM AUTHOR]

Water quality is essential for a healthy life, so it is necessary to look for technologies to measure its parameters in real time and automatically. The purpose of this study was to implement and determine the reliability of an automated system to evaluate the organoleptic water quality intended for human consumption, in the urban distribution network of the district of Daniel Hernandez (Peru), using a programmable logic controller (PLC) and Simulink. The study was carried out from January to March 2024, corresponding to the rainfall season. In the process of the research, a data acquisition and processing algorithm was implemented in a Simatic S7 1500 PLC with analog input module; using the national sanitation foundation water quality index (NSF-WQI) methodology. The work focused on five key water parameters: potential hydrogen (pH), electrical conductivity (EC), turbidity, free chlorine (FCL) and temperature. The methodology included programming in contact language (KOP) of the algorithm for calculating subscripts for each parameter, according to the functions established by NSF-WQI. Measurements were performed with 4-wire sensing devices with 4–20 mA current signals, ensuring data accuracy. The interface to visualize the parameters and the water quality index was implemented in Simulink, communicating via OPC UA with the PLC server, facilitating the graphical representation of the organoleptic water quality index. The overall equipment efficiency (OEE) or automated system implemented was 90.56%, indicating its acceptable reliability for evaluating water quality. By performing the measurements, with the sensors of the five parameters immersed in tap water, at each of the three established sampling points (Dwelling_1, Dwelling_2 and Dwelling_3) along the water distribution network, the system facilitated the automated and real-time evaluation of the quality, resulting in an average NSF-WQI of 83.08%, classifying the water as good for human consumption. This information is important for water quality management and can guide future treatments to achieve better quality. [ABSTRACT FROM AUTHOR]

This study investigates how blade aerodynamic modifications, including leading edge roughness (LER), influence wind turbine performance over their operational lifespan. It introduces a methodology developed to examine the intricate relationship between blade erosion, blade enhancements, operations and maintenance (O&M) events, control programmable logic controller (PLC) parameter updates, and their cumulative impact on turbine efficiency. Analysing data from 12 multi-megawatt offshore turbines over a 12-year period, the investigation hinges on the integration of supervisory control and data acquisition (SCADA) data, O&M records, and air density corrections. A key contribution is the development of the turbine performance integral (TPI) method, which, for the investigated turbines, leverages generator speed and power output data to track performance trajectories. Seasonal trend decomposition using locally estimated scatterplot smoothing (STL) further isolates long-term trends and seasonal variations in performance. Despite data availability and quality limitations, the study reveals significant findings concerning the impact of manufacturer software updates on turbine control strategies, resulting in improved performance; the variable effects of blade repairs and enhancements; and the complex interaction between O&M events and performance. This work applies a methodical approach and statistical rigour, offering a path forward for effectively monitoring wind turbine efficiency and advancing renewable energy. [ABSTRACT FROM AUTHOR]

This research paper discusses security issues with current deployments of Programmable Logic Controllers (PLCs) and Supervisory Control and Data Acquisition Systems (SCADA) in the industry and proposes a solution that enables PLC devices to query a blockchain infrastructure for commands and setpoints. The blockchain assumes a dual role in this context: serving as an immutable audit trail database as well as a trusted source for critical commands and setpoints. In contrast to the conventional paradigm, this novel approach does not require write access at the PLC level, thus minimizes its attack surface and helping to protect against known and zero-day vulnerabilities often used in cyberwarfare, such as in the case of the notorious Stuxnet worm. Applications that enforce the logging of user operations for Good Manufacturing Practices (GMP) or compliance purposes use the blockchain network as an audit trail database for user actions. Any attempt to maliciously circumvent the logging operation would not affect the operation of a critical process. Additionally, a prototype implementation developed as part of this research finds that modern PLC devices are more than capable of interacting with private Ethereum blockchain nodes. The required libraries and user code consume a small percentage of available resources, while the duration of a complete request-response cycle measured around 22msec. The authors anticipate that PLCBlox can be used as a drop-in replacement for applications requiring higher security standards and logging enforcement, such as nuclear power plants or other critical infrastructure. [ABSTRACT FROM AUTHOR]

