Big data analytics opportunities for applications in process engineering

References

Abbasi, A., Sarker, S., and Chiang, R.H. (2016). Big data research in information systems: toward an inclusive research agenda. J. Assoc. Inf. Syst. 17: 1–32, https://doi.org/10.17705/1jais.00423.Search in Google Scholar

Abdi, H. (2010). Partial least squares regression and projection on latent structure regression (PLS Regression). Wiley Interdiscip. Rev. Comput. Stat. 2: 97–106, https://doi.org/10.1002/wics.51.Search in Google Scholar

Abonyi, J. (2010). Process development based on model mining and experiment design techniques. In: IEEE 8th International Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, pp. 171–176.10.1109/SAMI.2010.5423743Search in Google Scholar

Abonyi, J., Farsang, B., and Kulcsar, T. (2014). Data-driven development and maintenance of soft-sensors. In: IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, pp. 239–244.10.1109/SAMI.2014.6822414Search in Google Scholar

Abonyi, J., Kulcsar, T., Balaton, M., and Nagy, L. (2013). Historical process data based energy monitoring-model based time-series segmentation to determine target values. Chem. Eng. Trans. 35: 931–936, https://doi.org/10.3303/CET1335155.Search in Google Scholar

Ahmed, E., Yaqoob, I., Hashem, I.A.T., Khan, I., Ahmed, A.I.A., Imran, M., and Vasilakos, A.V. (2017). The role of big data analytics in Internet of Things. Comput. Network. 129: 459–471, https://doi.org/10.1016/j.comnet.2017.06.013.Search in Google Scholar

Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., and Ayyash, M. (2015). Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17: 2347–2376, https://doi.org/10.1109/COMST.2015.2444095.Search in Google Scholar

Al-Sai, Z.A. and Abdullah, R. (2019). Big data impacts and challenges: a review. In: IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). IEEE, pp. 150–155.10.1109/JEEIT.2019.8717484Search in Google Scholar

Al-Shiakhli, S. (2019). Big data analytics: a literature review perspective, Master thesis. Luleå: Luleå University of Technology.Search in Google Scholar

Al-Thobiani, F. and Ball, A. (2014). An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks. Expert Syst. Appl. 41: 4113–4122, https://doi.org/10.1016/j.eswa.2013.12.026.Search in Google Scholar

Ansari, A., Mohaghegh, S.D., Shahnam, M., and Dietiker, J.F. (2019). Modeling average pressure and volume fraction of a fluidized bed using data-driven smart proxy. Fluids 4: 123, https://doi.org/10.3390/fluids4030123.Search in Google Scholar

Anuradha, J. (2015). A brief introduction on Big Data 5Vs characteristics and Hadoop technology. Procedia Comput. Sci. 48: 319–324, https://doi.org/10.1016/j.procs.2015.04.188.Search in Google Scholar

Atieno, O.P. (2009). An analysis of the strengths and limitation of qualitative and quantitative research paradigms. Problems of Education in the 21st Century 13: 13–18, http://oaji.net/articles/2014/457-1393665925.pdf.Search in Google Scholar

Atzori, L., Iera, A., and Morabito, G. (2010). The internet of things: a survey. Comput. Network. 54: 2787–2805, https://doi.org/10.1016/j.comnet.2010.05.010.Search in Google Scholar

Auerbach, C. and Silverstein, L.B. (2003). Qualitative data: an introduction to coding and analysis, Vol. 21. NYU Press.Search in Google Scholar

Ayers, R. (2017). Four things to know about big data in chemical engineering, Available at: https://www.aiche.org/chenected/2017/08/four-things-know-about-big-data-chemical-engineering.Search in Google Scholar

Bachnas, A.A., Tóth, R., Ludlage, J.H.A., and Mesbah, A. (2014). A review on data-driven linear parameter-varying modeling approaches: a high-purity distillation column case study. J. Process Control 24: 272–285, https://doi.org/10.1016/j.jprocont.2014.01.015.Search in Google Scholar

Bagga, S. and Sharma, A. (2018). Big data and its challenges: a review. In: IEEE 4th International Conference on Computing Sciences (ICCS), pp. 183–187.10.1109/ICCS.2018.00037Search in Google Scholar

Bai, X., Lu, G., Hossain, M.M., Szuhánszki, J., Daood, S.S., Nimmo, W., and Pourkashanian, M. (2017). Multi-mode combustion process monitoring on a pulverised fuel combustion test facility based on flame imaging and random weight network techniques. Fuel 202: 656–664, https://doi.org/10.1016/j.fuel.2017.03.091.Search in Google Scholar

Bailey, M.P. (2019). Artificial intelligence: a new reality for chemical engineers, Available at: https://www.chemengonline.com/artificial-intelligence-new-reality-chemical-engineers/?printmode=1.Search in Google Scholar

Balasko, B. and Abonyi, J. (2007). What happens to process data in chemical industry? From source to applications–an overview. Hungar. J. Ind. Chem. 35: 75–84, https://doi.org/10.1515/133/133.Search in Google Scholar

Beck, D.A., Carothers, J.M., Subramanian, V.R., and Pfaendtner, J. (2016a). Data science: accelerating innovation and discovery in chemical engineering. AIChE J. 62: 1402–1416, https://doi.org/10.1002/aic.15192.Search in Google Scholar

Beck, D., Pfaendtner, J., Carothers, J., and Subramanian, V. (2016b). Data science for chemical engineers. AIChE J.: 1402–1416, Available at: https://www.aiche.org/resources/publications/cep/2017/february/data-science-chemical-engineers.10.1002/aic.15192Search in Google Scholar

Belhadi, A., Zkik, K., Cherrafi, A., Sha’ri, Y.M., and El fezazi, S. (2019). Understanding the capabilities of Big Data Analytics for manufacturing process: insights from literature review and multiple case study. Comput. Ind. Eng. 137: 106099, https://doi.org/10.1016/j.cie.2019.106099.Search in Google Scholar

Benjelloun, F.Z., Lahcen, A.A., and Belfkih, S. (2015). An overview of big data opportunities, applications and tools. In: IEEE intelligent systems and computer vision (ISCV), pp. 1–6, https://doi.org/10.1109/ISACV.2015.7105553.Search in Google Scholar

Bharadwaj, A., El Sawy, O.A., Pavlou, P.A., and Venkatraman, N. (2013). Digital business strategy: toward a next generation of insights. MIS Q. 37: 471–482, Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2742300.10.25300/MISQ/2013/37:2.3Search in Google Scholar

Bhatia, R. and Sood, M. (2018). Security of big data: a review. In: IEEE Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC), pp. 182–186.10.1109/PDGC.2018.8745874Search in Google Scholar

Bilyk, V. (2020). Guide to machine learning applications: 7 major fields, Available at: https://volodymyrbilyk.medium.com/guide-to-machine-learning-applications-7-major-fields-58d8fd96f269.Search in Google Scholar

Bloom, G., Alsulami, B., Nwafor, E., and Bertolotti, I.C. (2018). Design patterns for the industrial Internet of Things. In: 14th IEEE International Workshop on Factory Communication Systems (WFCS), pp. 1–10.10.1109/WFCS.2018.8402353Search in Google Scholar

Boukouvala, F., Hasan, M.M.F., and Floudas, C.A. (2015). Global optimization of general constrained grey-box models: new method and its application to constrained PDEs for pressure swing adsorption. J. Global Optim. 67: 3–42, https://doi.org/10.1007/s10898-015-0376-2.Search in Google Scholar

Boyd, D. and Crawford, K. (2012). Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon. Inf. Commun. Soc. 15: 662–679, https://doi.org/10.1080/1369118X.2012.678878.Search in Google Scholar

Braun, H.T. (2015). Evaluation of Big Data maturity models – a benchmarking study to support Big Data maturity assessment in organizations, Master thesis, Tampere. Tampere University of Technology, Available at: http://urn.fi/URN:NBN:fi:tty-201505151279.Search in Google Scholar

Breneman, C.M., Brinson, L.C., Schadler, L.S., Natarajan, B., Krein, M., Wu, K., Morkowchuk, L., Li, Y., Deng, H., and Xu, H. (2013). Stalking the materials genome: a data-driven approach to the virtual design of nanostructured polymers. Adv. Funct. Mater. 23: 5746–5752, https://doi.org/10.1002/adfm.201301744.Search in Google Scholar PubMed PubMed Central

