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Annual Review of Chemical and Biomolecular Engineering

Volume 8, 2017

Big Data Analytics in Chemical Engineering

Abstract

Big data analytics is the journey to turn data into insights for more informed business and operational decisions. As the chemical engineering community is collecting more data (volume) from different sources (variety), this journey becomes more challenging in terms of using the right data and the right tools (analytics) to make the right decisions in real time (velocity). This article highlights recent big data advancements in five industries, including chemicals, energy, semiconductors, pharmaceuticals, and food, and then discusses technical, platform, and culture challenges. To reach the next milestone in multiplying successes to the enterprise level, government, academia, and industry need to collaboratively focus on workforce development and innovation.

Keyword(s): big data analytics, data-driven modeling, Industry 4.0, Internet of things, machine learning
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/content/journals/10.1146/annurev-chembioeng-060816-101555
2017-06-07
2024-04-19
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Literature Cited

  1. Podesta J, Pritzker P, Moniz EJ, Holdren J, Zients J. 1.  2014. Big data: seizing opportunities, preserving values. White House Rep., Washington, DC
  2. Marx V. 2.  2013. Biology: the big challenges of big data. Nature 498:7453255–60 [Google Scholar]
  3. 3. Sci. Staff. 2011. Introduction: dealing with data: challenges and opportunities. Science 331:6018692–93 [Google Scholar]
  4. 4. Nat. Neurosci. Staff. 2014. Focus on big data. Nat. Neurosci. 17:111429 [Google Scholar]
  5. Martens H. 5.  2015. Quantitative big data: where chemometrics can contribute. J. Chemom. 29:11563–81 [Google Scholar]
  6. Shi Y. 6.  2014. Editor's note: a global view of big data. Bridge 44:6–12 [Google Scholar]
  7. Qin SJ. 7.  2014. Process data analytics in the era of big data. AIChE J 60:93092–100 [Google Scholar]
  8. White D. 8.  2016. Big data: What is it?. CEP Magazine March 33–35
  9. García-Muñoz S, MacGregor JF. 9.  2016. Big data: success stories in the process industries. CEP Magazine March 36–40
  10. Colegrove LF, Seasholtz MB, Khare C. 10.  2016. Big data: getting started on the journey. CEP Magazine March 41–45
  11. Reis MS, Braatz RD, Chiang LH. 11.  2016. Big data: challenges and future research directions. CEP Magazine March 46–50
  12. Laney D. 12.  2001. 3D data management: controlling data volume, velocity, and variety. META Delta Feb. 6. http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
  13. 13. Wikipedia.org. 2016. Big Data https://en.wikipedia.org/wiki/Big_data
  14. Westerman A, Morawietz M, Geissbauer R, Vedso J, Schrauf S. 14.  2016. Industry 4.0: building the digital enterprise. PWC Glob. Ind. 4.0 Surv. https://www.pwc.com/gx/en/industries/industry-4.0.html
  15. Qin SJ. 15.  2012. Survey on data-driven industrial process monitoring and diagnosis. Annu. Rev. Control 36:220–34 [Google Scholar]
  16. Colegrove L. 16.  2015. Data initiative improves insights. Chemical Processing March 12
  17. Chiang LH, Russell EL, Braatz RD. 17.  2012. Fault Detection and Diagnosis in Industrial Systems. London: Springer
  18. Qin SJ. 18.  2012. Survey on data-driven industrial process monitoring and diagnosis. Annu. Rev. Control 36:2220–34 [Google Scholar]
  19. Severson K, Chaiwatanodom P, Braatz RD. 19.  2015. Perspectives on process monitoring of industrial systems. IFAC-PapersOnLine 48:21931–39 [Google Scholar]