The integration of automation equipment with intelligence has undoubtedly become the trend in the automation industry. In this regard, automated optical inspection (AOI) application, with advantages such as not being limited by working hours, rapid inspection, and low labor costs, are applied in the automation field. For mass production scenarios, AOI can address the allocation of operators, learning costs, and deficiencies in production efficiency. In this study, we adopted the You Only Look Once version 7 (YOLOv7) architecture for the AOI system to conduct sample inspections. At the same time, generative adversarial networks (GANs) were used to expand the sample database. The Ethernet communication architecture was also employed to integrate equipment such as a six-axis robotic arm, programmable logic controller (PLC), and an industrial computer. This integration gave the system intelligent functions such as automatic scheduling, production report statistics, and real-time monitoring. A graphical user interface was also designed to reduce personnel learning costs, simplify operations, and enhance equipment uptime. Ultimately, through the training of the YOLOv7 detection model, we achieved excellent detection results. The detection precision reached 91%, and the mean average precision (mAP@0.5) reached 83%. This confirms the value of using GAN technology to expand the AOI sample database, especially when there is a shortage of samples in the early stages of production. This high accuracy not only helps improve the accuracy of AOI detection but also enhances the production efficiency of the intelligent processing system. [ABSTRACT FROM AUTHOR]

The article from World Pumps discusses the challenges and opportunities faced by manufacturers in the pulp and paper market in Latin America. Despite challenges such as declining demand for traditional paper products and environmental concerns, the industry continues to thrive, with Latin America leading in kraft pulp production. The region's favorable climate, abundant resources, and infrastructure have contributed to rapid growth, with new plants being developed to meet increasing demand. Pump manufacturers play a crucial role in the production process, with digitalization and automation enhancing efficiency and performance in the industry. [Extracted from the article]

The study reports the results of a comparative analysis of advanced high-accuracy Stewart-lift platform along with a comparative study of dynamic control. A control system powered by a programmable logic controller (PLC) was used. The properties of the system were described using a dynamic model using the Lagrange method. The real object was verified by performing several tests and comparing them using quality indicators. The results of verification tests conclusively demonstrate the system suitability for applications within industrial automation and robotics systems. [ABSTRACT FROM AUTHOR]

SCADA systems play an important role in tracking the behaviour of critical process variables and connecting geographically dispersed subsystems at the industrial plant level. This article presents a PLC and SCADA-based control framework to automate and supervise the temperature control processes in the heat exchanger plant. The OMRON (NX1P2-9024DT1) PLC is interfaced with the Wonderware InTouch SCADA system to gather data, create a simulated temperature control prototype and carry out the necessary control operations within the heat exchanger plant. The PLC controls the entire process and programming of PLC is done using Sysmac studio automation software using the ladder programming language. The proposed system controls the temperature of the heat exchanger system through PID and Fractional Order PID (P I λ D μ ) controllers with Integral Anti-windup technique. Various control strategies like Cascade Control, Feedforward Control and Smith Predictor for time delayed process are discussed for controlling the temperature of the process. The performance of both PID and fractional order PID controllers is optimized using adaptive heuristic optimization techniques like Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). In control system design and analysis, the calculated performance indices are used as quantitative measures for evaluating the performance of a system. The combined form of temperature controller with Cascade control, Feedforward control and dead-time compensator is modelled and examined for simulation using MATLAB. Simulation and real-time experimentation analysis of the developed controllers are executed with metaheuristic optimization techniques based on different performance indices like ISE, IAE and ITAE. [ABSTRACT FROM AUTHOR]