Brown, D., Schintler, L.A., and McNeely, C.L. (2017). Encyclopedia of big data. Encyclopedia of Big Data, pp. 1–3.Search in Google Scholar

Brynjolfsson, E. and McAfee, A. (2011). Race against the machine: How the digital revolution is accelerating innovation, driving productivity, and irreversibly transforming employment and the economy. Digital Frontier Press.Search in Google Scholar

Bushkovskyi, O. (2020). What is big data analytics? Definition, types, software, and use cases, Available at: https://theappsolutions.com/blog/development/what-is-big-data-analytics/.Search in Google Scholar

Cai, B., Huang, L., and Xie, M. (2017). Bayesian networks in fault diagnosis. IEEE Trans. Ind. Inform. 13: 2227–2240, https://doi.org/10.1109/TII.2017.2695583.Search in Google Scholar

Cai, L. (2012). Latent variable modeling. Shanghai Arch. Psychiatry 24: 118–120, https://doi.org/10.3969/j.issn.1002-0829.2012.02.010.Search in Google Scholar PubMed PubMed Central

Chen, H., Chiang, R.H., and Storey, V.C. (2012). Business intelligence and analytics: from big data to big impact. MIS Q. 36: 1165–1188, https://doi.org/10.2307/41703503.Search in Google Scholar

Chen, J., Chen, Y., Du, X., Li, C., Lu, J., Zhao, S., and Zhou, X. (2013). Big data challenge: a data management perspective. Front. Comput. Sci. 7: 157–164, https://doi.org/10.1007/s11704-013-3903-7.Search in Google Scholar

Chen, S., Xu, H., Liu, D., Hu, B., and Wang, H. (2014). A vision of IoT: applications, challenges, and opportunities with China perspective. IEEE Internet Things J. 1: 349–359, https://doi.org/10.1109/JIOT.2014.2337336.Search in Google Scholar

Chen, X.W. and Lin, X. (2014). Big data deep learning: challenges and perspectives. IEEE Access 2: 514–525, https://doi.org/10.1109/ACCESS.2014.2325029.Search in Google Scholar

Chetouani, Y. (2014). Model selection and fault detection approach based on Bayes decision theory: application to changes detection problem in a distillation column. Process Saf. Environ. Protect. 92: 215–223, https://doi.org/10.1016/j.psep.2013.02.004.Search in Google Scholar

Chiang, L.H. and Colegrove, L.F. (2007). Industrial implementation of on-line multivariate quality control. Chemometr. Intell. Lab. Syst. 88: 143–153, https://doi.org/10.1016/j.chemolab.2007.02.005.Search in Google Scholar

Chiang, L.H., Leardi, R., Pell, R.J., and Seasholtz, M.B. (2006). Industrial experiences with multivariate statistical analysis of batch process data. Chemometr. Intell. Lab. Syst. 81: 109–119, https://doi.org/10.1016/j.chemolab.2005.10.006.Search in Google Scholar

Chiang, L., Lu, B., and Castillo, I. (2017). Big data analytics in chemical engineering. Annu. Rev. Chem. Biomol. Eng. 8: 63–85, https://doi.org/10.1146/annurev-chembioeng-060816-101555.Search in Google Scholar PubMed

Codd, E.F. (1983). A relational model of data for large shared data banks. Commun. ACM 26: 64–69, https://doi.org/10.1145/357980.358007.Search in Google Scholar

Colegrove, L. (2015). Data initiative improves insights. Chem. Process, Available at: https://www.chemicalprocessing.com/articles/2015/data-initiative-improves-insights/.Search in Google Scholar

Constantiou, I.D. and Kallinikos, J. (2015). New games, new rules: big data and the changing context of strategy. J. Inf. Technol. 30: 44–57, https://doi.org/10.1057/jit.2014.17.Search in Google Scholar

Cozad, A., Sahinidis, N.V., and Miller, D.C. (2014). Learning surrogate models for simulation based optimization. AIChE J. 60: 2211–2227, https://doi.org/10.1002/aic.14418.Search in Google Scholar

Cupek, R., Ziebinski, A., Huczala, L., and Erdogan, H. (2016). Agent-based manufacturing execution systems for short-series production scheduling. Comput. Ind. 82: 245–258, https://doi.org/10.1016/j.compind.2016.07.009.Search in Google Scholar

Da Xu, L. (2011). Enterprise systems: state-of-the-art and future trends. IEEE Trans. Ind. Inform. 7: 630–640, https://doi.org/10.1109/TII.2011.2167156.Search in Google Scholar

Da Xu, L., He, W., and Li, S. (2014). Internet of things in industries: a survey. IEEE Trans. Ind. Inform. 10: 2233–2243, https://doi.org/10.1109/TII.2014.2300753.Search in Google Scholar

Davenport, T. (2012). The three ‘…tives’ of business analytics; predictive, prescriptive and descriptive. CIO Enterprise Forum.Search in Google Scholar

Davenport, T.H. and Patil, D.J. (2012). Data scientist. Harv. Bus. Rev. 90: 70–76, Available at: http://advantagecourseware.s3.amazonaws.com/DASC/ENIT/EN/resources/assets/U1M1_Data_Scientist-The_Sexiest_Job_Of_the_21st_Century.pdf.Search in Google Scholar

Deshpande, A. and Kumar, M. (2018). Artificial intelligence for big data: complete guide to automating big data solutions using artificial intelligence techniques. Packt Publishing Ltd.Search in Google Scholar

Dey, C. and Sen, S.K. (2020). Supervisory control and data acquisition (SCADA). In: Industrial Automation Technologies, 1st ed. CRC Press, Boca Raton.10.1201/9780429299346-4Search in Google Scholar

Dietrich, D. (2015). Data science and big data analytics: discovering, analyzing, visualizing and presenting data. John Wiley & Sons.Search in Google Scholar

Dobbelaere, M.R., Plehiers, P.P., Van de Vijver, R., Stevens, C.V., and Van Geem, K.M. (2021). Machine learning in chemical engineering: strengths, weaknesses, opportunities, and threats. Engineering 7: 1201–1211, https://doi.org/10.1016/j.eng.2021.03.019.Search in Google Scholar

Dong, Y. and Qin, S.J. (2015). Dynamic-inner partial least squares for dynamic data modeling. IFAC-Papers Online 48: 117–122, https://doi.org/10.1016/j.ifacol.2015.08.167.Search in Google Scholar

Dong, Y. and Qin, S.J. (2018a). Dynamic-inner canonical correlation and causality analysis for high dimensional time series data. IFAC-Papers Online 51: 476–481, https://doi.org/10.1016/j.ifacol.2018.09.379.Search in Google Scholar

Dong, Y. and Qin, S.J. (2018b). Dynamic latent variable analytics for process operations and control. Comput. Chem. Eng. 114: 69–80, https://doi.org/10.1016/j.compchemeng.2017.10.029.Search in Google Scholar

Dong, Y. and Qin, S.J. (2018c). A novel dynamic PCA algorithm for dynamic data modeling and process monitoring. J. Process Control 67: 1–11, https://doi.org/10.1016/j.jprocont.2017.05.002.Search in Google Scholar

Dorgo, G. and Abonyi, J. (2019). Learning and predicting operation strategies by sequence mining and deep learning. Comput. Chem. Eng. 128: 174–187, https://doi.org/10.1016/j.compchemeng.2019.06.006.Search in Google Scholar

Duever, T.A. (2019). Data science in the chemical engineering curriculum. Processes 7: 830, https://doi.org/10.3390/pr7110830.Search in Google Scholar

Dunn, W.Jr, Burgun, A., Krebs, M.O., and Rance, B. (2017). Exploring and visualizing multidimensional data in translational research platforms. Briefings Bioinf. 18: 1044–1056, https://doi.org/10.1093/bib/bbw080.Search in Google Scholar PubMed PubMed Central

Eason, J.P. and Biegler, L.T. (2016). A trust region filter method for glass box/black box optimization. AIChE J. 62: 3124–3136, https://doi.org/10.1002/aic.15325.Search in Google Scholar

Elliott, R.F. (2013). Manufacturing execution system (MES) an examination of implementation strategy, Master Thesis. California: California Polytechnic State University.Search in Google Scholar

Erickson, K.T. (1996). Programmable logic controllers. IEEE Potentials, Institute of Electrical and Electronics Engineers (IEEE) 14–17, https://doi.org/10.1109/45.481370.Search in Google Scholar