  20. Yu J, Qin SJ. 20.  2008. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models. AIChE J 54:71811–29 [Google Scholar]
  21. Liu J. 21.  2007. On-line soft sensor for polyethylene process with multiple production grades. Control Eng. Pract. 15:7769–78 [Google Scholar]
  22. Liu Y, Zhang Z, Chen J. 22.  2015. Ensemble local kernel learning for online prediction of distributed product outputs in chemical processes. Chem. Eng. Sci. 137:140–51 [Google Scholar]
  23. Jin H, Chen X, Yang J, Wu L. 23.  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 [Google Scholar]
  24. Liu J, Han C, McAdon M, Goss J, Andrews K. 24.  2015. High throughput development of one carbon molecular sieve for many gas separations. Microporous Mesoporous Mater 206:207–16 [Google Scholar]
  25. Downs JJ, Vogel EF. 25.  1993. A plant-wide industrial process control problem. Comput. Chem. Eng. 17:3245–55 [Google Scholar]
  26. Chiang LH, Colegrove LF. 26.  2007. Industrial implementation of on-line multivariate quality control. Chemom. Intell. Lab. Syst. 88:2143–53 [Google Scholar]
  27. Lu B, Castillo I, Chiang L, Edgar TF. 27.  2014. Industrial PLS model variable selection using moving window variable importance in projection. Chemom. Intell. Lab. Syst. 135:90–109 [Google Scholar]
  28. Kordon A, Chiang L, Stefanov Z, Castillo I. 28.  2014. Consider robust inferential sensors. Chemical Processing Oct. 2
  29. Kadlec P, Gabrys B, Strandt S. 29.  2009. Data-driven soft sensors in the process industry. Comput. Chem. Eng. 33:4795–814 [Google Scholar]
  30. Kano M, Fujiwara K. 30.  2013. Virtual sensing technology in process industries: trends and challenges revealed by recent industrial applications. J. Chem. Eng. Jpn. 46:11–17 [Google Scholar]
  31. Lee JH, Lee JM. 31.  2014. Progress and challenges in control of chemical processes. Annu. Rev. Chem. Biomol. Eng. 5:383–404 [Google Scholar]
  32. Bauer M, Horch A, Xie L, Jelali M, Thornhill N. 32.  2016. The current state of control loop performance monitoring—a survey of application in industry. J. Process Control 38:1–10 [Google Scholar]
  33. Paulonis MA, Cox JW. 33.  2003. A practical approach for large-scale controller performance assessment, diagnosis, and improvement. J. Process Control 13:2155–68 [Google Scholar]
  34. Starr KD, Petersen H, Bauer M. 34.  2016. Control loop performance monitoring—ABB's experience over two decades. IFAC-PapersOnLine 49:7526–32 [Google Scholar]
  35. Chioua M, Bauer M, Chen S-L, Schlake JC, Sand G. 35.  et al. 2016. Plant-wide root cause identification using plant key performance indicators (KPIs) with application to a paper machine. Control Eng. Pract. 49:149–58 [Google Scholar]
  36. Yuan T, Qin SJ. 36.  2014. Root cause diagnosis of plant-wide oscillations using Granger causality. J. Process Control 24:2450–59 [Google Scholar]
  37. Rato TJ, Rendall R, Gomes V, Chin S-T, Chiang LH. 37.  et al. 2016. A systematic methodology for comparing batch process monitoring methods: part I—assessing detection strength. Ind. Eng. Chem. Res. 55:185342–58 [Google Scholar]
  38. Chiang LH, Leardi R, Pell RJ, Seasholtz MB. 38.  2006. Industrial experiences with multivariate statistical analysis of batch process data. Chemom. Intell. Lab. Syst. 81:2109–19 [Google Scholar]
  39. Kourti T. 39.  2003. Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions. J. Chemom. 17:193–109 [Google Scholar]
  40. Kassidas A, MacGregor JF, Taylor PA. 40.  1998. Synchronization of batch trajectories using dynamic time warping. AIChE J 44:4864–75 [Google Scholar]
