Difficulties and Critical Success Factors for Implementing Industry 4.0 Technologies in the Continuous Process Chemical Industry

Paulo Henrique Amorim Santos; Izabel Cristina Zattar; Robson Seleme

doi:10.18535/ijsrm/v9i07.em04

Abstract

Industry 4.0 technologies may provide great improvements in the productive environment of continuous chemical industries. For example, the availability of real-time data management improves unit operations integration in process intensification. This improvements provide increased profitability, safety, sustainability and fault prediction capability. However, the sector presents specific obstacles in deploying 4.0 technologies because of its intrinsic complexity. This paper objective is to identify the sector-specific difficulties and Critical Success Factors when implementing Industry 4.0 technologies. A comprehensive systematic literature review with content analysis was carried out . Among the emerging necessities identified, the need to simplify complex systems and intensify operations is highlighted. The literature also converges on the urgency for developing reliable systems for adverse event management and assessment models for health, safety and environmental management. This paper is therefore both a tool for managers who seek information when implementing 4.0 technologies and researchers who may be looking for new topics in this area. Future research opportunities in the area are also presented.

Keywords

Industry 4.0Chemical IndustryContinuous ProcessesCritical Success FactorsSystematic Literature ReviewContent Analysis. 1. Introduction The fourth industrial revolution is much more than a trend for automation. It is a set of new manufacturing

