Programmable Logic Controller Research Articles

Carryover slag weight prediction under converter tapping process by applying attention-shifting infrared thermal mapping technique

The prediction model of actual slag weight under converter tapping process was constructed by applying attention-shifting infrared thermal mapping technique. Firstly, the linkage between programmable logic controller signal of the converter mechanical structure and the infrared thermal mapping detector image was realized to ensure the real-time monitoring of the whole tapping process. Images should be preprocessed to ensure a good distinction between the flowing liquid and background at the tapping beginning, furthermore to separated slag from liquid steel at the following process. Moreover, the use of attention-shifting block belonging to the infrared thermal detector was effect to obtain more accurate image distinction between the liquid steel and slag. Hence the gray degree threshold value was adjusted to 220–225 in the early stage, and the value was needed to be lowered to 170–176 in the end. After using the model, the slag weight was less than < 8.0kg/t when only slag-stopping awl was adopted in tapping process. The average weight of slag in converter was 7.3kg/t by using slag-stopping awl and sliding plate slag-blocking.

Sistem Otomatisasi Pemberian Pakan secara Terjadwal untuk Pembibitan Ikan Lele di Fardu Farm Pekanbaru menggunakan PLC dan IoT

<p><strong>With the increasing global population growth, the demand for fishery products, including catfish, continues to rise. Catfish seed production plays a crucial role in the fisheries production chain, as this stage ensures the availability of high-quality fish seeds. However, fish farmers still face challenges in increasing aquaculture production, especially regarding inefficient manual feeding practices. This research aims to develop an automated catfish feeding system on a schedule at Fardu Farm using a combination of Programmable Logic Controller (PLC) and Internet of Things (IoT) technologies. In this study, the researchers design and implement an automation system that enables scheduled feeding in catfish seedlings. The use of Outseal PLC connected to IoT allows for precise and efficient feeding control. This research conducts testing to verify the system's performance, including measuring the weight of the feed dispensed and testing feeding over specific time periods. The results of the study indicate that this automation system successfully provides timely feeding according to the established schedule. Additionally, manual control using the Blynk platform also yields good responsiveness. Thus, the developed system can enhance efficiency in feeding catfish seedlings at Fardu Farm.</strong></p><p><strong><em>Keywords</em></strong> - <em>Automation, Feeding, Catfish Breeding, PLC, IoT.</em></p>

IoT Enabled Motor Drive Vehicle for the Early Fault Detection in New EnergyConservation

An IoT-enabled motor drive vehicle integrates Internet of Things (IoT) technology with traditional vehicle systems to enhance control, monitoring, and automation. Sensors and connected devices within the vehicle collect real-time data on parameters such as speed, battery status, motor performance, and environmental conditions. This data is transmitted to cloud-based platforms for analysis, enabling remote diagnostics, predictive maintenance, and optimization of vehicle performance. IoT integration also facilitates features like vehicle tracking, smart navigation, and user-specific adjustments, improving overall efficiency, safety, and user experience. This paper investigates the utilization of IoT enabled Programmable Logic Controller (PLC) technology to enhance fault detection in new energy vehicle (NEV) motor drive systems. With the increasing adoption of electric vehicles, ensuring the safety and reliability of motor drive systems becomes paramount. The study begins with a comprehensive review of existing literature, examining various fault detection methodologies and technologies. Subsequently, simulation analyses are conducted to evaluate the performance of IOT enabled PLC-based fault detection algorithms under different operating conditions. This paper presents the numerical results of fault detection in new energy vehicle (NEV) motor drive systems using Programmable Logic Controller (IOT enabled PLC) technology. Through comprehensive simulations and experimental validations, the IOT enabled PLC-based fault detection algorithms achieved an average detection accuracy of 93% across various fault scenarios. The response time of the fault detection system.