  41. Yao Y, Gao F. 41.  2009. A survey on multistage/multiphase statistical modeling methods for batch processes. Annu. Rev. Control 33:2172–83 [Google Scholar]
  42. Lennox B, Hiden HG, Montague GA, Kornfeld G, Goulding PR. 42.  2000. Application of multivariate statistical process control to batch operations. Comput. Chem. Eng. 24:2–7291–96 [Google Scholar]
  43. Rao M, Corbin J, Wang Q. 43.  1993. Soft sensors for quality prediction in batch chemical pulping processes. Proc. Int. Symp. Intell. Control150–55 New York: IEEE [Google Scholar]
  44. Peil KP, Neithamer DR, Patrick DW, Wilson BE, Tucker CJ. 44.  2004. Applications of high throughput research at the Dow Chemical Company. Macromol. Rapid Commun. 25:1119–26 [Google Scholar]
  45. Boussie TR, Diamond GM, Goh C, Hall KA, LaPointe AM. 45.  et al. 2003. A fully integrated high-throughput screening methodology for the discovery of new polyolefin catalysts: discovery of a new class of high temperature single-site group (IV) copolymerization catalysts. J. Am. Chem. Soc. 125:144306–17 [Google Scholar]
  46. Mohler CE, Kuhlman RL, Witham CA, Poindexter MK. 46.  2011. Development of high-performance drilling fluids using high-throughput methods. Presented at AADE Natl. Tech. Conf. Exhib., April 12–14, Houston, TX
  47. Wassick JM, Agarwal A, Akiya N, Ferrio J, Bury S, You F. 47.  2012. Addressing the operational challenges in the development, manufacture, and supply of advanced materials and performance products. Comput. Chem. Eng. 47:157–69 [Google Scholar]
  48. Nie Y, Biegler LT, Villa CM, Wassick JM. 48.  2015. Discrete time formulation for the integration of scheduling and dynamic optimization. Ind. Eng. Chem. Res. 54:164303–15 [Google Scholar]
  49. Nie Y, Biegler LT, Wassick JM. 49.  2012. Integrated scheduling and dynamic optimization of batch processes using state equipment networks. AIChE J 58:113416–32 [Google Scholar]
  50. Chu Y, You F, Wassick JM, Agarwal A. 50.  2015. Integrated planning and scheduling under production uncertainties: bi-level model formulation and hybrid solution method. Comput. Chem. Eng. 72:255–72 [Google Scholar]
  51. Krumeich J, Werth D, Loos P, Jacobi S. 51.  2014. Advanced planning and control of manufacturing processes in steel industry through big data analytics case study and architecture proposal. Proc. 2nd IEEE Int. Conf. Big Data, Oct. 27–30, Washington, DC16–24 New York: IEEE [Google Scholar]
  52. Chang A-F, Pashikanti K, Liu YA. 52.  2013. Refinery Engineering: Integrated Process Modeling and Optimization. Hoboken, NJ: John Wiley & Sons
  53. Karuppiah R, Furman KC, Grossmann IE. 53.  2008. Global optimization for scheduling refinery crude oil operations. Comput. Chem. Eng. 32:112745–66 [Google Scholar]
  54. Méndez CA, Grossmann IE, Harjunkoski I, Kaboré P. 54.  2006. A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations. Comput. Chem. Eng. 30:4614–34 [Google Scholar]
  55. Amaran S, Zhang T, Sahinidis NV, Sharda B, Bury SJ. 55.  2016. Medium-term maintenance turnaround planning under uncertainty for integrated chemical sites. Comput. Chem. Eng. 84:422–33 [Google Scholar]
  56. Chiang LH, Jiang B, Zhu X, Huang D, Braatz RD. 56.  2015. Diagnosis of multiple and unknown faults using the causal map and multivariate statistics. J. Process Control 28:27–39 [Google Scholar]
  57. Kumar A, Baldea M, Edgar TF. 57.  2016. Real-time optimization of an industrial steam-methane reformer under distributed sensing. Control Eng. Pract. 54:140–53 [Google Scholar]