References

S. Tigor, B. Tambunan, B. Christiananta, and D. Rachmawati, “A Conceptual Framework: Organizational Learning, Competencies, and Innovation in Indonesian Digital Startup,” doi: 10.18535/ijsrm/v7i10.em04.DOI ↗Google Scholar ↗
K. D. Thoben, S. A. Wiesner, and T. Wuest, “‘Industrie 4.0’ and smart manufacturing-a review of research issues and application examples,” Int. J. Autom. Technol., vol. 11, no. 1, pp. 4–16, 2017, doi: 10.20965/ijat.2017.p0004.DOI ↗Google Scholar ↗
Campos Martins, T. Edson, and E. Gonçalo, “INDUSTRIA 4.0 E BRASIL: POTENCIALIDADES E LIMITAÇÕES.”2018.Google Scholar ↗
T. H. Nguyen, M. T. Do, and Q. T. Vu, “Relationship between accounting benefits and ERP user satisfaction in the context of the fourth industrial revolution,” doi: 10.18535/ijsrm/v8i02.em08.DOI ↗Google Scholar ↗
M. Hermann, T. Pentek, and B. Otto, “Design principles for industrie 4.0 scenarios,” Proc. Annu. Hawaii Int. Conf. Syst. Sci., vol. 2016-March, pp. 3928–3937, 2016, doi: 10.1109/HICSS.2016.488.DOI ↗Google Scholar ↗
P. M. Piccione, “Realistic interplays between data science and chemical engineering in the first quarter of the 21st century, part 2: Dos and don’ts,” Chem. Eng. Res. Des., vol. 169, pp. 308–318, May 2021, doi: 10.1016/j.cherd.2021.03.012.DOI ↗Google Scholar ↗
H. Shevtsova, N. Shvets, and M. Kasatkina, “How Leading Global Chemical Companies Contribute to Industry 4.0,” Oct. 2020, doi: 10.1109/ITMS51158.2020.9259317.DOI ↗Google Scholar ↗
P. S. Aithal, “Information Communication and Computation Technology (ICCT) and its Contribution to Universal Technology for Societal Transformation by P. S. Aithal, Shubhrajyotsna Aithal :: SSRN,” 2021. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3768729 (accessed May 18, 2021).Google Scholar ↗
H. Kagermann, W. Wahlster, and J. Helbig, “Securing the future of German manufacturing industry: Recommendations for implementing the strategic initiative INDUSTRIE 4.0,” Final Rep. Ind. 4.0 Work. Gr., no. April, pp. 1–84, 2013.Google Scholar ↗
A. Kumar, M. Baldea, and T. F. Edgar, “Smart Manufacturing : Application To an Industrial Scale Steam-Methane Reformer,” pp. 2–5, 2015.Google Scholar ↗
M. Kotynkova, “Re-Industrialization of Europe: Industry4.0 and the Future of Work,” Eur. Sci. J., vol. 7881, no. April, pp. 249–256, 2017.Google Scholar ↗
L. Chiang, B. Lu, and I. Castillo, “Big data analytics in chemical engineering,” Annu. Rev. Chem. Biomol. Eng., vol. 8, no. August, pp. 63–85, 2017, doi: 10.1146/annurev-chembioeng-060816-101555.DOI ↗Google Scholar ↗
Y. Shu, L. Ming, F. Cheng, Z. Zhang, and J. Zhao, “Abnormal situation management: Challenges and opportunities in the big data era,” Comput. Chem. Eng., vol. 91, pp. 104–113, 2016, doi: 10.1016/j.compchemeng.2016.04.011.DOI ↗Google Scholar ↗
P. D. Christofides, J. F. Davis, N. H. El-Farra, D. Clark, K. R. D. Harris, and J. N. Gipson, “Smart plant operations: Vision, progress and challenges,” AIChE J., vol. 53, no. 11, pp. 2734–2741, Nov. 2007, doi: 10.1002/aic.11320.DOI ↗Google Scholar ↗
X. Ji, G. He, J. Xu, and Y. Guo, “Study on the mode of intelligent chemical industry based on cyber-physical system and its implementation,” Adv. Eng. Softw., vol. 99, pp. 18–26, 2016, doi: 10.1016/j.advengsoft.2016.04.010.DOI ↗Google Scholar ↗
R. Schlögl, “Catalysis 4.0,” ChemCatChem, vol. 9, no. 4, pp. 533–541, 2017, doi: 10.1002/cctc.201700026.DOI ↗Google Scholar ↗
J. Vinuto, “A amostragem em bola de neve na pesquisa qualitativa,” Tematicas, vol. 22, no. 44, pp. 203–220, Dec. 2014, doi: 10.20396/tematicas.v22i44.10977.DOI ↗Google Scholar ↗
B. E. Ydstie, “New vistas for process control: Integrating physics and communication networks,” AIChE J., vol. 48, no. 3, pp. 422–426, 2002, doi: 10.1002/aic.690480302.DOI ↗Google Scholar ↗
I. Grossmann, “Enterprise-wide optimization: A new frontier in process systems engineering,” AIChE J., vol. 51, no. 7, pp. 1846–1857, 2005, doi: 10.1002/aic.10617.DOI ↗Google Scholar ↗
J. Edgar, T; Davis, “SMART PROCESS MANUFACTURING – A VISION OF THE FUTURE,” 2009.Google Scholar ↗
J. Davis, T. Edgar, J. Porter, J. Bernaden, and M. Sarli, “Smart manufacturing, manufacturing intelligence and demand-dynamic performance,” Comput. Chem. Eng., vol. 47, pp. 145–156, 2012, doi: 10.1016/j.compchemeng.2012.06.037.DOI ↗Google Scholar ↗
