IoT Based Model for Data Analytics of KPI Platform in Continuous Process Industry

Jeeva Jose, Vijo Mathew

Abstract


Internet of Things (IoT) is gaining momentum now a days to real time operational environment. The related technologies of IoT is converging to the main stream of industrial applications and replacing the conventional models of data acquisition, analysis, visualization and control in continuous manufacturing process industries. In this paper, we are proposing an IoT based model platform for acquiring various data that is generated in a continuous process manufacturing plant. This includes data from mobile devices and ERP systems as well. This is analyzed using machine learning and artificial intelligence technologies which leads to visualization of Key Performance Indicators (KPIs). It can be displayed on plant level as well as head office level in static and mobile devices. Control instructions can also be given from static devices as well as from mobile devices. Along with proposed platform concept, a prototype is also developed for cement manufacturing plant which is a core engineering continuous process manufacturing industry. The general KPIs in cement plants are explained and the KPIs generated in visualizing devices by the prototype platform are also provided in this paper. 


Full Text:

PDF

References


C. R. Sekhar, P. Hema, and C. E. Reddy (2018). Equipment Effectiveness Improvement in a Continuous Process Industry. International Journal of Research and Analytical Reviews (IJRAR), vol. 5, pp. 134-142.

M. G. Hudedmani, R. M. Umayal, S. K. Kabberalli and R. Hittalamani (2017). Programmable Logic Controller (PLC) in Automation. Advanced Journal of Graduate Research, vol. 2, pp. 37-45.

https://doi.org/10.21467/ajgr.2.1.37-45

R. Kirubashankar, K. Krishnamurthy and J. Indra (2009). Remote monitoring system for distributed control of industrial plant process. Journal of Scientific & Industrial Research, vol. 68, pp.858-860.

M. M. Lashin (2014). Different Applications of Programmable Logic Controller (PLC). International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), vol. 4, pp.27-32. https://doi.org/10.5121/ijcseit.2014.4103

M. Dahm and A. Mathur (1990). Automation in the food processing industry: distributed control systems. Butterworth & Co. (Publishers) Ltd. Pp. 32-35.

K. Stouffer, J. Falco and K. Kent (2006). Guide to Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems Security. NIST Special Publication 800-82, Intelligent Systems Division, Gaithersburg.

S. Li, L. D. Xu and S. Zhao (2015). The internet of things: a survey. Inf Syst Front, Springer, vol. 17, pp. 243-259.

https://doi.org/10.1007/s10796-014-9492-7

A. Sadeghi, C. Wachsmann, M. Waidner (2015). Security and Privacy Challenges in Industrial Internet of Things. DAC ’15, ACM, San Francisco, CA, USA, pp. 7-11.

http://dx.doi.org/10.1145/2744769.2747942

M. Short and F. Abugchem (2017). A Microcontroller-Based Adaptive Model Predictive Control Platform for Process Control Applications. Electronics, vol. 6, pp.1-17.

https://doi.org/10.3390/electronics6040088

J. C. Kabugoa, S. L. Jounelaa, R. Schiemannb and C. Binder (2020): Industry 4.0 based process data analytics platform: A waste-to-energy plant case study. International Journal of Electrical Power and Energy Systems, vol. 115.

https://doi.org/10.1016/j.ijepes.2019.105508

Z. Ge, Z. Song, S. X. Ding and B. Huang (2017). Data Mining and Analytics in the Process Industry: The Role of Machine Learning. IEEE Access, vol. 5, pp. 20590-20616.

https://doi.org/10.1109/ACCESS.2017.2756872

E. Goldin, D. Feldman, G. Georgoulas, M. Castano and G. Nikolakopoulos (2017). Cloud Computing for Big Data Analytics in the Process Control Industry. Proceedings of 25th Mediterranean Conference on Control and Automation (MED), IEEE, Valletta, Malta.

https://doi.org/10.1109/MED.2017.7984310

M. H. Rehman, I. Yaqoob, K. Salah, M. Imran, P. P Jayaraman and C. Perera (2019). The Role of Big Data Analytics in Industrial Internet of Things. Future Generation Computer Systems, vol. 99, pp. 247-259.

https://doi.org/10.1016/j.future.2019.04.020

L.V. Zhihan, L. Qiao, S. Verma and Kavita (2021). AI-enabled IoT-Edge Data Analytics for Connected Living. ACM Transactions on Internet Technology, vol. 21, pp. 1-20.

https://doi.org/10.1145/3421510

D. Reguera-Bakhache, I. Garitano, R. Uribeetxeberria, C. Cernuda and U. Zurutuza (2020). Data-Driven Industrial Human-Machine Interface Temporal Adaptation for Process Optimization. Proceedings of 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, Vienna, Austria.

https://doi.org/10.1109/ETFA46521.2020.9211930

G.Prashanta (2020). Case Study: Industrial Automation Using PLC. Iconic Research and Engineering Journals, vol. 4, pp. 52-55.