  58. Davis J, Edgar T, Graybill R, Korambath P, Schott B. 58.  et al. 2015. Smart manufacturing. Annu. Rev. Chem. Biomol. Eng. 6:141–60 [Google Scholar]
  59. Davis C, Bollinger LA, Dijkema GPJ. 59.  2016. The state of the states: data-driven analysis of the US clean power plan. Renew. Sustain. Energy Rev. 60:631–52 [Google Scholar]
  60. Suganthi L, Iniyan S, Samuel AA. 60.  2015. Applications of fuzzy logic in renewable energy systems—a review. Renew. Sustain. Energy Rev. 48:585–607 [Google Scholar]
  61. Colak I, Sagiroglu S, Yesilbudak M. 61.  2012. Data mining and wind power prediction: a literature review. Renew. Energy 46:241–47 [Google Scholar]
  62. Wang X, Palazoglu A, El-Farra NH. 62.  2015. Operational optimization and demand response of hybrid renewable energy systems. Appl. Energy 143:324–35 [Google Scholar]
  63. Draxl C, Clifton A, Hodge B-M, McCaa J. 63.  2015. The Wind Integration National Dataset (WIND) Toolkit. Appl. Energy 151:355–66 [Google Scholar]
  64. Weitemeyer S, Kleinhans D, Vogt T, Agert C. 64.  2015. Integration of renewable energy sources in future power systems: the role of storage. Renew. Energy 75:14–20 [Google Scholar]
  65. Zhou K, Fu C, Yang S. 65.  2016. Big data driven smart energy management: from big data to big insights. Renew. Sustain. Energy Rev. 56:215–25 [Google Scholar]
  66. Diamantoulakis PD, Kapinas VM, Karagiannidis GK. 66.  2015. Big data analytics for dynamic energy management in smart grids. Big Data Res 2:394–101 [Google Scholar]
  67. Ludwig N, Feuerriegel S, Neumann D. 67.  2015. Putting big data analytics to work: feature selection for forecasting electricity prices using the lasso and random forests. J. Decis. Syst. 24:119–36 [Google Scholar]
  68. Javed F, Arshad N, Wallin F, Vassileva I, Dahlquist E. 68.  2012. Forecasting for demand response in smart grids: an analysis on use of anthropologic and structural data and short term multiple loads forecasting. Appl. Energy 96:150–60 [Google Scholar]
  69. Hare J, Shi X, Gupta S, Bazzi A. 69.  2016. Fault diagnostics in smart micro-grids: a survey. Renew. Sustain. Energy Rev. 60:1114–24 [Google Scholar]
  70. Rhodes JD, Upshaw CR, Harris CB, Meehan CM, Walling DA. 70.  et al. 2014. Experimental and data collection methods for a large-scale smart grid deployment: methods and first results. Energy 65:462–71 [Google Scholar]
  71. Aslam W, Soban M, Akhtar F, Zaffar NA. 71.  2015. Smart meters for industrial energy conservation and efficiency optimization in Pakistan: scope, technology and applications. Renew. Sustain. Energy Rev. 44:933–43 [Google Scholar]
  72. Hansen TM, Suryanarayanan S, Maciejewski AA, Siegel HJ, Modali AV. 72.  2015. A visualization aid for demand response studies in the smart grid. Electr. J. 28:3100–11 [Google Scholar]
  73. Kavousian A, Rajagopal R, Fischer M. 73.  2015. Ranking appliance energy efficiency in households: utilizing smart meter data and energy efficiency frontiers to estimate and identify the determinants of appliance energy efficiency in residential buildings. Energy Build 99:220–30 [Google Scholar]
  74. Weron R. 74.  2014. Electricity price forecasting: a review of the state-of-the-art with a look into the future. Int. J. Forecast. 30:41030–81 [Google Scholar]
  75. Zhou K, Yang S. 75.  2016. Understanding household energy consumption behavior: the contribution of energy big data analytics. Renew. Sustain. Energy Rev. 56:810–19 [Google Scholar]
  76. Leon-Garcia A. 76.  2010. Price prediction in real-time electricity. IEEE Trans. Smart Grid 1:2120–33 [Google Scholar]