Design and Construction of Automatic Railway Crossing Gate Control Using Proximity and Infrared Sensors Based on Omron CP1E E30-SDRA PLC

Railway crossing gates are one of the railway infrastructure facilities. Currently, there are still many problems, especially in traffic accidents. The cause of traffic accidents at railway crossings generally occurs due to the lack of facilities and infrastructure (rail crossing gates) and negligence of guards in carrying out their duties. Therefore, it is necessary to design an automatic railway crossing gate. The prototype of the automatic railway crossing controller uses proximity and infrared sensors to detect the arrival and departure of trains. When the infrared or proximity sensor detects the arrival of a train, the Programmable Logic Controller (PLC) activates Pulse Width Modulation (PWM), buzzer, and Light Emitting Diode (LED), so that the crossing motor goes down. After the crossing touches the limit switch, the motor stops and the buzzer and Light Emitting Diode (LED) remain on. The infrared or proximity sensor detects the departure of the train so that it reverses the motor so that the crossing goes up, then turns off the buzzer and Light Emitting Diode (LED). In this way, it is hoped that this design can be used to increase the efficiency of the system's operational costs and optimize the railway crossing system and is expected to reduce the number of traffic accidents at railway crossings.

PLC Controlled Fuzzy Logic-Based Egg Hatching Machine

In response to the increasing food demand due to the growing global population, this study has designed a fully automated incubator machine based on a Programmable Logic Controller (PLC) to enhance efficiency in the egg production sector. To align with Industry 4.0 technologies, this machine controls temperature and humidity using fuzzy logic, one of the artificial intelligence methods. The incubator is capable of meeting the specific temperature and humidity needs for the incubation processes of various types of bird eggs. It has been tested under different conditions, and its performance has been examined in detail. The analysis results show that the fuzzy logic-based temperature and humidity control systems on the PLC successfully reached the set reference values and maintained them continuously and stably after initial fluctuations. In conclusion, the design and control of an automated incubation machine with a PLC-based fuzzy logic controller were successfully accomplished.

Concept of flexible no-code automation for complex sample preparation procedures

Driven by demographic changes and dwindling Science Technology Engineering Mathematics enrolments, our research introduces no-code automation as a strategic response, aimed at mitigating labor shortages while enhancing productivity and safety in the laboratory environment. Employing a user-friendly, no-code software platform, we automated a complex HPTLC assay, enabling laboratory personnel to configure and modify workflows without requiring specialized programming skills. The manuscript outlines the deployment of a collaborative robot (cobot), a programmable logic controller (PLC), and the utilization of self-developed open-source hardware components to establish automated stations for sample handling, incubation, spraying, detection, and storage within the assay process. The research addresses challenges such as the handling of fragile HPTLC plates and the seamless integration of automated stations, solved through innovative design solutions and adaptive programming methods. This investigation demonstrates the feasibility and efficiency of no-code automation in overcoming skilled labor deficits.

Obfuscation strategies for industrial control systems

Recently released scan data on Shodan reveals that thousands of Industrial Control Systems (ICSs) worldwide are directly accessible via the Internet and, thus, exposed to cyber-attacks aiming at financial gain, espionage, or disruption and/or sabotage. Executing sophisticated cyber–physical attacks aiming to manipulate industrial functionalities requires a deep understanding of the underlying physical process at the core of the target ICS, for instance, through unauthorized access to memory registers of Programmable Logic Controllers (PLCs). However, to date, countermeasures aiming at hindering the comprehension of physical processes remain largely unexplored.In this work, we investigate the use of obfuscation strategies to complicate process comprehension of ICSs while preserving their runtime evolution. To this end, we propose a framework to design and evaluate obfuscation strategies for PLCs, involving PLC memory registers, PLC code (user program), and the introduction of extra (spurious) physical processes. Our framework categorizes obfuscation strategies based on two dimensions: the type of (spurious) registers employed in the obfuscation strategy and the dependence on the (genuine) physical process. To evaluate the efficacy of proposed obfuscation strategies, we introduce evaluation metrics to assess their potency and resilience, in terms of system invariants the attacker can derive, and their cost in terms of computational overhead due to runtime modifications of spurious PLC registers. We developed a prototype tool to automatize the devised obfuscation strategies and applied them to a non-trivial use case in the field of water tank systems. Our results show that code obfuscation can be effectively used to counter malicious process comprehension of ICSs achieved via scanning of PLC memory registers. To our knowledge, this is the first work using obfuscation as a technique to protect ICSs from such threats. The efficacy of the proposed obfuscation strategies predominantly depends on the intrinsic complexity of the interplay introduced between genuine and spurious registers.