Erl, T., Khattak, W., and Buhler, P. (2016). Big data fundamentals: concepts, drivers & techniques. Prentice Hall Press.Search in Google Scholar

Fan, C., Xiao, F., Li, Z., and Wang, J. (2018). Unsupervised data analytics in mining big building operational data for energy efficiency enhancement: a review. Energy Build. 159: 296–308, https://doi.org/10.1016/j.enbuild.2017.11.008.Search in Google Scholar

Fayyad, U.M. and Simoudis, E. (1997). Data mining and knowledge discovery. Tutorial Notes at PADD’97–1st Int. In: Conf. Prac. App. KDD & Data Mining, London.10.1023/A:1009792101442Search in Google Scholar

Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Mag. 17: 37–37, https://doi.org/10.1609/aimag.v17i3.1230.Search in Google Scholar

Fernandez, M., Barron, H., and Barnard, A.S. (2017). Artificial neural network analysis of the catalytic efficiency of platinum nanoparticles. RSC Adv. 7: 48962–48971, https://doi.org/10.1039/C7RA06622H.Search in Google Scholar

Fleming, O., Fountaine, T., Henke, N., and Saleh, T. (2018). Ten red flags signaling your analytics program will fail, Available at: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ten-red-flags-signaling-your-analytics-program-will-fail.Search in Google Scholar

Gao, X., Shang, C., Jiang, Y., Huang, D., and Chen, T. (2014). Refinery scheduling with varying crude: a deep belief network classification and multimodel approach. AIChE J. 60: 2525–2532, https://doi.org/10.1002/aic.14455.Search in Google Scholar

Ge, Z., Yang, C., and Song, Z. (2009). Improved kernel PCA-based monitoring approach for nonlinear processes. Chem. Eng. Sci. 64: 2245–2255, https://doi.org/10.1016/j.ces.2009.01.050.Search in Google Scholar

Geissbauer, R., Vedso, J., and Schrauf, S. (2016). Industry 4.0: building the digital enterprise, Retrieved from PwC Website: https://www.pwc.com/gx/en/industries/industries-4.0/landing-page/industry-4.0-building-your-digital-enterprise-april-2016. pdf.Search in Google Scholar

Genender-Feltheimer, A. (2018). Visualizing high dimensional and big data. Procedia Comput. Sci. 140: 112–121, https://doi.org/10.1016/j.procs.2018.10.308.Search in Google Scholar

Ghernaout, D., Aichouni, M., and Alghamdi, A. (2018). Applying big data in water treatment industry: a new era of advance. Int. J. Adv. Appl. Sci. 5: 89–97, https://doi.org/10.21833/ijaas.2018.03.013.Search in Google Scholar

Ghosh, K., Ramteke, M., and Srinivasan, R. (2014). Optimal variable selection for effective statistical process monitoring. Comput. Chem. Eng. 60: 260–276, https://doi.org/10.1016/j.compchemeng.2013.09.014.Search in Google Scholar

Ghoshal, A., Larson, E., Subramanyam, R., and Shaw, M. (2014). The impact of business analytics strategy on social, mobile, and cloud computing adoption. In: Paper presented at 35th International Conference on Information Systems: Building a Better World Through Information Systems, ICIS 2014, Auckland, New Zealand.Search in Google Scholar

Gilchrist, A. (2016). Industry 4.0: the industrial Internet of things. Academic Press.10.1007/978-1-4842-2047-4_12Search in Google Scholar

Guan, Y. (2017). Application of data mining in chemical production. Chem. Eng. Trans. 62: 805–810, https://doi.org/10.3303/CET1762135.Search in Google Scholar

Guillén-Gosálbez, G., You, F., Galán-Martin, A., Pozo, C., and Grossmann, I.E. (2019). Process systems engineering thinking and tools applied to sustainability problems: current landscape and future opportunities. Curr. Opin. Chem. Eng. 26: 170–179, https://doi.org/10.1016/j.coche.2019.11.002.Search in Google Scholar

Guo, F., Shang, C., Huang, B., Wang, K., Yang, F., and Huang, D. (2016). Monitoring of operating point and process dynamics via probabilistic slow feature analysis. Chemometr. Intell. Lab. Syst. 151: 115–125, https://doi.org/10.1016/j.chemolab.2015.12.017.Search in Google Scholar

Gupta, P. and Tyagi, N. (2015). An approach towards big data—a review. In: IEEE international conference on computing, communication & automation, pp. 118–123.10.1109/CCAA.2015.7148356Search in Google Scholar

Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A., and Khan, S.U. (2015). The rise of “big data” on cloud computing: review and open research issues. Inf. Syst. 47: 98–115, https://doi.org/10.1016/j.is.2014.07.006.Search in Google Scholar

Hassani, H. and Silva, E.S. (2018). Big Data: a big opportunity for the petroleum and petrochemical industry. OPEC Energy Rev. 42: 74–89, https://doi.org/10.1111/opec.12118.Search in Google Scholar

He, Q.P. and Wang, J. (2007). Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes. IEEE Trans. Semicond. Manuf. 20: 345–354, https://doi.org/10.1109/TSM.2007.907607.Search in Google Scholar

He, W. and Da Xu, L. (2012). Integration of distributed enterprise applications: a survey. IEEE Trans. Ind. Inform. 10: 35–42, https://doi.org/10.1109/TII.2012.2189221.Search in Google Scholar

Hermansson, A.W. and Syafiie, S. (2015). Model predictive control of pH neutralization processes: a review. Control Eng. Pract. 45: 98–109, https://doi.org/10.1016/j.conengprac.2015.09.005.Search in Google Scholar

Hollender, M., Chioua, M., and Xu, C. (2018). Online big data chemical batch analytics. Chim. Oggi 36: 33–35.Search in Google Scholar

Huang, K., Zhan, X.L., Chen, F.Q., and Lü, D.W. (2003). Catalyst design for methane oxidative coupling by using artificial neural network and hybrid genetic algorithm. Chem. Eng. Sci. 58: 81–87, https://doi.org/10.1016/S0009-2509(02)00432-3.Search in Google Scholar

Inselberg, A. (2008). Parallel coordinates: visualization, exploration and classification of high-dimensional data. In: Handbook of data visualization. Berlin, Heidelberg: Springer, pp. 643–680.10.1007/978-3-540-33037-0_25Search in Google Scholar

Inukollu, V.N., Arsi, S., and Ravuri, S.R. (2014). Security issues associated with big data in cloud computing. Int. J. Netw. Secur. Appl. 6: 45–56, https://doi.org/10.5121/ijnsa.2014.6304.Search in Google Scholar

Isermann, R. (2006). Fault detection of control loops. In: Fault-diagnosis systems. Berlin, Heidelberg: Springer.10.1007/3-540-30368-5Search in Google Scholar

Jagtap, S. and Duong, L.N.K. (2019). Improving the new product development using big data: a case study of a food company. Br. Food J. 121: 2835–2848, https://doi.org/10.1108/BFJ-02-2019-0097.Search in Google Scholar

Jardine, A.K., Lin, D., and Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 20: 1483–1510, https://doi.org/10.1016/j.ymssp.2005.09.012.Search in Google Scholar

Jia, X., Feng, Q., Fan, T., and Lei, Q. (2012). RFID technology and its applications in internet of things (IoT). In: IEEE 2nd international conference on consumer electronics, communications and networks (CECNet), pp. 1282–1285.10.1109/CECNet.2012.6201508Search in Google Scholar

Ji-fan Ren, S., Fosso Wamba, S., Akter, S., Dubey, R., and Childe, S.J. (2017). Modelling quality dynamics, business value and firm performance in a big data analytics environment. Int. J. Prod. Res. 55: 5011–5026, https://doi.org/10.1080/00207543.2016.1154209.Search in Google Scholar

Jin, H., Chen, X., Yang, J., and Wu, L. (2014). 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: 77–93, https://doi.org/10.1016/j.compchemeng.2014.07.014.Search in Google Scholar

Kadlec, P., Gabrys, B., and Strandt, S. (2009). Data-driven soft sensors in the process industry. Comput. Chem. Eng. 33: 795–814, https://doi.org/10.1016/j.compchemeng.2008.12.012.Search in Google Scholar

Kadlec, P., Grbić, R., and Gabrys, B. (2011). Review of adaptation mechanisms for data-driven soft sensors. Comput. Chem. Eng. 35: 1–24, https://doi.org/10.1016/j.compchemeng.2010.07.034.Search in Google Scholar