  77. Chou J-S, Ngo N-T. 77.  2016. Smart grid data analytics framework for increasing energy savings in residential buildings. Autom. Constr. 72:3247–57 [Google Scholar]
  78. Kusiak A, Li M, Zhang Z. 78.  2010. A data-driven approach for steam load prediction in buildings. Appl. Energy 87:3925–33 [Google Scholar]
  79. Oldewurtel F, Parisio A, Jones CN, Gyalistras D, Gwerder M. 79.  et al. 2012. Use of model predictive control and weather forecasts for energy efficient building climate control. Energy Build 45:15–27 [Google Scholar]
  80. Moreno MV, Dufour L, Skarmeta AF, Jara AJ, Genoud D. 80.  et al. 2016. Big data: the key to energy efficiency in smart buildings. Soft Comput 20:51749–62 [Google Scholar]
  81. Goiri Í, Haque ME, Le K, Beauchea R, Nguyen TD. 81.  et al. 2015. Matching renewable energy supply and demand in green datacenters. Ad Hoc Netw 25:520–34 [Google Scholar]
  82. Rong H, Zhang H, Xiao S, Li C, Hu C. 82.  2016. Optimizing energy consumption for data centers. Renew. Sustain. Energy Rev. 58:674–91 [Google Scholar]
  83. Qin SJ, Cherry G, Good R, Wang J, Harrison CA. 83.  2006. Semiconductor manufacturing process control and monitoring: a fab-wide framework. J. Process Control 16:179–91 [Google Scholar]
  84. May GS, Spanos CJ. 84.  2006. Fundamentals of Semiconductor Manufacturing and Process Control. Hoboken, NJ: Wiley-Intersci305 pp.
  85. Wang Y, Gao F, Doyle FJ. 85.  2009. Survey on iterative learning control, repetitive control, and run-to-run control. J. Process Control 19:101589–600 [Google Scholar]
  86. Chien CF, Chuang SC. 86.  2014. A framework for root cause detection of sub-batch processing system for semiconductor manufacturing big data analytics. IEEE Trans. Semicond. Manuf. 27:4475–88 [Google Scholar]
  87. Lu B, Stuber J, Edgar TF. 87.  2014. Integrated online virtual metrology and fault detection in plasma etch tools. Ind. Eng. Chem. Res. 53:135172–81 [Google Scholar]
  88. Khan AA, Moyne JR, Tilbury DM. 88.  2008. Virtual metrology and feedback control for semiconductor manufacturing processes using recursive partial least squares. J. Process Control 18:10961–74 [Google Scholar]
  89. Moyne J. 89.  2004. Making the move to fab-wide apc. Solid State Technol 47:947–52 [Google Scholar]
  90. Chien C-F, Wang W-C, Cheng J-C. 90.  2007. Data mining for yield enhancement in semiconductor manufacturing and an empirical study. Expert Syst. Appl. 33:192–98 [Google Scholar]
  91. Hsu SC, Chien CF. 91.  2007. Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing. Int. J. Prod. Econ. 107:188–103 [Google Scholar]
  92. Su AJ, Yu CC, Ogunnaike BA. 92.  2008. On the interaction between measurement strategy and control performance in semiconductor manufacturing. J. Process Control 18:3–4266–76 [Google Scholar]
  93. Chien CF, Hsu CY, Chen PN. 93.  2013. Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence. Flex. Serv. Manuf. J. 25:3367–88 [Google Scholar]
  94. Moyne J, Schulze B. 94.  2010. Yield management enhanced advanced process control system (YMeAPC)—part I: description and case study of feedback for optimized multiprocess control. IEEE Trans. Semicond. Manuf. 23:2221–35 [Google Scholar]
  95. Kuo Y, Yang T, Peters BA, Chang I. 95.  2007. Simulation metamodel development using uniform design and neural networks for automated material handling systems in semiconductor wafer fabrication. Simul. Model. Pract. Theory 15:81002–15 [Google Scholar]
  96. Moyne J, Samantaray J, Armacost M. 96.  2016. Big data capabilities applied to semiconductor manufacturing advanced process control. IEEE Trans. Semicond. Manuf. 29:4283–91 [Google Scholar]