METODOLOGÍA DE AUTOMATIZACIÓN DE UN PROCESO HÍBRIDO PARA LA ELABORACIÓN DE EMULSIONES

The methodology is developed for the automation of the emulsion manufacturing process using hybrid energy (Termo Solar). The above was developed for a sunscreen and cosmetics manufacturing company, which produces macroemulsions with the aim of making the process sustainable, increasing production efficiency, reducing the consumption of fossil fuels and thus reducing CO2 emissions. This methodology incorporates a Human Machine Interface (HMI) into the design using a programmable logic controller (PLC). The developed control system made it possible to standardize the production process and record data in real time. The emulsion manufacturing process was characterized, and the dynamics of the process were verified. With the methodology used, the emulsion manufacturing time was reduced by 20% compared to that carried out manually. The use of solar thermal energy reduced the use of fossil fuels by 40%. Keywords: Termo solar energy, Programmable Logic Controller, Instrumentation, Human-Machine Interface.

Data losses and synchronization according to delay in PLC-based industrial automation systems

PLC (Programmable Logic Controllers) based data collection is integral to industrial automation and data acquisition processes. The pipeline – between the device and the database – is a complex system with many components and often causes some time delay, due to the synchronization and the applied hardware and software components. This delay can also lead to data loss on the database site. In this study, we aimed to examine this problem connected to the synchronized behavior of four counter networks within the TIA software for PLCs, particularly focusing on the implications of a 1 Hz (Hz) clock frequency on counter synchronization. Meticulous experiments were conducted and the integration of Node-RED, as an instrumental tool in industrial automation, facilitated counter-behavior monitoring. Recorded values of counter and timestamps were meticulously stored in four separate databases (MSSQL, MySQL, MongoDB, and Apache Cassandra) for comprehensive data analysis. Throughout the experiments, inconsistencies in the counter values were encountered, leading to the discovery of missing values that varied across the ten tests. To detect the reason, a unique delay calculation method was developed. According to our results, we were able to do post-synchronization with millisecond-level accuracy. It can help reveal the missing values that depend on the Node-RED and the PLC cycle time differences.

Closed-loop Speed Control for a Three-Phase Alternating-Current Motor using a Modbus Network

This paper presents a closed-loop controller for speed regulation of a three-phase induction motor by means of a Modbus network, which is compound only by industrial-purpose hardware, namely two programmable logic controllers, a human-machine interface and a variable-frequency drive. This represents a step beyond the state of the art because the results in similar works reported in the literature were obtained using personal computers, academic-purpose hardware or a combination of both academic-industrial technologies. The speed regulation was achieved through a proportionalintegral controller executed in a programmable logic controller, whose feedback was supplied by another similar device completely dedicated to read and decode an industrial optical encoder; furthermore, the control signals were commanded to the alternating current motor using a variable-frequency drive. The proposed control network was tested through diverse regulation experiments, under different conditions, in order to assess its effectiveness and robustness. The obtained results were successful in terms of accuracy of the speed set-point tracking in comparison to the most similar works of the state of the art, namely zero steady-state error was achieved, 12 seconds earlier for 75% larger references and 22% less RMSE than other works, which are the best results reported so far. Such successful achievements encourage the later implementation of more advanced control techniques on three-phase-motor based industrial machinery using only industrial-purpose hardware.

