Julsereewong, Amphawan
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Preferred name
Julsereewong, Amphawan
Alternative Name
Julsereewong, A.
Main Affiliation
Email
amphawan.ju@kmitl.ac.th
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Item type:Publication, Toward Interoperable SCADA: Node-RED-Based Edge Integration of Heterogeneous PLCs and IoT Devices(2025-01-01); ; This article presents a novel approach to supervisory control and data acquisition (SCADA) design that emphasizes interoperability through edge integration, using Node-RED as an open-source programming platform deployed on an industrial edge computer. The proposed architecture integrates multiple-vendor programmable logic controllers (PLCs) and an Internet of Things (IoT) gateway at the edge layer to enable seamless interoperability across local control systems. A lab-scale case study demonstrates the feasibility of this integration, featuring four remote terminal units (RTUs) communicating concurrently with a SIMATIC IPC327G edge computer via standard protocols. Specifically, a SIMATIC S7-1200 (RTU-1), MELSEC iQ-F FX5U (RTU-2), and CompactLogix 1769-L30ER (RTU-3) control an object sorting station, a parcel sorting station, a water temperature control station, respectively, while a UG65 gateway (RTU-4) enables bidirectional data transfer between the edge computer and LoRaWAN devices. The inclusion of InfluxDB alongside Node-RED supports data logging and reporting functionalities. By distributing protocol translation, data processing, and shop-floor supervisory control to the edge layer, the architecture achieves real-time, vendor-neutral data orchestration while maintaining compatibility with a higher-level SCADA master terminal unit (MTU) or cloud system. Experimental results from both functional and performance testing confirm the system’s viability as a cost-effective, vendor-agnostic solution for modern industrial automation, offering a scalable model for multi-protocol industrial environments. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, PFD analysis of LNG fuel gas supply system for improving combined-cycle power plant safety(2022-04-01); ; In order to determine an unavailability of each safety function subsystem that influences on an overall unavailability of entire protection system for safety analysis and enhancement, a modeling is required. This article presents a safety system modeling to evaluate the detected–undetected failures and repairable behaviors of each safety function subsystem in the studied fuel gas supply system (FGSS), which is utilized in a combined-cycle power plant. The proposed modeling deals with probability of failure on demand (PFD) analysis for calculation of the unavailability of three safety function subsystems, which are gas analytical subsystem (GAS), fuel gas controller subsystem (FGC), and shutdown valve subsystem (SDV). The interested GAS includes two different groups of gas analyzers in 2-out-of-2 (2oo2) voting scheme, when using three gas analyzers in each group in 2-out-of-3 and diagnosis (2oo3D) voting scheme. The interested FGC consists of two controllers in 2oo2 voting scheme, when the structure of each controller is in 1-out-of-1 and diagnosis (1oo1D) voting scheme. The interested SDV consists of two shutdown valves, which are installed in 1-out-of-2 (1oo2) voting scheme. The reliability block diagram is utilized for representative of combination of all three safety function subsystems, and the fault tree is utilized for calculation of PFD unavailability of each safety function subsystem. As the results obtained from the proposed safety function modeling, the influence of three safety function subsystems on the total unavailability of the FGSS is described. In addition, an example of the proposed modeling application to provide two guidelines for effective engineering design and operation is also presented. The first is a guideline to implement an effective human machine interface (HMI) at an operator workstation for real-time monitoring of the studied FGSS in the presence of failures that can be automatically detected by device diagnosis. The latter is a guideline to maintain and troubleshoot the failures that can be revealed by proof test. Based on diagnostic information and proof test results, operation and maintenance savings can be achieved.