  97. Tsuda T, Inoue S, Kayahara A, Imai S, Tanaka T. 97.  et al. 2015. Advanced semiconductor manufacturing using big data. IEEE Trans. Semicond. Manuf. 28:3229–35 [Google Scholar]
  98. Jianbo Y. 98.  2012. Semiconductor manufacturing process monitoring using Gaussian mixture model and Bayesian method with local and nonlocal information. Semicond. Manuf. IEEE Trans. 25:3480–93 [Google Scholar]
  99. Fan S-KS, Chang Y-J. 99.  2013. An integrated advanced process control framework using run-to-run control, virtual metrology and fault detection. J. Process Control 23:7933–42 [Google Scholar]
  100. Kim D, Kang P, Lee S-k, Kang S, Doh S, Cho S. 100.  2015. Improvement of virtual metrology performance by removing metrology noises in a training dataset. Pattern Anal. Appl. 18:1173–89 [Google Scholar]
  101. Macher JT, Mowery DC. 101.  2003. “Managing” learning by doing: an empirical study in semiconductor manufacturing. J. Prod. Innov. Manag. 20:5391–410 [Google Scholar]
  102. Cattell J, Chilukuri S, Levy M. 102.  2013. How big data can revolutionize pharmaceutical R & D. White Pap., McKinsey & Co., New York. http://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/how-big-data-can-revolutionize-pharmaceutical-r-and-d
  103. Sliwoski G, Kothiwale S, Meiler J, Lowe EW. 103.  2014. Computational methods in drug discovery. Pharmacol. Rev. 66:1334–95 [Google Scholar]
  104. Kuhn M, Campillos M, González P, Jensen LJ, Bork P. 104.  2008. Large-scale prediction of drug-target relationships. FEBS Lett 582:81283–90 [Google Scholar]
  105. Klipp E, Wade RC, Kummer U. 105.  2010. Biochemical network-based drug-target prediction. Curr. Opin. Biotechnol. 21:4511–16 [Google Scholar]
  106. Alaimo S, Pulvirenti A, Giugno R, Ferro A. 106.  2013. Drug-target interaction prediction through domain-tuned network-based inference. Bioinformatics 29:162004–8 [Google Scholar]
  107. Cheng F, Liu C, Jiang J, Lu W, Li W. 107.  et al. 2012. Prediction of drug-target interactions and drug repositioning via network-based inference. PLOS Comput. Biol. 8:5e1002503 [Google Scholar]
  108. Koutsoukas A, Simms B, Kirchmair J, Bond PJ, Whitmore AV. 108.  et al. 2011. From in silico target prediction to multi-target drug design: current databases, methods and applications. J. Proteom. 74:2554–74 [Google Scholar]
  109. Perlman L, Gottlieb A, Atias N, Ruppin E, Sharan R. 109.  2011. Combining drug and gene similarity measures for drug-target elucidation. J. Comput. Biol. 18:2133–45 [Google Scholar]
  110. Tothill RW, Tinker AV, George J, Brown R, Fox SB. 110.  et al. 2008. Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome. Clin. Cancer Res. 14:165198–208 [Google Scholar]
  111. Ernst J, Kellis M. 111.  2012. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9:3215–16 [Google Scholar]
  112. Halpern Y, Choi Y, Horng S, Sontag D. 112.  2014. Using anchors to estimate clinical state without labeled data. AMIA Annu. Symp. Proc. 2014:606–15 [Google Scholar]
  113. Halpern Y, Horng S, Choi Y, Sontag D. 113.  2016. Electronic medical record phenotyping using the anchor and learn framework. J. Am. Med. Inform. Assoc 23731–40 [Google Scholar]
  114. Cheng C, Gerstein M. 114.  2012. Modeling the relative relationship of transcription factor binding and histone modifications to gene expression levels in mouse embryonic stem cells. Nucleic Acids Res 40:2553–68 [Google Scholar]