The automated guided vehicle (AGV) is currently used in a wide range of industries. The control system is the core of the AGV, responsible for task assignment, localization, path planning, motion planning, vehicle management, etc. In previous AGV implementations, personal computer, embedded computer, digital signal processor for computing, and programmable logic controller, micro control unit, field programmable gate array for control have been combined to form the whole control system. These solutions can solve common control problems, but they are complex to implement. To simplify the implementation complexity, the following work is presented in this study: (1) a new real-time operating system (RTOS), called Student RTOS, is developed. The RTOS includes memory management, task management, message queue, semaphore, synchronization, and timer, etc.; (2) a control system based on the newly designed RTOS is presented on a single processor; (3) the RTOS and the control system are tested on a newly designed AGV. From the actual test results, the newly designed RTOS and control system can meet the requirements of real-time, functionality, etc., and can effectively replace the original control system composed of multiple device combinations. In addition to running on the ARM Cortex-A processor used in the AGV control system, the RTOS also runs on the ARM Cortex-M and the STM8S (8-bit) processors for more low-end applications. [ABSTRACT FROM AUTHOR]

During the operation of jack-up offshore platforms, the complex and variable seabed geological conditions involved can lead to serious accidents, such as pile leg punctures and platform tilt. The aim of this study is to introduce an early warning method for punctures that ensures the operational safety of these platforms. To accomplish this, a real-time monitoring and controlling system is designed using a programmable logic controller that shares data from sensors measuring displacement, tilt, and pressure. In addition, an experimental device is constructed to simulate a jack-up offshore platform in order to assess the safety response of the control system. The working state of the platform under different working conditions (puncture of one independent pile, same side or diagonal piles, and three-pile linkage) is evaluated by analysing the structural motion response, including platform tilt and foundation pressure. The findings reveal that the system developed in this study accurately detects the tilt condition of the offshore platform, and can ensure the operational safety of jack-up offshore platforms. [ABSTRACT FROM AUTHOR]

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Copyright of FME Transactions is the property of University of Belgrade, Faculty of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, we propose an intelligent drinking water controller based on programmable logic controller (PLC) specifically designed for pig breeding, which significantly reduces the water waste caused by the use of traditional drinking bowls by regulating the frequency and flow of water release. In addition, the drinking water system has a tracking and recording function, which can record the frequency and duration with which fattening pigs drink water in each pen in detail, thus providing farmers with a wealth of pig health and behavior data to help optimize breeding management decisions. In order to deeply analyze the effects of the intelligent drinking water controller on the growth, resources environment and economic benefits of fattening pigs under the condition of large-scale breeding, a single factor comparison experiment was designed.In this experiment, 84 fattening pigs were selected and distributed in 12 pens. Among them, six pens were randomly designated as the control group;the pig in this group used ordinary drinking water bowls for the water supply. The other six pens were designated as the experimental group;the pigs in this group used the intelligent drinking water controller. The experimental results showed that in the experimental group with the intelligent drinking water controller, the average daily water waste per finishing pig was only 0.186 L (p < 0.05), accounting for only 25.98% of the average daily water waste per pig in the control group (p < 0.05). In terms of water quality, the intelligent drinking water controller also showed better performance, and the performance indicators were effectively reduced, with the highest reduction reaching 39.86%, which greatly reduced water pollution. Compared with the traditional drinking bowl, the average daily weight increment of fattening pigs in the pen using the intelligent drinking water controller was increased by 0.02 kg. In terms of long-term benefits, the PLC-based intelligent drinking water controller significantly improves the economic returns of the farm and has a positive impact on pig health. The high frequency data collection of the pigs' drinking habits through the intelligent drinking water controller can also provide data support for the subsequent establishment of a pig water-drinking behavior analysis model. [ABSTRACT FROM AUTHOR]

In this article, we propose a distributed synchronization solution to achieve decentralized coordination in a system of collaborative robots. This is done by leveraging cloud-based computing and 5G technology to exchange causal ordering messages between the robots, eliminating the need for centralized control entities or programmable logic controllers in the system. The proposed solution is described, mathematically formulated, implemented in software, and validated over realistic network conditions. Further, the performance of the decentralized solution via 5G technology is compared to that achieved with traditional coordinated/uncoordinated cabled control systems. The results indicate that the proposed decentralized solution leveraging cloud-based 5G wireless is scalable to systems of up to 10 collaborative robots with comparable efficiency to that from standard cabled systems. The proposed solution has direct application in the control of producer–consumer and automated assembly line robotic applications. [ABSTRACT FROM AUTHOR]