Integrating deep learning, MATLAB, and advanced CAD for predictive root cause analysis in PLC systems: A multi-tool approach to enhancing industrial automation and reliability

The integration of Deep Learning (DL), MATLAB, and Advanced Computer- Aided Design (CAD) in the root cause analysis of prognostic errors in Programmable Logic Controller (PLC) systems represents a significant advancement in industrial automation and reliability. This research explores the synergistic application of these technologies to diagnose, predict, and mitigate failures in PLC systems, which are critical for controlling automated processes in various industries. By employing DL algorithms, the study enhances predictive maintenance capabilities, allowing for early detection of anomalies and reducing downtime. MATLAB is utilized as the central platform for data processing, algorithm development, and simulation, providing a versatile environment for integrating DL models with real-time data from PLCs. Advanced CAD tools are employed to model and visualize the physical systems controlled by the PLCs, offering a comprehensive view that bridges the gap between digital analysis and physical implementation. The research methodology includes data collection from PLC systems, DL model training and validation, MATLAB-based simulations, and CAD modelling. The findings demonstrate improved accuracy in identifying the root causes of PLC prognostic errors, leading to more efficient maintenance strategies and enhanced system reliability. This paper concludes that the integration of DL, MATLAB, and CAD provides a powerful approach for advancing predictive maintenance in industrial settings, ultimately contributing to greater operational efficiency and cost savings.

Determining the efficiency of techniques for optimizing the number of tags in modern human-machine interfaces under conditions of limited resources

The object of this study is modern Human-Machine Interfaces (HMI) and SCADA systems in the industry. The subject of research is techniques for optimizing the number of tags (variables) in the SCADA/HMI environment to enhance resource utilization efficiency. One of the challenges in creating SCADA/HMI-based solutions can be the number of tags (variables) in the runtime environment. A large number of tags can lead to a problem of limited available resources. The technique presented here allow for the optimization of the number of tags used in Human-Machine Interface systems built with SCADA software and operator panels in combination with Programmable Logic Controllers (PLCs). An evaluation of the efficiency of techniques for reducing the number of HMI tags was conducted on an experimental configuration consisting of objects such as discrete input/output, analog input/output, actuators such as valves with discrete/analog control, and drives with frequency converters. The optimization coefficient, defined as the ratio of the number of input/output tags used directly to the number of tags after applying the optimization principle, was used as the efficiency criterion. Depending on the techniques and their combinations, the criterion values reached orders of 4, 10, and in one case even more than 100. These values are explained by the application of multiplexing approaches and various packing techniques. The advantages and disadvantages of the reported techniques, as well as their application limitations, have been identified. Some techniques are suitable only for specific tasks. These techniques could be applied in practical implementation when designing modern high-efficiency Human-Machine Interfaces under conditions of limited resources.

Optimal Energy Management System in Thailand Convenience Stores

The purpose of this research is to study and investigate the energy consumption and reduction of convenience stores. There is one retail building that is increasing rapidly in Thailand every year, and almost all of them operate 24 hours per day. There is a significant amount of energy consumption. This research is to present an updated energy portion of convenience stores through sampling at 13 stores. These stores have a sales area of 153 to 311 m2 and operating hours of 16 to 24 hours per day. We found that energy consumption recorded from the Digital Energy Meter shows that all of them have cooling systems that use a huge portion of energy (refrigeration and air conditioning systems, 48% and 24%, respectively). Moreover, this research aims to develop a Retail Energy Management System (REMS) to increase the energy efficiency of air conditioning systems at convenience stores in Thailand while still maintaining comfort for occupants by keeping the required temperature at 26 degrees Celsius. We selected 3 of 13 stores that have the same sales area of 280 m2 but different operating hours and numbers of air conditioners. We optimized the operating air conditioning system by data-driven with the Internet of Things system concept, which manages and controls the air conditioning system through the Programmable Logic Control (PLC) program. REMS can reduce energy consumption by 24 and 15 kWh/day, or 14.5% and 8.3%, at operating hours of 24 and 16 hours per day, respectively. There is an average return on investment (ROI) of 3-5 years, depending on the operating hours of the store. In the future, regarding the infrastructure of energy management systems, that can extend to finding some opportunity to save energy consumption in another system, for example, the refrigeration system of their convenience stores.