S. Thalmann, J. Mangler, T. Schreck, C. Huemer, M. Streit, F. Pauker, G. Weichhart, S. Schulte, C. Kittl, C. Pollak, M. Vukovic, G. Kappel, M. Gashi, S. Rinderle-Ma, J. Suschnigg, N. Jekic, S. Lindstaedt (2018). Data Analytics for Industrial Process Improvement A Vision Paper. Proceedings of the IEEE 20th Conference on Business Informatics, IEEE, Vienna, Austria, pp. 92-96.

https://doi.org/10.1109/CBI.2018.10051

S. D. Anton, D. Fraunholz, C. Lipps, F. Pohl, M. Zimmermann and H. D. Schotten (2017). Two decades of SCADA exploitation: A brief history. Proceedings of the 2017 IEEE Conference on Application, Information and Network Security (AINS), IEEE, Miri, Malaysia, pp. 98-104.

https://doi.org/10.1109/AINS.2017.8270432

P. Samuel, V. R. Alexandru, M. Alexandru, Z. B. Constantin (2020). Architectural Issues in Implementing a Distributed Control System for an Industry 4.0 Prototype. Proceedings of 15th International Conference on Development and Application Systems, IEEE, Suceava, Romania, pp. 56-59.

https://doi.org/10.1109/DAS49615.2020.9108924

M. B. Yassein, M. Q. Shatnawi, S. Aljwarneh and R. Al-Hatmi (2017). Internet of Things: Survey and open issues of MQTT protocol. Proceedings of the International Conference on Engineering & MIS (ICEMIS), Monastir, Tunisia, pp. 1-6.

https://doi.org/10.1109/ICEMIS.2017.8273112

T. Yokotani, S. Ohno, H. Mukai and K. Ishibashi (2021). IoT Platform with Distributed Brokers on MQTT. International Journal of Future Computer and Communication, vol. 10, pp. 7-12.

https://doi.org/10.18178/ijfcc.2021.10.1.572

H. Chien, Y. Chen, G. Qiu, J. F. Liao, R. Hung, P. Lin, X. Kou, M. Chiang and C. Su (2020). A MQTT-API-compatible IoT security-enhanced platform. Int. J. Sensor Networks, vol. 32, pp. 54-68.

https://dx.doi.org/10.1504/IJSNET.2020.104463

H. Bauer, S. Hoppner, C. Iatrou, Z. Charania, S. Hartmann, S. Rehman, A.Dixius, G. Ellguth, D. Walter, J. Uhlig, F. Neumarker, M. Berthel, M. Stolba, F.Kelber, L. Urbas and C. Mayr (2021). Hardware Implementation of an OPC UA Server for Industrial Field Devices. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 29.

https://doi.ieeecomputersociety.org/10.1109/TVLSI.2021.3117401

M. Younan, E. H. Houssein, M. Elhoseny, A. A. Ali (2019). Challenges and recommended technologies for the industrial internet of things: A comprehensive review. Measurement, vol. 151 http://dx.doi.org/10.1016/j.measurement.2019.107198

N. Mehdiyev, A. Emrich, B. Stahmer, P. Fettke and P. Loos (2017). iPRODICT – Intelligent Process Prediction based on Big Data Analytics. Proceedings of Business Process Management (BPM-17) Industry Track, Barcelona, Spain.

I. Ahmed, S. Obermeier, S. Sudhakaran and V. Roussev (2017). Programmable Logic Controller Forensics. IEEE Security & Privacy, vol. 15, pp. 18-24. https://doi.ieeecomputersociety.org/10.1109/MSP.2017.4251102

C. Rameback (2003). Process automation systems-history and future. Proceedings of 2003 IEEE Conference on Emerging Technologies and Factory Automation, IEEE, Lisbon, Portugal.

https://doi.org/10.1109/ETFA.2003.1247680

D. R. Milivojevic, V. Despotovic, V. Tasic and M. Pavlov (2010). Process Control Program as an Element of Distributed Control System. Information Technology and Control, vol. 39. pp. 152-158.

J. M. Sardroud (2012). Influence of RFID technology on automated management of construction materials and components. Scientia Iranica, vol. 19(3), pp. 381-392.

http://dx.doi.org/10.1016/j.scient.2012.02.023

A. Akbari, S. Mirshahi and M. Hashemipour (2015). Application of RFID System for the Process Control of Distributed Manufacturing System. Proceedings of Canadian Conference on Electrical and Computer Engineering, IEEE, Halifax, NS, Canada.

https://doi.org/10.1109/CCECE.2015.7129325

L. G. Kurmi, S. D. Patil and M. L. Yadav (2014). NFC Based Library Automation using Smart Phone. International Journal of Engineering Research & Technology (IJERT), vol. 3. pp. 1648-1651.