J. Davis and D. Swink, “Smart Manufacturing as a Real-Time Networked Enterprise and a Market-Driven Innovation Platform Role/Viewpoint of panelist or organization,” Smart Manuf. Leadersh. Coalit., p. 5, 2012, [Online]. Available: https://smartmanufacturingcoalition.org/sites/default/files/smlc_sm-real-time_networked_enterprise_1.pdf.Google Scholar ↗
J. Davis, T. Edgar, R. Graybill, P. Korambath, B. Schott, D. Swink, J. Wang, and J. Wetzel, "Smart Manufacturing" Annual Review of Chemical and Biomolecular Engineering., vol. 6 n. 1, 2015, doi:10.1146/annurev-chembioeng-061114-123255DOI ↗Google Scholar ↗
Q. Hao, F. Zhang, Z. Liu, and L. Qin, “Design of Chemical Industrial Park Integrated Information Management Platform Based on Cloud Computing and IOT (The Internet of Things) Technologies,” Int. J. Smart Home, vol. 9, no. 4, pp. 35–46, Apr. 2015, doi: 10.14257/ijsh.2015.9.4.04.DOI ↗Google Scholar ↗
S. Van Thieven, A. Cliton, M. Mahto, and B. Sniderman, “chemicals industry Catalyzing transformation,” Deloitte Univeristy Press, pp. 1–24, 2016.Google Scholar ↗
K. Inaba, “The Common Integration The Group Operation of Petrochemical Complexes in Japan,” J. Innov. Sustain. RISUS ISSN 2179-3565, vol. 5, no. 1, p. 94, 2014, doi: 10.24212/2179-3565.2014v5i2p94-102.DOI ↗Google Scholar ↗
M. V. García, E. Irisarri, F. Pérez, E. Estévez, and M. Marcos, “An Open CPPS Automation Architecture based on IEC-61499 over OPC-UA for flexible manufacturing in Oil&Gas Industry,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 1231–1238, 2017, doi: 10.1016/j.ifacol.2017.08.347.DOI ↗Google Scholar ↗
C. Khare and S. T. Chin, “Potential for data analytics opportunities in SMART Chemical industry,” Chim. Oggi/Chemistry Today, vol. 35, no. 2, pp. 60–61, 2017.Google Scholar ↗
T. Wang, T. Li, Y. Xia, Z. Zhang, and S. Jin, “Risk Assessment and Online Forewarning of Oil & Gas Storage and Transportation Facilities Based on Data Mining,” Procedia Comput. Sci., vol. 112, pp. 1945–1953, 2017, doi: 10.1016/j.procs.2017.08.052.DOI ↗Google Scholar ↗
Q. P. He, J. Wang, D. Shah, and N. Vahdat, “Statistical Process Monitoring for IoT-Enabled Cybermanufacturing: Opportunities and Challenges,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 14946–14951, 2017, doi: 10.1016/j.ifacol.2017.08.2546.DOI ↗Google Scholar ↗
G. Martin, “Wireless networks improve refinery operation.,” Hydrocarb. Process., vol. 88, no. 3, pp. 29–32, 2009.Google Scholar ↗
R.-F. Chang, C.-W. Chang, K.-H. Tseng, C.-L. Chiang, W.-S. Kao, and W.-J. Chen, “Structural planning and implementation of a microprocessor-based human–machine interface in a steam-explosion process application,” Comput. Stand. Interfaces, vol. 33, no. 3, pp. 232–248, Mar. 2011, doi: 10.1016/j.csi.2010.05.001.DOI ↗Google Scholar ↗
S. Natarajan, K. Ghosh, and R. Srinivasan, “An ontology for distributed process supervision of large-scale chemical plants,” Comput. Chem. Eng., vol. 46, pp. 124–140, 2012, doi: 10.1016/j.compchemeng.2012.06.009.DOI ↗Google Scholar ↗
R. Rathinasabapathy, M. J. Elsass, J. R. Josephson, and J. F. Davis, “A smart manufacturing methodology for real time chemical process diagnosis using causal link assessment,” AIChE J., vol. 62, no. 9, pp. 3420–3431, Sep. 2016, doi: 10.1002/aic.15403.DOI ↗Google Scholar ↗
J.-P. Belaud, S. Negny, F. Dupros, D. Michéa, and B. Vautrin, “Collaborative simulation and scientific big data analysis: Illustration for sustainability in natural hazards management and chemical process engineering,” Comput. Ind., vol. 65, no. 3, pp. 521–535, Apr. 2014, doi: 10.1016/j.compind.2014.01.009.DOI ↗Google Scholar ↗
S. J. Qin, “Process data analytics in the era of big data,” AIChE J., vol. 60, no. 9, pp. 3092–3100, Sep. 2014, doi: 10.1002/aic.14523.DOI ↗Google Scholar ↗
R. Blackburn, K. Lurz, B. Priese, R. Göb, and I.-L. Darkow, “A predictive analytics approach for demand forecasting in the process industry,” Int. Trans. Oper. Res., vol. 22, no. 3, pp. 407–428, May 2015, doi: 10.1111/itor.12122.DOI ↗Google Scholar ↗
R. Squire and H. Song, “Cyber-physical systems opportunities in the chemical industry: A security and emergency management example,” Process Saf. Prog., vol. 33, no. 4, pp. 329–332, Dec. 2014, doi: 10.1002/prs.11676.DOI ↗Google Scholar ↗
Z. Zhang et al., “A strategy for enhancing the operational agility of petroleum refinery plant using case based fuzzy reasoning method,” Comput. Chem. Eng., vol.Google Scholar ↗