Kaestner, M. (2016). Big data means big opportunities for chemical companies. Reaction–Chemicals Magazine, 20th ed. KPMG Global Chemicals Institute.Search in Google Scholar

Kano, M. and Fujiwara, K. (2012). Virtual sensing technology in process industries: trends and challenges revealed by recent industrial applications. J. Chem. Eng. Jpn. 46: 1–17, https://doi.org/10.1252/jcej.12we167.Search in Google Scholar

Katal, A., Wazid, M., and Goudar, R.H. (2013). Big data: issues, challenges, tools and good practices. In: IEEE sixth international conference on contemporary computing (IC3), pp. 404–409.10.1109/IC3.2013.6612229Search in Google Scholar

Keane, A. and Nair, P. (2005). Computational approaches for aerospace design: the pursuit of excellence. John Wiley & Sons.10.1002/0470855487Search in Google Scholar

Kebisek, M., Tanuska, P., Spendla, L., Kotianova, J., and Strelec, P. (2020). Artificial intelligence platform proposal for paint structure quality prediction within the industry 4.0 concept. IFAC Papers Online 53: 1168–11174, https://doi.org/10.1016/j.ifacol.2020.12.299.Search in Google Scholar

Khatibisepehr, S., Huang, B., and Khare, S. (2013). Design of inferential sensors in the process industry: a review of Bayesian methods. J. Process Control 23: 1575–1596, https://doi.org/10.1016/j.jprocont.2013.05.007.Search in Google Scholar

Kletti, J. (Ed.) (2007). Manufacturing execution system-MES. Springer Science & Business Media.10.1007/978-3-540-49744-8Search in Google Scholar

Knight, O.S. (1948). Quantitative analysis for chemical engineering students. J. Chem. Educ. 25: 602–604, https://doi.org/10.1021/ed025p602.Search in Google Scholar

Koch, M.T., Baars, H., Lasi, H., and Kemper, H.G. (2010). Manufacturing execution systems and business intelligence for production environments. In: AMCIS, p. 436, Available at: https://aisel.aisnet.org/amcis2010/436.Search in Google Scholar

Komal, Ms. (2018). A review paper on big data analytics tools. Int. J. Tec. Innov. Mod. Eng. Sci. 4: 1012–1017.Search in Google Scholar

Komorowski, M., Marshall, D.C., Salciccioli, J.D., and Crutain, Y. (2016). Exploratory data analysis. In: Secondary analysis of electronic health records. Cham: Springer, pp. 185–203, https://doi.org/10.1007/978-3-319-43742-2_15.Search in Google Scholar PubMed

Krumeich, J., Jacobi, S., Werth, D., and Loos, P. (2014b). Big data analytics for predictive manufacturing control-a case study from process industry. In: IEEE international congress on big data, pp. 530–537.10.1109/BigData.Congress.2014.83Search in Google Scholar

Krumeich, J., Werth, D., Loos, P., Schimmelpfennig, J., and Jacobi, S. (2014a). Advanced planning and control of manufacturing processes in steel industry through big data analytics: case study and architecture proposal. In: IEEE international conference on big data, pp. 16–24, https://doi.org/10.1109/BigData.2014.7004408.Search in Google Scholar

Lakshminarayanan, D. and Prabhakaran, S. (2020). A study on python programming language. Dogo Rangsang Res. J. 10: 2347–7180.Search in Google Scholar

Laney, D. (2001). 3D data management: controlling data volume, velocity and variety. META Group Research Note 6: 1.Search in Google Scholar

Lavin, A. and Klabjan, D. (2015). Clustering time-series energy data from smart meters. Energy efficiency 8: 681–689, https://doi.org/10.1007/s12053-014-9316-0.Search in Google Scholar

Lee, J.H., Shin, J., and Realff, M.J. (2018). Machine learning: overview of the recent progresses and implications for the process systems engineering field. Comput. Chem. Eng. 114: 111–121, https://doi.org/10.1016/j.compchemeng.2017.10.008.Search in Google Scholar

Lee, J.M., Qin, S.J., and Lee, I.B. (2007). Fault detection of nonlinear processes using kernel independent component analysis. Can. J. Chem. Eng. 85: 526–536, https://doi.org/10.1002/cjce.5450850414.Search in Google Scholar

Lee, J.M., Yoo, C.K., and Lee, I.B. (2004). Enhanced process monitoring of fed-batch penicillin cultivation using time-varying and multivariate statistical analysis. J. Biotechnol. 110: 119–136, https://doi.org/10.1016/j.jbiotec.2004.01.016.Search in Google Scholar PubMed

Li, F. (2011). Dynamic neural network based nonlinear control of a distillation column. Intell. Control Autom. 2: 383–387, https://doi.org/10.4236/ica.2011.24043.Search in Google Scholar

Li, F., Zhang, J., Shang, C., Huang, D., Oko, E., and Wang, M. (2018). Modelling of a post-combustion CO2 capture process using deep belief network. Appl. Therm. Eng. 130: 997–1003, https://doi.org/10.1016/j.applthermaleng.2017.11.078.Search in Google Scholar

Li, C., Chen, Y., and Shang, Y. (2022). A review of industrial big data for decision making in intelligent manufacturing. Int. J. Eng. Sci. Technol. 29: 101021. https://doi.org/10.1016/j.jestch.2021.06.001.Search in Google Scholar

Li, S., Da Xu, L., and Wang, X. (2012). Compressed sensing signal and data acquisition in wireless sensor networks and internet of things. IEEE Trans. Ind. Inform. 9: 2177–2186, https://doi.org/10.1109/TII.2012.2189222.Search in Google Scholar

Li, Z., Ma, X., and Xin, H. (2017). Feature engineering of machine-learning chemisorption models for catalyst design. Catal. Today 280: 232–238, https://doi.org/10.1016/j.cattod.2016.04.013.Search in Google Scholar

Liu, S., Mcgree, J., Ge, Z., and Xie, Y. (2015). Computational and statistical methods for analyzing big data with applications. Academic Press.10.1016/B978-0-12-803732-4.00007-6Search in Google Scholar

Liu, Y., Yang, C., Gao, Z., and Yao, Y. (2018). Ensemble deep kernel learning with application to quality prediction in industrial polymerization processes. Chemometr. Intell. Lab. Syst. 174: 15–21, https://doi.org/10.1016/j.chemolab.2018.01.008.Search in Google Scholar

Long, N.B., Tran-Dang, H., and Kim, D.S. (2018). Energy-aware real-time routing for large-scale industrial Internet of Things. IEEE Internet Things J. 5: 2190–2199, https://doi.org/10.1109/JIOT.2018.2827050.Search in Google Scholar

Lu, B., Castillo, I., Chiang, L., and Edgar, T.F. (2014). Industrial PLS model variable selection using moving window variable importance in projection. Chemometr. Intell. Lab. Syst. 135: 90–109, https://doi.org/10.1016/j.chemolab.2014.03.020.Search in Google Scholar

Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S.J.H., Ogata, H., Baltes, J., Guerra, R., Li, P., and Tsai, C.-C. (2020). Challenges and future directions of big data and artificial intelligence in education. Front. Psychol. 11: 580820, https://doi.org/10.3389/fpsyg.2020.580820.Search in Google Scholar PubMed PubMed Central

Ma, Y. and Huang, B. (2018). Extracting dynamic features with switching models for process data analytics and application in soft sensing. AIChE J. 64: 2037–2051, https://doi.org/10.1002/aic.16059.Search in Google Scholar

Ma, Y., Niu, P., Yan, S., and Li, G. (2018). A modified online sequential extreme learning machine for building circulation fluidized bed boiler’s NOx emission model. Appl. Math. Comput. 334: 214–226, https://doi.org/10.1016/j.amc.2018.03.010.Search in Google Scholar

Maaten, L.V.D. and Hinton, G. (2008). Visualizing data using t-SNE. J. Mach. Learn. Res. 9: 2579–2605, Available at: http://www.jmlr.org/papers/v9/vandermaaten08a.html.Search in Google Scholar

MacGregor, J. and Cinar, A. (2012). Monitoring, fault diagnosis, fault-tolerant control and optimization: data driven methods. Comput. Chem. Eng. 47: 111–120, https://doi.org/10.1016/j.compchemeng.2012.06.017.Search in Google Scholar