  115. Yu LX. 115.  2008. Pharmaceutical quality by design: product and process development, understanding, and control. Pharm. Res. 25:4781–91 [Google Scholar]
  116. Boukouvala F, Muzzio FJ, Ierapetritou MG. 116.  2011. Dynamic data-driven modeling of pharmaceutical processes. Ind. Eng. Chem. Res. 50:116743–54 [Google Scholar]
  117. Eberle L, Sugiyama H, Papadokonstantakis S, Graser A, Schmidt R, Hungerbühler K. 117.  2016. Data-driven tiered procedure for enhancing yield in drug product manufacturing. Comput. Chem. Eng. 87:82–94 [Google Scholar]
  118. Muteki K, Yamamoto K, Reid GL, Krishnan M. 118.  2011. De-risking scale-up of a high shear wet granulation process using latent variable modeling and near-infrared spectroscopy. J. Pharm. Innov. 6:3142–56 [Google Scholar]
  119. Severson K, VanAntwerp JG, Natarajan V, Antoniou C, Thömmes J, Braatz RD. 119.  2015. Elastic net with Monte Carlo sampling for data-based modeling in biopharmaceutical manufacturing facilities. Comput. Chem. Eng. 80:30–36 [Google Scholar]
  120. Ahn Y-Y, Ahnert SE, Bagrow JP, Barabási A-L. 120.  2011. Flavor network and the principles of food pairing. Sci. Rep. 1:196 [Google Scholar]
  121. Pinel F, Varshney LR. 121.  2014. Computational creativity for culinary recipes. Proc. Ext. Abstr. 32nd Annu. ACM Conf. Hum. Factors Comput. Syst.439–42 New York: Assoc. Comput. Mach. [Google Scholar]
  122. Nordén B, Broberg P, Lindberg C, Plymoth A. 122.  2005. Analysis and understanding of high-dimensionality data by means of multivariate data analysis. Chem. Biodivers. 2:111487–94 [Google Scholar]
  123. Karp NA, Spencer M, Lindsay H, O'Dell K, Lilley KS. 123.  2005. Impact of replicate types on proteomic expression analysis. J. Proteome Res. 4:51867–71 [Google Scholar]
  124. Pedreschi R, Hertog M, Lilley KS, Nicolai B. 124.  2010. Proteomics for the food industry: opportunities and challenges. Crit. Rev. Food Sci. Nutr. 50:7680–92 [Google Scholar]
  125. Piras C, Roncada P, Rodrigues PM, Bonizzi L, Soggiu A. 125.  2016. Proteomics in food: quality, safety, microbes, and allergens. Proteomics 16:5799–815 [Google Scholar]
  126. Dajana Gaso-Soka, Spomenka Kova DJ. 126.  2010. Application of proteomics in food technology and food biotechnology: process development, quality control and product safety. Food Technol. Biotechnol. 48:3284–95 [Google Scholar]
  127. Griffiths PR. 127.  2006. Introduction to vibrational spectroscopy. Handbook of Vibrational Spectroscopy JM Chalmers, PR Griffiths Hoboken, NJ: Wiley4000 pp. [Google Scholar]
  128. Guillen MD, Cabo N. 128.  1997. Infrared spectroscopy in the study of edible oils and fats. J. Sci. Food Agric. 75:1 [Google Scholar]
  129. Rodriguez-Saona LE, Allendorf ME. 129.  2011. Use of FTIR for rapid authentication and detection of adulteration of food. Annu. Rev. Food Sci. Technol. 2:1467–83 [Google Scholar]
  130. Cozzolino D, Holdstock M, Dambergs RG, Cynkar WU, Smith PA. 130.  2009. Mid infrared spectroscopy and multivariate analysis: a tool to discriminate between organic and non-organic wines grown in Australia. Food Chem 116:3761–65 [Google Scholar]
  131. Sivakesava S, Irudayaraj J. 131.  2002. Classification of simple and complex sugar adulterants in honey by mid-infrared spectroscopy. Int. J. Food Sci. Technol. 37:4351–60 [Google Scholar]
  132. Sivakesava S, Irudayaraj J. 132.  2002. Rapid determination of tetracycline in milk by FT-MIR and FT-NIR spectroscopy. J. Dairy Sci. 85:3487–93 [Google Scholar]
  133. Che Man YB, Syahariza ZA, Mirghani MES, Jinap S, Bakar J. 133.  2005. Analysis of potential lard adulteration in chocolate and chocolate products using Fourier transform infrared spectroscopy. Food Chem 90:815–19 [Google Scholar]