To improve the uniformity of microwave heating and enhance the quality of continuous microwave drying for raspberry pulp, a self-developed programmable logic controller (PLC) control system with pulsed microwave output was implemented. The central composite experimental design method was employed to analyze the effects of four experimental factors, namely microwave duty cycle, pulse period, air velocity and drying time on the average temperature, temperature dispersion value, moisture content, moisture content dispersion value, anthocyanin retention rate, and anthocyanin retention rate dispersion value of raspberry pulp. The optimal process for pulsed microwave drying of raspberry pulp was obtained. Compared with dried pulp quality and uniformity indexes from pulsed microwave drying, commonly used feedback temperature control microwave drying, intermittent microwave drying, and continuous microwave drying processes to develop a continuous microwave drying quality optimization control system for raspberry pulp. The results showed that the magnetron from pulsed microwave drying in the form of pulse output rectangular wave form power through the PLC control system. Turning on the magnetron intermittently was more effective than continuous microwave heating. Pulsed microwave drying of raspberry pulp has better product quality and heating efficiency, lower cost and better reliability than feedback temperature control systems. The optimal process parameters for pulsed microwave drying were microwave duty cycle of 0.6, pulse period of 17 seconds, air velocity of 1.9 m/s, and drying time of 12.5 minutes. Compared with continuous microwave drying, pulsed microwave drying resulted in the highest quality of dried pulp as a 35.31%±0.55% increase in anthocyanin retention, a 33.59%±0.23% increase under feedback temperature control, a 16.42%±0.18% increase under intermittent microwave drying. Pulsed microwave drying can control the power to reduce the dried fruit pulp temperature, improve the uniformity of post-drying quality and provide a basis for the industrial production of dried raspberry products. [ABSTRACT FROM AUTHOR]

During adaptive laboratory evolution experiments, any unexpected interruption in data monitoring or control could lead to the loss of valuable experimental data and compromise the integrity of the entire experiment. Most homemade mini-bioreactors are built employing microcontrollers such as Arduino. Although affordable, these platforms lack the robustness of the programmable logic controller (PLC), which enhances the safety and robustness of the control process. Here, we describe the design and validation of a PLC-controlled morbidostat, an innovative automated continuous-culture mini-bioreactor specifically created to study the evolutionary pathways to drug resistance in microorganisms. This morbidostat includes several improvements, both at the hardware and software level, for better online monitoring and a more robust operation. The device was validated employing Escherichia coli, exploring its adaptive evolution in the presence of didecyldimethylammonium chloride (DDAC), a quaternary ammonium compound widely used for its antimicrobial properties. E. coli was subjected to increasing concentrations of DDAC over 3 days. Our results demonstrated a significant increase in DDAC susceptibility, with evolved populations exhibiting substantial changes in their growth after exposure. [ABSTRACT FROM AUTHOR]

Sprinkler irrigation is widely used in agriculture because it allows for rational use of water. However, it can induce negative effects of soil erosion and of surface waterproofing. The scholars of these phenomena use the numerical integration of the equation of motion, but if there was an analytical solution, the study would be facilitated, and this solution could be used as software for regulating sprinklers. Therefore, in this study, the solution of the differential equation of the flight of droplets produced by sprinklers in the absence of wind was developed. The impossibility of an exact analytical solution to the ballistic problem due to the variability of the drag coefficient of the droplets is known; therefore, to find the integrals in closed form, the following were adopted: a new formula for the drag coefficient; a projection of the dynamic's equation onto two local axes, one tangent and one normal to the trajectory and some linearization. To reduce the errors caused by the latter, the linearization coefficients and their calculation formulas were introduced through multiple non-linear regressions with respect to the jet angle and the initial droplet speed. The analytical modeling obtained, valid for jet angles from 10° to 40°, was compared to the exact numerical solution, showing, for the total travel distance, a high accuracy with a mean relative error MRE of 1.8% ± 1.4%. Even the comparison with the experimental data showed high accuracy with an MRE of 2.5% ±1.1%. These results make the analytical modeling capable of reliably calculating the travel distance, the flight time, the maximum trajectory height, the final fall angle and the ground impact speed. Since the proposed analytical modeling uses only elementary functions, it can be implemented in PLC programmable logic controllers, which could be useful for controlling water waste and erosive effects on the soil during sprinkler irrigation. [ABSTRACT FROM AUTHOR]