Mahadevan, S. and Shah, S.L. (2009). Fault detection and diagnosis in process data using one-class support vector machines. J. Process Control 19: 1627–1639, https://doi.org/10.1016/j.jprocont.2009.07.011.Search in Google Scholar

Maisel, D. (2019). Measurement in the smart factory: improving productivity, efficiency, and safety, Available at: https://www.plantservices.com/articles/2019/measurement-in-the-smart-factory-improving-productivity-efficiency-and-safety/.Search in Google Scholar

Manee, V., Chebeir, J., and Romagnoli, J. (2019). A comparative study of different deep learning approaches for the prediction of natural gas demand in the United States. Chem. Eng. Trans. 74: 745–750, https://doi.org/10.3303/CET1974125.Search in Google Scholar

Manogaran, G., Thota, C., and Kumar, M.V. (2016). Meta cloud data storage architecture for big data security in cloud computing. Procedia Comput. Sci. 87: 128–133, https://doi.org/10.1016/j.procs.2016.05.138.Search in Google Scholar

Manyika, J. (2011). Big data: the next Frontier for innovation, competition, and productivity, Available at: http://www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation.Search in Google Scholar

Maria, R.E., Junior, L.A.R., de Vasconcelos, L.E.G., Pinto, A.F.M., Tsoucamoto, P.T., Silva, H.N.A., and Dias, L.A.V. (2015). Applying scrum in an interdisciplinary project using big data, internet of things, and credit cards. In: IEEE 12th international conference on information technology-new generations, pp. 67–72.10.1109/ITNG.2015.17Search in Google Scholar

Maroufkhani, P., Wagner, R., Wan Ismail, W.K., Baroto, M.B., and Nourani, M. (2019). Big data analytics and firm performance: a systematic review. Information 10: 1–21, https://doi.org/10.3390/info10070226.Search in Google Scholar

Mauro, A., Greco, M., and Grimaldi, M. (2016). A formal definition of big data based on its essential features. Libr. Rev. 65: 122–135, https://doi.org/10.1108/LR-06-2015-0061.Search in Google Scholar

Mehdiyev, N., Lahann, J., Emrich, A., Enke, D., Fettke, P., and Loos, P. (2017). Time series classification using deep learning for process planning: a case from the process industry. Procedia Comput. Sci. 114: 242–249, https://doi.org/10.1016/j.procs.2017.09.066.Search in Google Scholar

Mehta, B.R. and Reddy, Y.J. (2014). Industrial process automation systems: design and implementation. Butterworth-Heinemann.Search in Google Scholar

Meng, C., Wang, Y., Zhang, X., Mandal, A., Zhong, W., and Ma, P. (2017). Effective statistical methods for big data analytics. In: Handbook of research on applied cybernetics and systems science. IGI Global, pp. 280–299.10.4018/978-1-5225-2498-4.ch014Search in Google Scholar

Meyer, H., Fuchs, F., and Thiel, K. (2009). Manufacturing execution systems (MES): optimal design, planning, and deployment. McGraw Hill Professional.Search in Google Scholar

Mikalef, P., Pappas, I.O., Krogstie, J., and Giannakos, M. (2018). Big data analytics capabilities: a systematic literature review and research agenda. Inf. Syst. E Bus. Manag. 16: 547–578, https://doi.org/10.1007/s10257-017-0362-y.Search in Google Scholar

Mikusz, M. (2014). Towards an understanding of cyber-physical systems as industrial software-product-service systems. Procedia CIRP 16: 385–389, https://doi.org/10.1016/j.procir.2014.02.025.Search in Google Scholar

Miorandi, D., Sicari, S., De Pellegrini, F., and Chlamtac, I. (2012). Internet of things: vision, applications and research challenges. Ad Hoc Netw. 10: 1497–1516, https://doi.org/10.1016/j.adhoc.2012.02.016.Search in Google Scholar

Mishra, S. and Misra, A. (2017). Structured and unstructured big data analytics. In: IEEE international conference on current trends in computer, electrical, electronics and communication (CTCEEC), pp. 740–746.10.1109/CTCEEC.2017.8454999Search in Google Scholar

Misra, N.N., Dixit, Y., Al-Mallahi, A., Bhullar, M.S., Upadhyay, R., and Martynenko, A. (2020). IoT, big data and artificial intelligence in agriculture and food industry. IEEE Internet Things J., https://doi.org/10.1109/JIOT.2020.2998584.Search in Google Scholar

Mohamed, N. and Al-Jaroodi, J. (2014). Real-time big data analytics: applications and challenges. In: IEEE international conference on high performance computing & simulation (HPCS), pp. 305–310.10.1109/HPCSim.2014.6903700Search in Google Scholar

Mohammadi, A., Zarghami, R., Lefebvre, D., Golshan, S., and Mostoufi, N. (2019). Soft sensor design and fault detection using Bayesian network and probabilistic principal component analysis. J. Adv. Manuf. Process. 1: e10027, https://doi.org/10.1002/amp2.10027.Search in Google Scholar

Mohammadpoor, M. and Torabi, F. (2018). Big Data analytics in oil and gas industry: an emerging trend. Petroleum, https://doi.org/10.1016/j.petlm.2018.11.001.Search in Google Scholar

Monk, E. and Wagner, B. (2012). Concepts in enterprise resource planning. Cengage Learning.Search in Google Scholar

Monostori, L. (2014). Cyber-physical production systems: roots, expectations and R&D challenges. Procedia CIRP 17, Variety Management in Manufacturing. Procedia CIRP 17: 9–13, https://10.1016/j.procir.2014.03.115.10.1016/j.procir.2014.03.115Search in Google Scholar

Mourtzis, D., Vlachou, E., and Milas, N.J.P.C. (2016a). Industrial big data as a result of IoT adoption in manufacturing. Procedia CIRP 55: 290–295, https://doi.org/10.1016/j.procir.2016.07.038.Search in Google Scholar

Mourtzis, D., Vlachou, E., Milas, N., and Dimitrakopoulos, G. (2016b). Energy consumption estimation for machining processes based on real-time shop floor monitoring via wireless sensor networks. Procedia CIRP 57: 637–642, https://doi.org/10.1016/j.procir.2016.11.110.Search in Google Scholar

Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R., and Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. J. Big Data, 2: 1, https://doi.org/10.1186/s40537-014-0007-7.Search in Google Scholar

National Research Council (2013). Frontiers in massive data analysis. National Academies Press.Search in Google Scholar

Newhart, K.B., Holloway, R.W., Hering, A.S., and Cath, T.Y. (2019). Data-driven performance analyses of wastewater treatment plants: a review. Water Res. 157: 498–513, https://doi.org/10.1016/j.watres.2019.03.030.Search in Google Scholar PubMed

Ngai, E.W.T., Moon, K.K., Riggins, F.J., and Candace, Y.Y. (2008). RFID research: an academic literature review (1995–2005) and future research directions. Int. J. Prod. Econ. 112: 510–520, https://doi.org/10.1016/j.ijpe.2007.05.004.Search in Google Scholar

Nguyen, T., Gosine, R.G., and Warrian, P. (2020). A systematic review of big data analyticsfor oil and gas industry 4.0. IEEE Access 8: 61184–61201, https://doi.org/10.1109/ACCESS.2020.2979678.Search in Google Scholar

Nguyen, T.T., Cao, L.H., Nguyen, T.A., and Dang, X.P. (2019). Multi-response optimization of the roller burnishing process in terms of energy consumption and product quality. J. Clean. Prod., https://doi.org/10.1016/j.jclepro.2019.119328.Search in Google Scholar

Nie, Y., Biegler, L.T., Villa, C.M., and Wassick, J.M. (2015). Discrete time formulation for the integration of scheduling and dynamic optimization. Ind. Eng. Chem. Res. 54: 4303–4315, https://doi.org/10.1021/ie502960p.Search in Google Scholar

Ning, C. and You, F. (2019). Optimization under uncertainty in the era of big data and deep learning: when machine learning meets mathematical programming. Comput. Chem. Eng. 125: 434–448, https://doi.org/10.1016/j.compchemeng.2019.03.034.Search in Google Scholar

Niu, Y. (2018). Application of data mining technology in chemical engineering optimization. Chem. Eng. Trans. 66: 901–906, https://doi.org/10.3303/CET1866151.Search in Google Scholar

Nyce, C. (2007). Predictive analytics white paper, American Institute for chartered property casualty underwriters. Insurance Institute of America, Available at: https://www.the-digital-insurer.com/wp-content/uploads/2013/12/78-Predictive-Modeling-White-Paper.pdf.Search in Google Scholar