  134. Gurdeniz G, Ozen B. 134.  2009. Detection of adulteration of extra-virgin olive oil by chemometric analysis of mid-infrared spectral data. Food Chem 116:2519–25 [Google Scholar]
  135. McCarthy J, Barr D, Sinclair A. 135.  2008. Determination of trans fatty acid levels by FTIR in processed foods in Australia. Asia Pac. J. Clin. Nutr. 17:3391–96 [Google Scholar]
  136. Du C-J, Sun D-W. 136.  2004. Recent developments in the applications of image processing techniques for food quality evaluation. Trends Food Sci. Technol. 15:5230–49 [Google Scholar]
  137. Li Q, Wang M, Gu W. 137.  2002. Computer vision based system for apple surface defect detection. Comput. Electron. Agric. 36:215–23 [Google Scholar]
  138. Leemans V, Magein H, Destain MF. 138.  1999. Defect segmentation on “jonagold” apples using colour vision and a Bayesian classification method. Comput. Electron. Agric. 23:143–53 [Google Scholar]
  139. Brøndum J, Egebo M, Agerskov C, Busk H. 139.  1998. On-line pork carcass grading with the Autofom ultrasound system. J. Anim. Sci. 76:71859–68 [Google Scholar]
  140. Fernández C, Gallego L, Quintanilla A. 140.  1997. Lamb fat thickness and longissimus muscle area measured by a computerized ultrasonic system. Small Rumin. Res. 26:3277–82 [Google Scholar]
  141. Du CJ, Sun DW. 141.  2006. Learning techniques used in computer vision for food quality evaluation: a review. J. Food Eng. 72:139–55 [Google Scholar]
  142. Kumar A, Baldea M, Edgar TF, Ezekoye OA. 142.  2015. Smart manufacturing approach for efficient operation of industrial steam-methane reformers. Ind. Eng. Chem. Res. 54:164360–70 [Google Scholar]
  143. Boukouvala F, Muzzio FJ, Ierapetritou MG. 143.  2010. Predictive modeling of pharmaceutical processes with missing and noisy data. AIChE J 56:112860–72 [Google Scholar]
  144. Ng CW, Hussain MA. 144.  2004. Hybrid neural network—prior knowledge model in temperature control of a semi-batch polymerization process. Chem. Eng. Process. Process Intensif. 43:4559–70 [Google Scholar]
  145. Bennett J, Lanning S. 145.  2007. The Netflix Prize https://www.cs.uic.edu/∼liub/KDD-cup-2007/NetflixPrize-description.pdf
  146. Davenport TH, Patil DJ. 146.  2012. Data scientist: the sexiest job of the 21st century. Harv. Bus. Rev. 90:1071–76 [Google Scholar]
  147. Vesset D, Olofson CW, Nadkarni A, Zaidi A, McDonough B. 147.  et al. 2015. IDC FutureScape: Worldwide big data and analytics 2016 predictions. White Pap., IDC Research, Inc., Framingham, MA. https://www.idc.com/getdoc.jsp?containerId=259835
  148. McKinsey Co. 148.  2011. Big data: the next frontier for innovation, competition, and productivity. Rep. 156, McKinsey Glob. Inst. http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation
  149. 149. Data-Informed.com. 2016. Map of University Programs in Big Data Analytics http://data-informed.com/bigdata_university_map/
  150. 150. Natl. Sci. Found. Critical techniques, technologies and methodologies for advancing foundations and applications of big data sciences and engineering (BIGDATA) Accessed on February 1, 2017. http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504767
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Big Data Analytics in Chemical Engineering
Annual Review of Chemical and Biomolecular Engineering 8, 63 (2017); https://doi.org/10.1146/annurev-chembioeng-060816-101555
/content/journals/10.1146/annurev-chembioeng-060816-101555
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