The paper presents a case study that applies a model predictive control (MPC) approach in a Micro850 programmable logic controller (PLC) to a laboratory pressure swing adsorption (PSA) process used for separating gas mixtures of CO2 and CH4. PLC is an industrial hardware characterized by its robustness to hazardous environments and limited computational capacities, which poses computational challenges for MPC implementation. This paper's main contribution is the application of the modified Takagi–Sugeno–Kang-based MPC (MTSK-MPC) algorithm to this PSA unit, which provides features to investigate and implement feasible MPC designs in PLCs. The investigation consists of a sensitivity analysis of how some design parameters influence the PLC memory and the MPC implementation and a comparative evaluation of the computational processing from different MPC algorithms and simulations. The comparison comprises software-in-the-loop simulations with three algorithms in the PC: an implicit MPC, an explicit MPC, and the MTSK-MPC. Additionally, it includes a hardware-in-the-loop simulation with the implemented MTSK-MPC in Micro850. The results show that the MPC algorithms achieve close performance, tracking setpoint changes and rejecting output disturbances, with the MTSK-MPC presenting the lower processing time among the MPCs in the PC. The study concludes that the implementation of MTSK-MPC in the Micro850 is feasible. [ABSTRACT FROM AUTHOR]

Aqueous Organic Redox Flow Batteries are a family of promising energy storage systems. However, they face various challenges related to their lifetime, such as the Faradaic imbalance due to the occurrence of parasitic reaction leading to the fading of its energy storage capacity. Herein, automatization of a rebalancing system to reverse the detrimental effects of Faradaic imbalance due to the unavoidable presence of small quantities of oxygen in the negative reservoir or hydrogen evolution reaction is developed and implemented in an alkaline flow battery. A membrane‐free rebalancing cell is proposed to promote the oxygen evolution reaction and reverse the accumulated charge in the catholyte showing a 100 % coulombic efficiency. The programmable logic controller monitors the open circuit voltage to calculate the charge stored in each charge/discharge step and closes a circuit so a fixed voltage is applied to the rebalancing cell when the battery needs to be rebalanced. The system is tested using an alkaline flow battery consisting of ferrocyanide and 2,6‐dihydroxyanthraquinone, improving the energy capacity retention from 0.27 % cycle‐1 and 0.47 % h‐1 without rebalancing system to 100 % retention after >850 cycles and >24 days (without Ar‐filled glovebox), demonstrating the feasibility of this proposed system to address the Faradaic imbalance. [ABSTRACT FROM AUTHOR]

A cryptography algorithm for data transfer encryption provides confidentiality, requires considerable computing power, and is not commonly implemented in embedded systems with limited computing power, such as Programmable Logic Controller (PLC). PLC is the core component for automation and control in industrial automation. For decades, PLC has prioritized speed over security; program execution in PLC must be as efficient as possible. The cryptography algorithm uses a seed, the initialization vector, to encrypt the data with the cryptography key to strengthen the encryption. Pseudo Random Number Generator (PRNG) can be used as the initialization vector. This paper proposes the XORasm PRNG algorithm, the lightweight XORshift-based algorithm with a modified seed from the system's clock. The applied methodology generates and visualizes PRNG, tests the randomness, and implements the PRNG on compact PLC. XORasm is evaluated statistically with runs-test in simulation by comparing the algorithm to one of the simulated compact PLC's PRNG implementations. The findings from this research are that p-values demonstrate that XORasm is statistically and significantly more random than the current implementation, and there is evidence that XORasm's generated data distribution is practically random at a 99.95% confidence level, suitable for implementation in embedded systems as a lightweight PRNG. [ABSTRACT FROM AUTHOR]