Ochieng, P.A. (2009). An analysis of the strengths and limitation of qualitative and quantitative research paradigms. Probl. Educ. 21st Century 13: 13–18, Available at: http://oaji.net/articles/2014/457-1393665925.pdf.Search in Google Scholar

Olkin, I. and Sampson, A.R. (2001). Multivariate analysis: overview. Int. Encyclopedia Soc. Behav. Sci.: 10240–10247, https://doi.org/10.1016/b0-08-043076-7/00472-1.Search in Google Scholar

Olshannikova, E., Ometov, A., Koucheryavy, Y., and Olsson, T. (2016). Visualizing big data. In: Big data technologies and applications. Cham: Springer, pp. 101–131, https://doi.org/10.1007/978-3-319-44550-2_4.Search in Google Scholar

Onel, M., Kieslich, C.A., Guzman, Y.A., Floudas, C.A., and Pistikopoulos, E.N. (2018). Big data approach to batch process monitoring: simultaneous fault detection and diagnosis using nonlinear support vector machine-based feature selection. Comput. Chem. Eng. 115: 46–63, https://doi.org/10.1016/j.compchemeng.2018.03.025.Search in Google Scholar PubMed PubMed Central

Ould-Bouamama, B., El Harabi, R., Abdelkrim, M.N., and Gayed, M.B. (2012). Bond graphs for the diagnosis of chemical processes. Comput. Chem. Eng. 36: 301–324, https://doi.org/10.1016/j.compchemeng.2011.07.008.Search in Google Scholar

Peil, K.P., Neithamer, D.R., Patrick, D.W., Wilson, B.E., and Tucker, C.J. (2004). Applications of high throughput research at the dow chemical company. Macromol. Rapid Commun. 25: 119–126, https://doi.org/10.1002/marc.200300160.Search in Google Scholar

Perera, C., Ranjan, R., Wang, L., Khan, S.U., and Zomaya, A.Y. (2015). Big data privacy in the internet of things era. IT Professional 17: 32–39, https://doi.org/10.1109/MITP.2015.34.Search in Google Scholar

Piccione, P.M. (2019). Realistic interplays between data science and chemical engineering in the first quarter of the 21st century: facts and a vision. Chem. Eng. Res. Des. 147: 668–675, https://doi.org/10.1016/j.cherd.2019.05.046.Search in Google Scholar

Pistikopoulos, E.N., Barbosa-Povoa, A., Lee, J.H., Misener, R., Mitsos, A., Reklaitis, G.V., Venkatasubramanian, V., You, F., and Gani, R. (2021). Process systems engineering – the generation next? Comput. Chem. Eng. 147: 107252, https://doi.org/10.1016/j.compchemeng.2021.107252.Search in Google Scholar

Plovoso, M.J. and Kosanovich, K.A. (1994). Applications of multivariate statistical methods to process monitoring and controller design. Int. J. Control 59: 743–765, https://doi.org/10.1080/00207179408923103.Search in Google Scholar

Prabhu, C.S.R., Chivukula, A.S., Mogadala, A., Ghosh, R., and Livingston, L.J. (2019). Big data analytics. In: Big data analytics: systems, algorithms, applications. Singapore: Springer, pp. 1–23, https://doi.org/10.1007/978-981-15-0094-7_1.Search in Google Scholar

Qi, Q. and Tao, F. (2018). Digital twin and big data towards smart manufacturing and industry 4.0: 360-degree comparison. IEEE Access 6: 3585–3593, https://doi.org/10.1109/ACCESS.2018.2793265.Search in Google Scholar

Qin, S.J. (2014). Process data analytics in the era of big data. AIChE J. 60: 3092–3100, https://doi.org/10.1002/aic.14523.Search in Google Scholar

Rad, M.A.A. and Yazdanpanah, M.J. (2015). Designing supervised local neural network classifiers based on EM clustering for fault diagnosis of Tennessee Eastman process. Chemometr. Intell. Lab. Syst. 146: 149–157, https://doi.org/10.1016/j.chemolab.2015.05.013.Search in Google Scholar

Rajaraman, V. (2016). Big data analytics. Resonance 21: 695–716, https://doi.org/10.1111/j.1740-9713.2014.00762.x/full.Search in Google Scholar

Ramadan, R.A. (2017). Big data tools-an overview. Int. J. Comput. Softw. Eng., https://doi.org/10.15344/2456-4451/2017/125.Search in Google Scholar

Rato, T., Reis, M., Schmitt, E., Hubert, M., and De Ketelaere, B. (2016). A systematic comparison of PCA based statistical process monitoring methods for high dimensional, time dependent processes. AIChE J. 62: 1478–1493, https://doi.org/10.1002/aic.15062.Search in Google Scholar

Reinhardt, I.C., Oliveira, J.C., and Ring, D.T. (2020). Current perspectives on the development of industry 4.0 in the pharmaceutical sector. J. Ind. Inf. Integr. 18: 100131, https://doi.org/10.1016/j.jii.2020.100131.Search in Google Scholar

Riabacke, M., Danielson, M., and Ekenberg, L. (2012). State-of-the-art prescriptive criteria weight elicitation. Adv. Decis. Sci., https://doi.org/10.1155/2012/276584.Search in Google Scholar

Riggins, F.J. and Wamba, S.F. (2015). Research directions on the adoption, usage, and impact of the internet of things through the use of big data analytics. In: IEEE 48th Hawaii international conference on system sciences, pp. 1531–1540, https://doi.org/10.1109/HICSS.2015.186.Search in Google Scholar

Robinne, F.N., Bladon, K.D., Miller, C., Parisien, M.A., Mathieu, J., and Flannigan, M.D. (2018). A spatial evaluation of global wildfire-water risks to human and natural systems. Sci. Total Environ. 610: 1193–1206, https://doi.org/10.1016/j.scitotenv.2017.08.112.Search in Google Scholar PubMed

Romijn, J.B. (2014). Using big data in the public sector. Uncertainties and Readiness in the Dutch Public Executive Sector, Master Thesis. Delft: Delft University of Technology.Search in Google Scholar

Roy, A., Cruz, R.M., Sabourin, R., and Cavalcanti, G.D. (2018). A study on combining dynamic selection and data preprocessing for imbalance learning. Neurocomputing 286: 179–192, https://doi.org/10.1016/j.neucom.2018.01.060.Search in Google Scholar

Sadiku, M.N., Musa, S.M., and Musa, O.M. (2019). Big data in chemical engineering. Int. J. Adv. Sci. Res. Eng. 5: 100–102, https://doi.org/10.31695/IJASRE.2019.Search in Google Scholar

Saenz de Ugarte, B., Artiba, A., and Pellerin, R. (2009). Manufacturing execution system – a literature review. Prod. Plann. Control 20: 525–539, https://doi.org/10.1080/09537280902938613.Search in Google Scholar

Sagiroglu, S. and Sinanc, D. (2013). Big data: a review. In: IEEE international conference on collaboration technologies and systems (CTS), pp. 42–47.10.1109/CTS.2013.6567202Search in Google Scholar

Santos, A.F.C., Teles, Í.P., Siqueira, O.M.P., and Oliveira, A.A. (2018). Big data: a systematic review. In: Latifi, S. (Ed.), Information technology – new generations. Advances in intelligent systems and computing, Vol. 558. Cham: Springer.10.1007/978-3-319-54978-1_64Search in Google Scholar

Sendek, A.D., Yang, Q., Cubuk, E.D., Duerloo, K.A., Cui, Y., and Reed, E.J. (2017). Holistic computational structure screening of more than 12000 candidates for solid lithium-ion conductor materials. Energy Environ. Sci. 10: 306–320, https://doi.org/10.1039/C6EE02697D.Search in Google Scholar

Seng, N.Y. and Srinivasan, R. (2009). Data mining for the chemical process industry. In: Encyclopedia of data warehousing and mining, 2nd ed. IGI Global, pp. 458–464.10.4018/978-1-60566-010-3.ch072Search in Google Scholar

Shang, C. and You, F. (2018). Distributionally robust optimization for planning and scheduling under uncertainty. Comput. Chem. Eng. 110: 53–68, https://doi.org/10.1016/j.compchemeng.2017.12.002.Search in Google Scholar