Research on jujube storage and preservation system based on Internet of Things (IoT)

With the surge in Xinjiang jujube production, there has been a concurrent increase in storage losses, posing an ongoing challenge for farmers to preserve the freshness of the jujubes. To tackle this issue, a novel storage and freshness control system for jujubes, integrating Internet of Things technology, has been developed. The control system is centered around a programmable logic controller (PLC) and utilizes an HMI as the interface for human–computer interaction. It is structured with distinct layers, including equipment, transportation, and application layers. By integrating with the Internet of Things cloud platform, data transmission between the device layer and the cloud server is ensured through the use of the TCP/IP protocol over a 4G network. A mobile app enables remote control functionality for monitoring and managing the storage and preservation environment. Experimental results demonstrate the system's stable performance, enabling remote monitoring and convenient operation by management personnel during the jujube storage and preservation process. This contributes to enhancing automation levels in jujube storage, extending its storage cycle while maintaining quality standards, thus providing valuable insights into intelligent fruit and vegetable storage practices in Xinjiang.

PLCBlox: Using blockchain-based audit trails to generate secure PLC commands

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.

Design of Moringa Leaf Dryer Based on Programmable Logic Control Laboratory Scale

Moringa plants can be found in most tropical and subtropical climates. In some parts of Indonesia, the plant is called kelor or marungga. People need the tree as a source of protein, vitamins, minerals, and carbohydrates. The plant contains more than 90 nutrients, antioxidants, and 8 essential amino acids, called the ‘elixir tree’. The problem with this plant is that its leaves are easily damaged when picked. One way to avoid this is to perform a drying process. This Moringa dryer consists of an inner cabinet made of aluminum and an outer side made of zinc. The cabinet measures 40 cm long, 35 cm wide, and 35 cm high, has three shelves arranged horizontally, and each shelf can dry between 100 grams to 200 grams of moringa leaves simultaneously. In addition, it has a heater consisting of two 125-watt heaters and two upper and lower fans. In the first experiment, Moringa leaves were dried for 9 hours at 40°C, yielding 30g of dried leaves from 100g of fresh leaves. The second experiment lasted 6 hours at 50 °C, yielding 30 g of dried leaves from 100 g of fresh leaves. Finally, the leaves were dried for 3 hours at 60°C, yielding 30g of dried leaves from the initial 100g. These results also show a large weight loss at various temperatures and durations. After calculating the percentage of weight loss, it was found that the leaves lost 70% of their weight. Outseal PLC can automatically control the drying process according to the pre-programmed program. Temperature and humidity sensors maintain the nutritional quality and texture of the moringa leaves produced by controlling the dryer environment appropriately. This tool can be used easily by users without requiring high technical skills.

Experimental Study of a Puncture Warning System for a Jack-Up Offshore Platform

Abstract 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.

Decentralized System Synchronization among Collaborative Robots via 5G Technology.

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.

A PLC-Embedded Implementation of a Modified Takagi–Sugeno–Kang-Based MPC to Control a Pressure Swing Adsorption Process

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.

The application of the Bayesian linear regression model to optimize the maintenance of a programmable logic controller

In this paper, an optimization approach, which is based on the Bayesian Linear Inference (BLI) model, has been proposed for the maintenance of Programmable Logic Controllers (PLCs). The BLI model, which is implemented using historical data, incorporates maintenance indicators like the number of failures (NF), total downtime (TD), total unexpected intervals (TUI), mean time to repair (MTTR) and mean time between failures (MTBF). It offers a probabilistic framework for determining the influence of each predictor variable on PLC maintenance. The model produces posterior means, credible intervals, and standard deviations, which provide insights into the magnitude and uncertainty of these relationships. The results from the study show that factors like NF and TD are influenced by the magnitude and direction of the maintenance levels. Also, the R-squared score (0.85) also indicates how much of the variability in maintenance in the system. From the results obtained, the study can conclude that the BLI model can optimize PLC maintenance procedures by identifying essential components and their contributions. Also, it is able to estimate future maintenance requirements and helps with resource allocation and process optimization decisions.