Perspective for smart factory in petrochemical industry

Computers and Chemical Engineering

Volumn 91, Issue , 2016, Pages 136-148

  Li, Defang ^a  

^a SINOPEC   (China)

Author keywords

Petrochemical industry; Process system engineering; Smart factory

Indexed keywords

CHEMICAL PLANTS; INDUSTRIAL RESEARCH; LIFE CYCLE; MANUFACTURE; PROBLEM SOLVING; SUPPLY CHAINS;

DISRUPTIVE TECHNOLOGY; FACTORY DEVELOPMENT; INDUSTRIAL PROCESSS; LIFE-CYCLE MANAGEMENT; MANUFACTURING METHODOLOGY; PETROCHEMICAL INDUSTRY; PROCESS SYSTEM ENGINEERING; TECHNOLOGY ROADMAPS;

PETROCHEMICALS;

EID: 84962019236     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2016.03.006     Document Type: Article

References (56)

1. Accenture, Accenture technology vision. 2015 https://www.accenture.com/us-en/it-technology-trends-2015.aspx.
2. Aguilar, J., An artificial immune system for fault detection. Proceedings of IEA/AIE, 2004, 219–228.
3. Barbosa-Póvoa, A.P., Progresses and challenges in process industry supply chains optimization. Curr Opin Chem Eng 1:4 (2012), 446–452.
4. Barbosa-Póvoa, A.P., Process supply chains management – where are we? Where to go next?. Process Energy Syst Eng, 2, 2014, 23.
5. Bar-Cohen, Y., Hanson, D., The coming robot revolution. 2015, China Machine Press, Beijing.
6. Bertsimas, D., Brown, D.B., Caramanis, C., Theory and applications of robust optimization. SIAM Rev 53:3 (2011), 464–501.
7. Birge, J.R., Louveaux, F.V., Introduction to stochastic programming. 1997, Springer.
8. Block, F., Horn, M.S., Phillips, B.C., Diamond, J., Evans, E.M., Shen, C., The deeptree exhibit: visualizing the tree of life to facilitate informal learning. IEEE Trans Visual Comput Graph 18:12 (2012), 2789–2798.
9. Chia, R., Hou, Z., Huang, B., Jin, S., A unified data-driven design framework of optimality-based generalized iterative learning control. Comput Chem Eng 77 (2015), 10–23.
10. Chong, F.K., Eljack, F.T., Atilhan, M., Foo, D.C.Y., Chemmangattuvalappil, N.G., Ionic liquid design for enhanced carbon dioxide capture-a computer aided molecular design approach. Chem Eng Trans 39 (2014), 8–253.
11. Chopra, S., Meindl, P., Supply chain management: strategy, planning, and operation. 5th ed., 2012, Prentice Hall, Upper Saddle River, NJ, USA.
12. Christofides, P.D., Riccardo, S., de la Peña, D.M., Jinfeng, L., Distributed model predictive control: a tutorial review and future research direction. Comput Chem Eng 51 (2013), 21–41.
13. Chua, Y., You, F., Wassickb, J.M., Agarwal, A., Integrated planning and scheduling under production uncertainties: bi-level model formulation and hybrid solution method. Comput Chem Eng 72 (2015), 255–272.
14. Cóccola, M., Méndez, C.A., Dondo, R.G., A branch-and-price approach to evaluate the role of cross-docking operations in consolidated supply chains. Comput Chem Eng 80 (2015), 15–29.
15. Davis, J., Edgar, T., Porter, J., Bernaden, J., Sarli, M., Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Comput Chem Eng 47 (2012), 145–156.
16. Dondo, R., Cerdá, J., The heterogeneous vehicle routing and truck scheduling problem in a multi-door cross-dock system. Comput Chem Eng 76 (2015), 42–62.
17. Dong, C., Zhang, J., Wang, F., Xue, Y., From smart grid to energy internet: basic concept and research framework. Autom Electric Power Syst 38 (2014), 1–11.
18. Emun, F., Gadalla, M., Majozi, T., Boer, D., Integrated gasification combined cycle (IGCC) process simulation and optimization. Comput Chem Eng 34 (2010), 331–338.
19. Eslick, J.C., Ye, Q., Park, J., Topp, E.M., Spencer, P., Camarda, K.V., A computational molecular design framework for crosslinked polymer networks. Comput Chem Eng 33:5 (2009), 954–963.
20. Garcia, D.G., You, F., Multiobjective optimization of product and process networks: general modeling framework, efficient global optimization algorithm, and case studies on bioconversion. AIChE J 61:2 (2015), 530–554.
21. Garcia, D.J., You, F., Supply chain design and optimization: challenges and opportunities. Comput Chem Eng 81 (2015), 153–170.
22. Gartner, G., Hype cycle. 2015 http://www.gartner.com/newsroom/id/3114217.
23. Gebreslassie, B.H., Yao, Y., You, F., Design under uncertainty of hydrocarbon bio-refinery supply-chains: multi-objective stochastic programming models, decomposition algorithm, and a comparison between CVaR and downside risk. AIChE J 58:7 (2012), 2155–2179.
24. Graves, S.C., Willems, S.P., Optimizing the supply chain configuration for new products. Manage Sci 51:8 (2005), 1165–1180.
25. Grossmann, I.E., Advances in mathematical programming models for enterprise-wide optimization. Comput Chem Eng 47 (2012), 2–18.
26. Grossmann, I.E., Challenges in the application of mathematical programming in the enterprise-wide optimization of process industries. Theor Found Chem Eng 48:5 (2014), 555–573.
27. Harini, M., Adhikari, J., Rani, K.Y., A review on property estimation methods and computational schemes for rational solvent design: a focus on pharmaceuticals. Ind Eng Chem Res 52:21 (2013), 6869–6893.