[refR-1] S. Tigor, B. Tambunan, B. Christiananta, and D. Rachmawati, “A Conceptual Framework: Organizational Learning, Competencies, and Innovation in Indonesian Digital Startup,” doi: 10.18535/ijsrm/v7i10.em04.DOI ↗Google Scholar ↗

[refR-2] K. D. Thoben, S. A. Wiesner, and T. Wuest, “‘Industrie 4.0’ and smart manufacturing-a review of research issues and application examples,” Int. J. Autom. Technol., vol. 11, no. 1, pp. 4–16, 2017, doi: 10.20965/ijat.2017.p0004.DOI ↗Google Scholar ↗

[refR-3] Campos Martins, T. Edson, and E. Gonçalo, “INDUSTRIA 4.0 E BRASIL: POTENCIALIDADES E LIMITAÇÕES.”2018.Google Scholar ↗

[refR-4] T. H. Nguyen, M. T. Do, and Q. T. Vu, “Relationship between accounting benefits and ERP user satisfaction in the context of the fourth industrial revolution,” doi: 10.18535/ijsrm/v8i02.em08.DOI ↗Google Scholar ↗

[refR-5] M. Hermann, T. Pentek, and B. Otto, “Design principles for industrie 4.0 scenarios,” Proc. Annu. Hawaii Int. Conf. Syst. Sci., vol. 2016-March, pp. 3928–3937, 2016, doi: 10.1109/HICSS.2016.488.DOI ↗Google Scholar ↗

[refR-6] P. M. Piccione, “Realistic interplays between data science and chemical engineering in the first quarter of the 21st century, part 2: Dos and don’ts,” Chem. Eng. Res. Des., vol. 169, pp. 308–318, May 2021, doi: 10.1016/j.cherd.2021.03.012.DOI ↗Google Scholar ↗

[refR-7] H. Shevtsova, N. Shvets, and M. Kasatkina, “How Leading Global Chemical Companies Contribute to Industry 4.0,” Oct. 2020, doi: 10.1109/ITMS51158.2020.9259317.DOI ↗Google Scholar ↗

[refR-8] P. S. Aithal, “Information Communication and Computation Technology (ICCT) and its Contribution to Universal Technology for Societal Transformation by P. S. Aithal, Shubhrajyotsna Aithal :: SSRN,” 2021. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3768729 (accessed May 18, 2021).Google Scholar ↗

[refR-9] H. Kagermann, W. Wahlster, and J. Helbig, “Securing the future of German manufacturing industry: Recommendations for implementing the strategic initiative INDUSTRIE 4.0,” Final Rep. Ind. 4.0 Work. Gr., no. April, pp. 1–84, 2013.Google Scholar ↗

[refR-10] A. Kumar, M. Baldea, and T. F. Edgar, “Smart Manufacturing : Application To an Industrial Scale Steam-Methane Reformer,” pp. 2–5, 2015.Google Scholar ↗

[refR-11] M. Kotynkova, “Re-Industrialization of Europe: Industry4.0 and the Future of Work,” Eur. Sci. J., vol. 7881, no. April, pp. 249–256, 2017.Google Scholar ↗

[refR-12] L. Chiang, B. Lu, and I. Castillo, “Big data analytics in chemical engineering,” Annu. Rev. Chem. Biomol. Eng., vol. 8, no. August, pp. 63–85, 2017, doi: 10.1146/annurev-chembioeng-060816-101555.DOI ↗Google Scholar ↗

[refR-13] Y. Shu, L. Ming, F. Cheng, Z. Zhang, and J. Zhao, “Abnormal situation management: Challenges and opportunities in the big data era,” Comput. Chem. Eng., vol. 91, pp. 104–113, 2016, doi: 10.1016/j.compchemeng.2016.04.011.DOI ↗Google Scholar ↗