C. Lesjak, T. Ruprechter, H. Bock, J. Haid and

E.Brenner (2014). Facilitating a Secured Status Data Acquisition from Industrial Equipment via NFC. International Journal of Internet Technology and Secured Transactions, vol. 3(3), pp.288 – 299. http://dx.doi.org/10.20533/jitst.2046.3723.2014.0037

T. Kurfess, C. Saldana, K. Saleeby and M. Parto-Dezfouli (2020). A Review of Modern Communication Technologies for Digital Manufacturing Processes in Industry 4.0. Journal of Manufacturing Science and Engineering, vol. 142. pp.

http://doi.org/10.1115/1.4048206

B. Dafflon, N. Moalla and Y. Ouzrout (2021). The challenges, approaches, and used techniques of CPS for manufacturing in Industry 4.0: a literature review. The International Journal of Advanced Manufacturing Technology, vol. 113, pp.2395–2412.

http://dx.doi.org/10.20533/jitst.2046.3723.2014.0037

D. Raposo, A. Rodrigues, S. Sinche, J. S. Silva and F. Boavida (2018). Industrial IoT Monitoring: Technologies and Architecture Proposal. Sensors, vol. 18(10), pp. 1-32.

http://dx.doi.org/10.3390/s18103568

S.S. Mahmood and P.Sharma (2019). Industrial Automation using Zigbee Communication Protocol. International Journal of Recent Technology and Engineering (IJRTE), vol.8, pp. 7240-7243.

http://dx.doi.org/10.35940/ijrte.C6294.098319

P. A. M. Devan, F. A. Hussin, R. Ibrahim, K. Bingi and F. A. Khanday (2021). A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control. Sensors, vol. 21(15), pp. 1-26.

https://doi.org/10.3390/s21154951

T. Hasegawa, H. Hayashi, T. Kitai, H. Sasajima (2011). Industrial Wireless Standardization - Scope and Implementation of ISA SP100 Standard. Proceedings of SICE Annual Conference, IEEE, Tokyo, Japan, pp. 2059-2064.

Y. N. Valadao, G. Kunzel, I. Muller and C. E. Pereira (2018). Industrial Wireless Automation: Overview and Evolution of WIA-PA. Proceedings of the International Federation of Automatic Control, Energy Procedia, Elsevier, pp. 175-180.

https://doi.org/10.1016/j.ifacol.2018.06.257

C. F. Lindberga, S.T. Tan, J.Y. Yan, F. Starfelt (2015). Key performance indicators improve industrial performance. Proceedings of the 7th International Conference on Applied Energy – ICAE2015, Energy Procedia, Elsevier, pp. 1785-1790.

https://doi.org/10.1016/j.egypro.2015.07.474

B. Galloway and G. P. Hancke (2012). Introduction to Industrial Control Networks. IEEE Communications Surveys & Tutorials, vol.15, pp. 860 – 880.

https://doi.org/10.1109/SURV.2012.071812.00124

L. Cattaneo, L. Fumagalli, M. Macchi and E. Negri (2018). Clarifying Data Analytics Concepts for Industrial Engineering. Proceedings of the International Federation of Automatic Control, Elsevier, pp. 820-825.

https://doi.org/10.1016/j.ifacol.2018.08.440

A. K. Y. Benhamidouche (2021). Prediction of Cement Fineness Using Machine Learning Approaches. PhD. Thesis, Faculty of Technology, University Mohamed Boudiaf - M’sila, People's Democratic Republic of Algeria.

D. N. Huntzinger and T. D. Eatmon (2008). A life-cycle assessment of Portland cement manufacturing: comparing the traditional process with alternative technologies. Journal of Cleaner Production, vol. 17. pp. 668-675.

https://doi.org/10.1016/j.jclepro.2008.04.007

A. Rahman, M.G. Rasul, M.M.K. Khan and S. Sharma (2013). Impact of alternative fuels on the cement manufacturing plant performance: an overview. Proceedings of the 5th BSME International Conference on Thermal Engineering, Elsevier, pp. 393-400.

https://doi.org/10.1016/j.proeng.2013.03.138

E. Amrina and A. L. Vilsi (2015). Key Performance Indicators for Sustainable Manufacturing Evaluation in Cement Industry. Proceedings of the 12th Global Conference on Sustainable Manufacturing, Elsevier, pp. 19-23.

https://doi.org/10.1016/j.procir.2014.07.173

J. P. John (2020). Parametric Studies of Cement Production Processes. Journal of Energy, vol. 2020, pp. 1-17.

https://doi.org/10.1155/2020/4289043

R. Feiz, J. Ammenberg, L.Baas, M. Eklund, A. Helgstrand and R. Marshall (2015). Improving the CO2 performance of cement, part I: Utilizing life-cycle assessment and key performance indicators to assess development within the cement industry. Journal of Cleaner Production, vol.98, pp.272-281.

http://dx.doi.org/10.1016/j.jclepro.2014.01.083

A. K. Mishra and A. Jha (2019). Quality Assessment of Sarbottam Cement of Nepal. International Journal of Operations Management and Services, vol. 9. pp. 1-22.

N.A. Madlool, R. Saidur, M.S. Hossain, N.A. and Rahim (2011). A critical review on energy use and savings in the cement industries. Renewable and Sustainable Energy Reviews, vol. 15. pp. 2042-2060. https://doi.org/10.1016/j.rser.2011.01.005




DOI: https://doi.org/10.31449/inf.v48i1.3826

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.