This study explores the key role of Programmable Logic Controllers (PLCs) in strengthening safety in modern industrial automation, focusing on glass manufacturing. It assesses PLC setup in furnace control, gob distribution, and plunger-shear areas, stressing real-time fault detection, reliability, and alignment with standards like IEC 61508 and ISO 13849. Over 45 days of field tests, results showed fast response times (e.g., 100 ms for emergency stops), strong diagnostic coverage, and good operator use of Human-Machine Interfaces (HMIs). Case studies proved PLCs\' skill in keeping sync, avoiding equipment harm, and logging faults well.
The research deeply reviews system response times, fault logic, and operator interface ease. Findings show that current PLC safety designs, matched to IEC 61508 and ISO 13849, reach high diagnostic reach, few false stops, and quick recovery after halts.
It covers hardware-software links, backup plans, and compliance needs for safe work. Results prove PLCs are more than automation tools; they core industrial safety, enabling preventive control, adaptive fault handling, and smooth fit with Industry 4.0. This gives useful insights for automation experts building or improving safety systems, showing how PLCs make safer, smarter industries.
Introduction
This study explores how Programmable Logic Controllers (PLCs)—especially Safety PLCs—enhance safety, reliability, and automation in high-risk industrial environments, with a focus on glass manufacturing (furnaces, gob distributors, and plungers). Originating in the 1960s, PLCs evolved from relay replacements to intelligent, safety-certified control systems compliant with standards like IEC 61508, IEC 61511, and ISO 13849.
???? Background:
PLCs have revolutionized process control by offering flexibility, real-time responsiveness, and low maintenance. In hazardous industries, such as glass production where temperatures exceed 1400°C, safety is critical. PLCs are now central not only to automation but also to safety-critical functions—handling faults, managing shutdowns, and ensuring compliance with Safety Integrity Levels (SILs).
?? Problem Statement:
Despite their benefits, many plants still rely on outdated relay systems or basic PLCs lacking redundancy and fault tolerance. Common issues include:
Poor or delayed integration of safety features
Misconfiguration of Safety PLCs
Weak compliance with safety standards
Poor HMI design
Rising cybersecurity threats due to IIoT connectivity
Inadequate real-time response during emergencies
???? Study Objectives:
Evaluate the role of Safety PLCs in high-risk operations.
Assess compliance with safety standards like IEC 61508 and ISO 13849.
Propose optimized PLC design and logic blocks for safety-critical tasks.
Examine real-time response, interlock performance, and HMI effectiveness.
Investigate cyber vulnerabilities and future AI integration potential.
???? Methodology:
Field-based case study conducted over 45 days in a mid-sized glass plant.
???? Focus Zones:
Furnace: Overfill, overtemp hazards
Gob Distributor: Alignment and spill risk
Plunger-Shear: Sync timing and mechanical failure risks
???? Data Collection:
Quantitative: Event logs, cycle times, response times (e.g., E-stops <200 ms), system uptime (99.72%), using PLCs and SCADA.
PLC: Modular (e.g., Siemens S7-1200), <10 ms scan cycles
I/O Modules: Analog/digital sensors (e.g., AI0 for furnace levels), actuators
HMI: Ethernet/RS-485, real-time alarms, user feedback
SCADA: Logging, diagnostics, analytics
Network: VLAN segmentation, fieldbus communication, limited ports for cyber safety
???? Safety Logic Design:
Ladder Logic (IEC 61131-3) with:
Fail-safe mechanisms
Dual input validation
Interlocked zones
Watchdog timers
Examples:
Furnace overfill → gob stop
Gob misalignment → isolate zone
Plunger desync → motion block
Reset Logic: Manual, post-checks, with HMI-controlled restart
Response Time Targets: <200 ms for critical functions
???? Hazard Analysis (HAZID):
Hazard
Zone
Consequence
Overtemp
Furnace
Burn risk
Misalign
Gob
Spill
Desync
Plunger
Crush
Risk matrix (5×5): Used to compute Risk Priority Number (RPN)
E-stop function required SIL 3 integrity
???? Key Results:
Furnace:
Overfill shutdown in 180 ms
99.72% system uptime
Gob Zone:
Misalignment trigger held system in 160 ms
Plunger-Shear:
1.2 s cycle time
Fault response time: 200 ms
0% false trips in simulation
???? Significance of the Study:
Demonstrates real-world deployment of Safety PLCs in hazardous manufacturing
Provides modular logic templates and design frameworks
Reinforces importance of standard compliance, HMI usability, and cybersecurity
Supports future integration of AI/ML for predictive fault detection
???? Future Implications:
AI integration for predictive safety
Wider IIoT and SCADA synergy with secure communication
Standardized design templates for reusable safety blocks
Deployment in other high-risk sectors (e.g., oil & gas, chemicals)
Conclusion
Thesis traces PLCs from relays to safety cores in glass. Key: Intro evolution, Lit gaps, Method design (incl. data collection), Results fast response (with original figures), Sim match.
Ch1: PLC growth, 4.0 risks.
Ch2: Integration issues [2].
Ch3: Logic, risks.
Ch4: 100 ms stops, cases.
Ch5: Sim validates.
PLCs beat old in speed, HMI aids; document figures provided key visual proof.
References
[1] Dhameliya, N. (2023). American Digits, 1(1), 33-48.
[2] Hajda, J., et al. (2021). Applied Sciences, 11(21), 9785.
[3] Sharma, R. (2024). Int J Smart Sustain Intell Comput, 1(2), 1-20.
[4] Channi, H. K., et al. (2024). Comput Intell Tech Mechatron, 185-209.
[5] De Rosa, F., et al. (2017). Reliab Eng Syst Saf, 165, 124-133.
[6] Nankya, M., et al. (2023). Sensors, 23(21), 8840.
[7] Sehr, M. A., et al. (2020). IEEE Trans Ind Inf, 17(5), 3523-3533.
[8] Nelson, B., et al. (2005). Educ Technol, 21-28.
[9] Cavaliere, P. (2023). Water Electrolysis, pp. 729-791.
[10] Etz, D. (2024). Doctoral diss, TU Wien.
[11] Serhane, A. (2022). Doctoral diss, U Wollongong.
[12] De Rosa, F., et al. (2017). As above.
[13] Smith, D., & Simpson, K. (2004). Functional Safety. Routledge.
[14] Hubert, M. (2019). Springer Handb Glass, pp. 1195-1231.
[15] Olhager, J., et al. (2015). Int J Phys Distrib Logist Manag, 45(1/2), 138-158.
[16] Brooks, C. J., & Craig, P. A. (2022). Pract Ind Cybersecurity. Wiley.
[17] Markowski, A. S., et al. (2009). J Loss Prev Process Ind, 22(6), 695-702.
[18] Wang, K., Chen, J., & Song, Z. (2018).
[19] Vogel-Heuser, B., et al. (2016). J Softw Eng Appl, 9(01), 1.
[20] Kumar, N., & Lee, S. C. (2022). Technol Forecast Soc Change, 174, 121284.
[21] Thatikonda, K. (2023). Integr Electr Syst Intell Comput. Academic Guru.
[22] Goel, P., et al. (2017). J Loss Prev Process Ind, 50, 23-36.
[23] Channi, H. K., et al. (2024). As above.
[24] Parry, I. P., & Smith, P. R. (2002). Meas Control, 35(10), 302-309.
[25] Knapp, E. D. (2024). Ind Netw Secur. Elsevier.
[26] Wang, H., et al. (2024). Electron, 13(4), 802.