[refR-14] P. D. Christofides, J. F. Davis, N. H. El-Farra, D. Clark, K. R. D. Harris, and J. N. Gipson, “Smart plant operations: Vision, progress and challenges,” AIChE J., vol. 53, no. 11, pp. 2734–2741, Nov. 2007, doi: 10.1002/aic.11320.DOI ↗Google Scholar ↗

[refR-15] X. Ji, G. He, J. Xu, and Y. Guo, “Study on the mode of intelligent chemical industry based on cyber-physical system and its implementation,” Adv. Eng. Softw., vol. 99, pp. 18–26, 2016, doi: 10.1016/j.advengsoft.2016.04.010.DOI ↗Google Scholar ↗

[refR-16] R. Schlögl, “Catalysis 4.0,” ChemCatChem, vol. 9, no. 4, pp. 533–541, 2017, doi: 10.1002/cctc.201700026.DOI ↗Google Scholar ↗

[refR-17] J. Vinuto, “A amostragem em bola de neve na pesquisa qualitativa,” Tematicas, vol. 22, no. 44, pp. 203–220, Dec. 2014, doi: 10.20396/tematicas.v22i44.10977.DOI ↗Google Scholar ↗

[refR-18] B. E. Ydstie, “New vistas for process control: Integrating physics and communication networks,” AIChE J., vol. 48, no. 3, pp. 422–426, 2002, doi: 10.1002/aic.690480302.DOI ↗Google Scholar ↗

[refR-19] I. Grossmann, “Enterprise-wide optimization: A new frontier in process systems engineering,” AIChE J., vol. 51, no. 7, pp. 1846–1857, 2005, doi: 10.1002/aic.10617.DOI ↗Google Scholar ↗

[refR-20] J. Edgar, T; Davis, “SMART PROCESS MANUFACTURING – A VISION OF THE FUTURE,” 2009.Google Scholar ↗

[refR-21] J. Davis, T. Edgar, J. Porter, J. Bernaden, and M. Sarli, “Smart manufacturing, manufacturing intelligence and demand-dynamic performance,” Comput. Chem. Eng., vol. 47, pp. 145–156, 2012, doi: 10.1016/j.compchemeng.2012.06.037.DOI ↗Google Scholar ↗

[refR-22] J. Davis and D. Swink, “Smart Manufacturing as a Real-Time Networked Enterprise and a Market-Driven Innovation Platform Role/Viewpoint of panelist or organization,” Smart Manuf. Leadersh. Coalit., p. 5, 2012, [Online]. Available: https://smartmanufacturingcoalition.org/sites/default/files/smlc_sm-real-time_networked_enterprise_1.pdf.Google Scholar ↗

[refR-23] J. Davis, T. Edgar, R. Graybill, P. Korambath, B. Schott, D. Swink, J. Wang, and J. Wetzel, "Smart Manufacturing" Annual Review of Chemical and Biomolecular Engineering., vol. 6 n. 1, 2015, doi:10.1146/annurev-chembioeng-061114-123255DOI ↗Google Scholar ↗

[refR-24] Q. Hao, F. Zhang, Z. Liu, and L. Qin, “Design of Chemical Industrial Park Integrated Information Management Platform Based on Cloud Computing and IOT (The Internet of Things) Technologies,” Int. J. Smart Home, vol. 9, no. 4, pp. 35–46, Apr. 2015, doi: 10.14257/ijsh.2015.9.4.04.DOI ↗Google Scholar ↗

[refR-25] S. Van Thieven, A. Cliton, M. Mahto, and B. Sniderman, “chemicals industry Catalyzing transformation,” Deloitte Univeristy Press, pp. 1–24, 2016.Google Scholar ↗

[refR-26] K. Inaba, “The Common Integration The Group Operation of Petrochemical Complexes in Japan,” J. Innov. Sustain. RISUS ISSN 2179-3565, vol. 5, no. 1, p. 94, 2014, doi: 10.24212/2179-3565.2014v5i2p94-102.DOI ↗Google Scholar ↗