28. He, Y., Geng, Z., Zhu, Q., Data driven soft sensor development for complex chemical processes using extreme learning machine. Chem Eng Res Des 2 (2015), 1–11.
29. Hoskins, J.C., Kaliyun, K.M., Himmelblau, D.M., Fault diagnosis in complex chemical plants using artificial neural networks. AIChE J 37:1 (1991), 137–141.
30. Isenberg, P., Fisher, D., Paul, S.A., Morris, M.R., Inkpen, K., Czerwinski, M., Co-located collaborative visual analytics around a tabletop display. IEEE Trans Visual Comput Graph 18:5 (2012), 689–702.
31. Jin, H., Chen, X., Yang, J., Wu, L., Adaptive soft sensor modeling framework based on just-in-time learning and kernel partial least squares regression for nonlinear multiphase batch processes. Comput Chem Eng 71 (2014), 77–93.
32. Kjøbsted Huusom, J., Challenges and opportunities in integration of design and control. Comput Chem Eng 81 (2015), 138–146.
33. Li, B., Zhang, L., Wang, S., Tao, F., Cao, J., Jiang, X., et al. Cloud manufacturing: a new service-oriented networked manufacturing model. Comput Integr Manuf Syst 16:1 (2010), 1–7.
34. Li, B., Zhang, L., Ren, L., Chai, X., Tao, F., Wang, Y., et al. Typical characteristics, technologies and applications of cloud manufacturing. Comput Integr Manuf Syst 18:7 (2012), 1345–1356.
35. Liu, Y., Chen, J., Integrated soft sensor using just-in-time support vector regression and probabilistic analysis for quality prediction of multi-grade processes. J. Process Control 23 (2013), 793–804.
36. Liu, S., Zhang, J., Liu, J., Economic MPC with terminal cost and application to oils and separation. 9th international symposium on advanced control of chemical processes, The International Federation of Automatic Control, June 7–10, Whistler, British Columbia, Canada, 2015.
37. MacGregor, J., Cinar, A., Monitoring, fault diagnosis, fault-tolerant control and optimization: data driven methods. Comput Chem Eng 47 (2012), 111–120.
38. Mafakheri, F., Nasiri, F., Modeling of biomass-to energy supply chain operations: applications, challenges and research directions. Energy Policy 67 (2014), 116–126.
39. Mitra, K., Gudi, R.D., Patwardhan, S.C., Sardar, G., Resiliency issues in integration of scheduling and control. Ind Eng Chem Res 49 (2010), 35–222.
40. Ottino, J.M., Chemical engineering in a complex world: grand challenges, vast opportunities. AIChE J 57:7 (2011), 1654–1668.
41. 2 capture solvents. Chem Eng Trans 39 (2014), 211–216.
42. Pavan, K.N., Arief, A., Karimi, I.A., Rajagopalan, S., From PSE to PSE2 – decision support for resilient enterprises. Comput Chem Eng 33:12 (2009), 1939–1949.
43. Pieters, B., Andrieux, G., Eloy, J.C., The impact of microtechnologies on chemical and pharmaceutical production processes. Chem Eng Technol 30:3 (2007), 407–409.
44. Rifkin, J., The third industrial revolution. 2012, China CITIC Press, Beijing.
45. Rivotti, P., Pistikopoulos, E.N., A dynamic programming based approach for explicit model predictive control of hybrid systems. Comput Chem Eng 72 (2015), 126–144.
46. Samudra, A., Sahinidis, N.V., Design of heat-transfer media components for retail food refrigeration. Ind Eng Chem Res 52:25 (2013), 8518–8526.
47. Sendle, U., Industry 4.0. 2014, China Machine Press, Beijing.
48. Shang, C., Yang, F., Huang, D., Liu, W., Data-driven soft sensor development based on deep learning technique. J. Process Control 24 (2014), 223–233.
49. Sharma, B., Ingalls, R.G., Jones, C.L., Khanchi, A., Biomass supply chain design and analysis: basis, overview, modeling, challenges, and future. Renew Sustain Energy Rev 24 (2013), 608–627.
50. SMLC, Smart process manufacturing an operations and technology roadmap. 2009 http://issuu.com/fiatech/docs/smartprocessmanufaturinganoperationsandtechnologyr (accessed 29.09.2015).
51. SMLC, Implementing 21st century smart manufacturing workshop summary report. 2011 https://smartmanufacturingcoalition.org/sites/default/files/implementing_21st_century_smart_manufacturing_report_2011_0.pdf (accessed 29.09.15).
52. Tabari, A., Ahmad, A., A semicontinuous approach for heterogeneous azeotropic distillation processes. Comput Chem Eng 73 (2015), 183–190.
53. Tao, F., Zhang, L., Guo, H., Luo, Y., Ren, L., Typical characteristics of cloud manufacturing and several key issues of cloud service composition. Comput Integr Manuf Syst 17 (2011), 477–486.
54. Terrazas-Moreno, S., Trotter, P.A., Grossmann, I.E., Temporal and spatial Lagrangean decompositions in multi-site, multi-period production planning problems with sequence-dependent changeovers. Comput Chem Eng 35:12 (2011), 2913–2928.
55. You, F., Grossmann, I.E., Wassick, J.M., Multisite capacity, production, and distribution planning with reactor modifications: MILP model, bi-level decomposition algorithm versus Lagrangean decomposition scheme. Ind Eng Chem Res 50:9 (2011), 4831–4849.
56. Zhu, Z., Wang, L., Ma, Y., Wang, W., Wang, Y., Separating an azeotropic mixture of toluene and ethanol via heat integration pressure swing distillation. Comput Chem Eng 76 (2015), 137–149.