Shang, C., and You, F. (2019). Data analytics and machine learning for smart process manufacturing: recent advances and perspectives in the big data era. Engineering 5: 1010–1016, https://doi.org/10.1016/j.eng.2019.01.019.Search in Google Scholar

Shang, C., Huang, X., and You, F. (2017). Data-driven robust optimization based on kernel learning. Comput. Chem. Eng. 106: 464–479, https://doi.org/10.1016/j.compchemeng.2017.07.004.Search in Google Scholar

Shang, C., Yang, F., Gao, X., Huang, X., Suykens, J.A., and Huang, D. (2015). Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis. AIChE J. 61: 3666–3682, https://doi.org/10.1002/aic.14888.Search in Google Scholar

Shang, C., Yang, F., Huang, B., and Huang, D. (2018). Recursive slow feature analysis for adaptive monitoring of industrial processes. IEEE Trans. Ind. Electron. 65: 8895–8905, https://doi.org/10.1109/TIE.2018.2811358.Search in Google Scholar

Shang, C., Yang, F., Huang, D., and Lyu, W. (2014). Data-driven soft sensor development based on deep learning technique. J. Process Control 24: 223–233, https://doi.org/10.1016/j.jprocont.2014.01.012.Search in Google Scholar

Shi, S. and Zhao, Z. (2018). Fluidized bed agglomeration monitoring based on convolutional neural networks. In: IEEE 37th Chinese control conference (CCC), pp. 5973–5978.10.23919/ChiCC.2018.8483845Search in Google Scholar

Shobha, G. and Rangaswamy, S. (2018). Chapter 8 - machine learning. In: Gudivada, V.N. and Rao, C.R. (Eds.). Handbook of statistics. Elsevier, pp. 197–228.10.1016/bs.host.2018.07.004Search in Google Scholar

Shu, Y., Ming, L., Cheng, F., Zhang, Z., and Zhao, J. (2016). Abnormal situation management: challenges and opportunities in the big data era. Comput. Chem. Eng. 91: 104–113, https://doi.org/10.1016/j.compchemeng.2016.04.011.Search in Google Scholar

Siddiqa, A., Karim, A., and Gani, A. (2017). Big data storage technologies: a survey. Front. Inf. Technol. Electron. Eng. 18: 1040–1070, https://doi.org/10.1631/FITEE.1500441.Search in Google Scholar

Stamford, C. (2020). Gartner identifies the top strategic technology trends for 2021, Available at: https://www.gartner.com/en/newsroom/press-releases/2020-10-19-gartner-identifies-the-top-strategic-technology-trends-for-2021.Search in Google Scholar

Stewart, T.R. and McMillan, C. (1987). Descriptive and prescriptive models for judgment and decision making: implications for knowledge engineering. In: Expert judgment and expert systems. Berlin, Heidelberg: Springer, pp. 305–320.10.1007/978-3-642-86679-1_17Search in Google Scholar

Suchetha, K.N. and Guruprasad, H.S. (2015). Integration of IOT, cloud and big data. Glob. J. Eng. Sci. Res. 2: 251–258.Search in Google Scholar

Sumbal, M.S., Tsui, E., and See-to, E.W. (2017). Interrelationship between big data and knowledge management: an exploratory study in the oil and gas sector. J. Knowl. Manag., https://doi.org/10.1108/JKM-07-2016-0262.Search in Google Scholar

Sun, B., Wang, J., He, Z., Zhou, H., and Gu, F. (2019). Fault identification for a closed-loop control system based on an improved deep neural network. Sensors 19: 2131, https://doi.org/10.3390/s19092131.Search in Google Scholar PubMed PubMed Central

Sun, C. (2012). Application of RFID technology for logistics on internet of things. AASRI Procedia 1: 106–111, https://doi.org/10.1016/j.aasri.2012.06.019.Search in Google Scholar

Sun, W., Wang, Q., Zhou, Y., and Wub, J. (2020). Material and energy flows of the iron and steel industry: status quo, challenges and perspectives. Appl. Energy 268: 114946, https://doi.org/10.1016/j.apenergy.2020.114946.Search in Google Scholar

Swanstrom, R. (2015). NIST definition of big data and data science, Available at: www.101.datascience.community/2015/nist-defines-big-data-and-data-science.Search in Google Scholar

Tan, L. and Wang, N. (2010). Future internet: the internet of things. In: IEEE 3rd international conference on advanced computer theory and engineering (ICACTE), Vol. 5, pp. V5–V376.Search in Google Scholar

Tao, D., Yang, P., and Feng, H. (2020). Utilization of text mining as a big data analysis tool for food science and nutrition. Compr. Rev. Food Sci. Food Saf.: 1–20, https://doi.org/10.1111/1541-4337.12540.Search in Google Scholar PubMed

Tasdemir, K. and Merényi, E. (2012). SOM-based topology visualisation for interactive analysis of high-dimensional large datasets. Mach. Learn. Rep. 1: 13–15, Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.684.2191&rep=rep1&type=pdf.Search in Google Scholar

Team, O.R. (2011). Big data now: current perspectives from O’Reilly radar. Sebastopol, CA: OReilly Media.Search in Google Scholar

Teti, R., Jemielniak, K., O’Donnell, G., and Dornfeld, D. (2010). Advanced monitoring of machining operations. CIRP Ann. 59: 717–739, https://doi.org/10.1016/j.cirp.2010.05.010.Search in Google Scholar

Theoleyre, F., and Pang, A.C. (Eds.) (2013). Internet of things and M2M communications. River Publishers Series in Information Science and Technology.Search in Google Scholar

Thienen, S.V., Clinton, A., Mahto, M., and Shiderman, B. (2016). Industry 4.0 and the chemicals industry. Catalyzing transformation through operations improvement and business growth. Deloitte University Press.Search in Google Scholar

Thoben, K.D., Wiesner, S., and Wuest, T. (2017). “Industry 4.0” and smart manufacturing-a review of research issues and application examples. Int. J. Autom. Technol. 11: 4–16, https://doi.org/10.20965/ijat.2017.p0004.Search in Google Scholar

Tóth, L.R., Torgyik, T., Nagy, L., and Abonyi, J. (2016). Multiobjective optimization for efficient energy utilization in batch biodiesel production. Clean Technol. Environ. Policy 18: 95–104, https://doi.org/10.1007/s10098-015-0996-8.Search in Google Scholar

Trifu, M.R. and Ivan, M.L. (2014). Big data: present and future. Database Syst. J. 5: 32–41, Available at: http://www.scce.ac.in/e_journals/15_4.pdf.Search in Google Scholar

Tsay, C. and Baldea, M. (2019). Integrating production scheduling and process control using latent variable dynamic models. Control Eng. Pract. 94: 104201, https://doi.org/10.1016/j.conengprac.2019.104201.Search in Google Scholar

Tsay, C., Kumar, A., Flores-Cerrillo, J., and Baldea, M. (2019). Optimal demand response scheduling of an industrial air separation unit using data-driven dynamic models. Comput. Chem. Eng. 126: 22–34, https://doi.org/10.1016/j.compchemeng.2019.03.022.Search in Google Scholar

Tsuchiya, S., Sakamoto, Y., Tsuchimoto, Y., and Lee, V. (2012). Big data processing in cloud enviroments. Fujitsu Sci. Tech. J. 48: 159–168.Search in Google Scholar

Tulasi, B. (2013). Significance of big data and analytics in higher education. Int. J. Comput. Appl. 68: 21–23, https://doi.org/10.5120/11648-7142.Search in Google Scholar

Udugama, I., A., Gargalo, C.L., Yamashita, Y., Taube, M.A., Palazoglu, A., Young, B.R., Gernaey, K.V., Kulahci, M., and Bayer, C. (2020). The role of big data in industrial (bio)chemical process operations. Ind. Eng. Chem. Res. 59: 15283–15297, https://doi.org/10.1021/acs.iecr.0c01872.Search in Google Scholar

Usama, M., Qadir, J., Raza, A., Arif, H., Yau, K.L.A., Elkhatib, Y., and Al-Fuqaha, A. (2019). Unsupervised machine learning for networking: techniques, applications and research challenges. IEEE Access 7: 65579–65615, https://doi.org/10.1109/ACCESS.2019.2916648.Search in Google Scholar

Valdés, J. and Barton, A. (2006). Virtual reality spaces: visual data mining with a hybrid computational intelligence tool. NRC/ERB-1137, NRC 48501.Search in Google Scholar