[refR-27] M. V. García, E. Irisarri, F. Pérez, E. Estévez, and M. Marcos, “An Open CPPS Automation Architecture based on IEC-61499 over OPC-UA for flexible manufacturing in Oil&Gas Industry,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 1231–1238, 2017, doi: 10.1016/j.ifacol.2017.08.347.DOI ↗Google Scholar ↗

[refR-28] C. Khare and S. T. Chin, “Potential for data analytics opportunities in SMART Chemical industry,” Chim. Oggi/Chemistry Today, vol. 35, no. 2, pp. 60–61, 2017.Google Scholar ↗

[refR-29] T. Wang, T. Li, Y. Xia, Z. Zhang, and S. Jin, “Risk Assessment and Online Forewarning of Oil & Gas Storage and Transportation Facilities Based on Data Mining,” Procedia Comput. Sci., vol. 112, pp. 1945–1953, 2017, doi: 10.1016/j.procs.2017.08.052.DOI ↗Google Scholar ↗

[refR-30] Q. P. He, J. Wang, D. Shah, and N. Vahdat, “Statistical Process Monitoring for IoT-Enabled Cybermanufacturing: Opportunities and Challenges,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 14946–14951, 2017, doi: 10.1016/j.ifacol.2017.08.2546.DOI ↗Google Scholar ↗

[refR-31] G. Martin, “Wireless networks improve refinery operation.,” Hydrocarb. Process., vol. 88, no. 3, pp. 29–32, 2009.Google Scholar ↗

[refR-32] R.-F. Chang, C.-W. Chang, K.-H. Tseng, C.-L. Chiang, W.-S. Kao, and W.-J. Chen, “Structural planning and implementation of a microprocessor-based human–machine interface in a steam-explosion process application,” Comput. Stand. Interfaces, vol. 33, no. 3, pp. 232–248, Mar. 2011, doi: 10.1016/j.csi.2010.05.001.DOI ↗Google Scholar ↗

[refR-33] S. Natarajan, K. Ghosh, and R. Srinivasan, “An ontology for distributed process supervision of large-scale chemical plants,” Comput. Chem. Eng., vol. 46, pp. 124–140, 2012, doi: 10.1016/j.compchemeng.2012.06.009.DOI ↗Google Scholar ↗

[refR-34] R. Rathinasabapathy, M. J. Elsass, J. R. Josephson, and J. F. Davis, “A smart manufacturing methodology for real time chemical process diagnosis using causal link assessment,” AIChE J., vol. 62, no. 9, pp. 3420–3431, Sep. 2016, doi: 10.1002/aic.15403.DOI ↗Google Scholar ↗

[refR-35] J.-P. Belaud, S. Negny, F. Dupros, D. Michéa, and B. Vautrin, “Collaborative simulation and scientific big data analysis: Illustration for sustainability in natural hazards management and chemical process engineering,” Comput. Ind., vol. 65, no. 3, pp. 521–535, Apr. 2014, doi: 10.1016/j.compind.2014.01.009.DOI ↗Google Scholar ↗

[refR-36] S. J. Qin, “Process data analytics in the era of big data,” AIChE J., vol. 60, no. 9, pp. 3092–3100, Sep. 2014, doi: 10.1002/aic.14523.DOI ↗Google Scholar ↗

[refR-37] R. Blackburn, K. Lurz, B. Priese, R. Göb, and I.-L. Darkow, “A predictive analytics approach for demand forecasting in the process industry,” Int. Trans. Oper. Res., vol. 22, no. 3, pp. 407–428, May 2015, doi: 10.1111/itor.12122.DOI ↗Google Scholar ↗

[refR-38] R. Squire and H. Song, “Cyber-physical systems opportunities in the chemical industry: A security and emergency management example,” Process Saf. Prog., vol. 33, no. 4, pp. 329–332, Dec. 2014, doi: 10.1002/prs.11676.DOI ↗Google Scholar ↗

[refR-39] Z. Zhang et al., “A strategy for enhancing the operational agility of petroleum refinery plant using case based fuzzy reasoning method,” Comput. Chem. Eng., vol.Google Scholar ↗