Varga, T., Abonyi, J., and Szeifert, F. (2007). Applying decision trees to investigate the operating regimes of a production process. Acta agr. Kapos. 11: 175–186, Available at: http://journal.ke.hu/index.php/aak/article/view/1878/2407.Search in Google Scholar

Venkatasubramanian, V. (2009). Drowning in data: informatics and modeling challenges in a data-rich networked world. Chem. Eng., https://doi.org/10.1002/aic.11756.Search in Google Scholar

Venkatasubramanian, V. (2018). The promise of artificial intelligence in chemical engineering: is it here, finally? AIChE J., https://doi.org/10.1002/aic.16489.Search in Google Scholar

Venkatasubramanian, V., Chan, K., and Caruthers, J.M. (1994). Computer-aided molecular design using genetic algorithms. Comput. Chem. Eng. 18: 833–844, https://doi.org/10.1016/0098-1354(93)E0023-3.Search in Google Scholar

Venkatasubramanian, V., Rengaswamy, R., and Kavuri, S.N. (2003b). A review of process fault detection and diagnosis: Part II: qualitative models and search strategies. Comput. Chem. Eng. 27: 313–326, https://doi.org/10.1016/S0098-1354(02)00161-8.Search in Google Scholar

Venkatasubramanian, V., Rengaswamy, R., Yin, K., and Kavuri, S.N. (2003a). A review of process fault detection and diagnosis: part I: quantitative model-based methods. Comput. Chem. Eng. 27: 293–311, https://doi.org/10.1016/S0098-1354(02)00160-6.Search in Google Scholar

Vidal-Puig, S., Vitale, R., and Ferrer, A. (2019). Data-driven supervised fault diagnosis methods based on latent variable models: a comparative study. Chemometr. Intell. Lab. Syst. 187: 41–52, https://doi.org/10.1016/j.chemolab.2019.02.006.Search in Google Scholar

Wamba, S.F., Akter, S., Edwards, A., Chopin, G., and Gnanzou, D. (2015). How ‘big data’can make big impact: findings from a systematic review and a longitudinal case study. Int. J. Prod. Econ. 165: 234–246, https://doi.org/10.1016/j.ijpe.2014.12.031.Search in Google Scholar

Wang, J.P., Zhang, W.S., Shi, Y.K., Duan, S.H., and Liu, J. (2018). Industrial big data analytics: challenges, methodologies, and applications. Submitted to IEEE Trans. Autom. Sci. Eng., arXiv:1807.01016v2.Search in Google Scholar

Wang, L. and Alexander, C.A. (2015). Big data in design and manufacturing engineering. Am. J. Eng. Appl. Sci. 8: 223, https://doi.org/10.3844/ajeassp.2015.223.232.Search in Google Scholar

Wang, P., Chaudhry, S., Li, L., Li, S., Tryfonas, T., and Li, H. (2016). The Internet of Things: a security point of view. Internet Res. 26: 337–359, https://doi.org/10.1108/IntR-07-2014-0173.Search in Google Scholar

Ward, J.S. and Barker, A. (2013). Undefined by data: a survey of big data definitions, ArXiv, abs/1309.5821, https://arxiv.org/pdf/1309.5821.pdf.Search in Google Scholar

White, D. (2016). Big data: what is it? CEP Magazine: 33–35, https://www.aiche.org/resources/publications/cep/2016/march/big-data-what-it.Search in Google Scholar

Wu, D., Sakr, S., and Zhu, L. (2017). Big data storage and data models. In: Handbook of big data technologies. Cham: Springer, pp. 3–29.10.1007/978-3-319-49340-4_1Search in Google Scholar

Wu, H. and Zhao, J. (2018). Deep convolutional neural network model based chemical process fault diagnosis. Comput. Chem. Eng. 115: 185–197, https://doi.org/10.1016/j.compchemeng.2018.04.009.Search in Google Scholar

Xie, D. and Bai, L. (2015). A hierarchical deep neural network for fault diagnosis on Tennessee-Eastman process. In: IEEE 14th international conference on machine learning and applications (ICMLA), pp. 745–748, https://doi.org/10.1109/ICMLA.2015.208.Search in Google Scholar

Yan, W., Tang, D., and Lin, Y. (2016). A data-driven soft sensor modeling method based on deep learning and its application. IEEE Trans. Ind. Electron. 64: 4237–4245, https://doi.org/10.1109/TIE.2016.2622668.Search in Google Scholar

Yao, L. and Ge, Z. (2017). Deep learning of semisupervised process data with hierarchical extreme learning machine and soft sensor application. IEEE Trans. Ind. Electron. 65: 1490–1498, https://doi.org/10.1109/TIE.2017.2733448.Search in Google Scholar

Yaqoob, I., Ahmed, E., Hashem, I.A.T., Ahmed, A.I.A., Gani, A., Imran, M., and Guizani, M. (2017). Internet of things architecture: recent advances, taxonomy, requirements, and open challenges. IEEE Wirel. Commun. 24: 10–16, https://doi.org/10.1109/MWC.2017.1600421.Search in Google Scholar

Yin, S., Gao, H., Qiu, J., and Kaynak, O. (2016). Fault detection for nonlinear process with deterministic disturbances: a just-in-time learning based data driven method. IEEE Trans. Cybern. 47: 3649–3657, https://doi.org/10.1109/TCYB.2016.2574754.Search in Google Scholar PubMed

Yuan, X., Ge, Z., Ye, L., and Song, Z. (2016). Supervised neighborhood preserving embedding for feature extraction and its application for soft sensor modeling. J. Chemometr. 30: 430–441, https://doi.org/10.1002/cem.2811.Search in Google Scholar

Zaki, M.J., and Ho, C.T. (Eds.) (2000). Large-scale parallel data mining (No. 1759). Springer Science & Business Media.10.1007/3-540-46502-2Search in Google Scholar

Zhang, H., Tian, X., Deng, X., and Cao, Y. (2018a). Batch process fault detection and identification based on discriminant global preserving kernel slow feature analysis. ISA Trans. 79: 108–126, https://doi.org/10.1016/j.isatra.2018.05.005.Search in Google Scholar PubMed

Zhang, Y., Huang, T., and Bompard, E.F. (2018b). Big data analytics in smart grids: a review. Energy Informatics 1: 8, https://doi.org/10.1186/s42162-018-0007-5.Search in Google Scholar

Zhang, Z. and Zhao, J. (2017). A deep belief network based fault diagnosis model for complex chemical processes. Comput. Chem. Eng. 107: 395–407, https://doi.org/10.1016/j.compchemeng.2017.02.041.Search in Google Scholar

Zhang, Z., Schott, J.A., Liu, M., Chen, H., Lu, X., Sumpter, B.G., and Dai, S. (2019). Prediction of carbon dioxide adsorption via deep learning. Angew. Chem. Int. Ed. 57: 1–6, https://doi.org/10.1002/anie.201812363.Search in Google Scholar PubMed

Zhao, C., Ren, L., Laili, Y., and Lai, L. (2018). An architecture of knowledge cloud based on manufacturing big data. In: IECON 2018-44th annual conference of the IEEE industrial electronics society, pp. 4176–4180, https://doi.org/10.1109/IECON.2018.8591529.Search in Google Scholar

Zhaomin, L., Qingchao, J., and Xuefeng, Y. (2014). Batch process monitoring based on multisubspace multiway principal component analysis and time-series Bayesian inference. Ind. Eng. Chem. Res. 53: 6457–6466, https://doi.org/10.1021/ie403576c.Search in Google Scholar

Zhou, T., Song, S., and Sundmacher, K. (2019). Big data creates new opportunities for materials research: a review on methods and applications of machine learning for materials design. Engineering 5: 1017–1026, https://doi.org/10.1016/j.eng.2019.02.011.Search in Google Scholar

Zhou, Z.H., Chawla, N.V., Jin, Y., and Williams, G.J. (2014). Big data opportunities and challenges: discussions from data analytics perspectives [discussion forum]. IEEE Comput. Intell. Mag. 9: 62–74, https://doi.org/10.1109/MCI.2014.2350953.Search in Google Scholar

Zhu, W., Ma, Y., Benton, M.G., Romagnoli, J.A., and Zhan, Y. (2019). Deep learning for pyrolysis reactor monitoring: from thermal imaging toward smart monitoring system. AIChE J. 65: 582–591, https://doi.org/10.1002/aic.16452.Search in Google Scholar