1932

Abstract

We review the impact of control systems and strategies on the energy efficiency of chemical processes. We show that, in many ways, good control performance is a necessary but not sufficient condition for energy efficiency. The direct effect of process control on energy efficiency is manyfold: Reducing output variability allows for operating chemical plants closer to their limits, where the energy/economic optima typically lie. Further, good control enables novel, transient operating strategies, such as conversion smoothing and demand response. Indirectly, control systems are key to the implementation and operation of more energy-efficient plant designs, as dictated by the process integration and intensification paradigms. These conclusions are supported with references to numerous examples from the literature.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-chembioeng-092319-083227
2020-06-07
2024-04-25
Loading full text...

Full text loading...

/deliver/fulltext/chembioeng/11/1/annurev-chembioeng-092319-083227.html?itemId=/content/journals/10.1146/annurev-chembioeng-092319-083227&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Edgar TF. 2004. Control and operations: When does controllability equal profitability. ? Comput. Chem. Eng. 29:41–49
    [Google Scholar]
  2. 2. 
    Davis J, Edgar T, Porter J, Bernaden J, Sarli M 2012. Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Comput. Chem. Eng. 47:145–56
    [Google Scholar]
  3. 3. 
    Seborg DE, Mellichamp DA, Edgar TF, Doyle FJ III 2010. Process Dynamics and Control Hoboken, NJ: John Wiley & Sons
  4. 4. 
    Skogestad S. 2000. Self-optimizing control: the missing link between steady-state optimization and control. Comput. Chem. Eng. 24:569–75
    [Google Scholar]
  5. 5. 
    de Araújo ACB, Govatsmark M, Skogestad S 2007. Application of plantwide control to the HDA process. I—steady-state optimization and self-optimizing control. Control Eng. Pract. 15:1222–37
    [Google Scholar]
  6. 6. 
    Qin SJ, Badgwell TA. 2003. A survey of industrial model predictive control technology. Control Eng. Pract. 11:733–64
    [Google Scholar]
  7. 7. 
    Rawlings JB, Angeli D, Bates CN 2012. Fundamentals of economic model predictive control. 2012 IEEE 51st IEEE Conference on Decision and Control (CDC)3851–61 Maui, HI: IEEE
    [Google Scholar]
  8. 8. 
    Findeisen R, Allgöwer F. 2002. An introduction to nonlinear model predictive control. Proceedings of the 21st Benelux Meeting on Systems and Control, 11:119–41 Eindhoven, Neth.: Tech. Univ. Eindhoven
    [Google Scholar]
  9. 9. 
    Mesbah A. 2016. Stochastic model predictive control: an overview and perspectives for future research. IEEE Control Syst. Mag. 36:30–44
    [Google Scholar]
  10. 10. 
    Bemporad A, Morari M. 1999. Robust model predictive control: a survey. Robustness in Identification and Control A Garulli, A Tesi, A Vicino 207–26 London: Springer-Verlag
    [Google Scholar]
  11. 11. 
    Cutler CR, Perry R. 1983. Real time optimization with multivariable control is required to maximize profits. Comput. Chem. Eng. 7:663–67
    [Google Scholar]
  12. 12. 
    Marlin TE, Hrymak AN. 1997. Real-time operations optimization of continuous processes. In AIChE Symposium Series 93156–64 New York: Am. Inst. Chem. Eng.
    [Google Scholar]
  13. 13. 
    Froisy JB. 2006. Model predictive control—building a bridge between theory and practice. Comput. Chem. Eng. 30:1426–35
    [Google Scholar]
  14. 14. 
    Kadam J, Marquardt W, Schlegel M, Backx T, Bosgra O et al. 2003. Towards integrated dynamic real-time optimization and control of industrial processes. FOCAPO 2003: A View to the Future Integration of R&D, Manufacturing, and the Global Supply Chain: Fourth International Conference on Foundations of Computer-Aided Process Operations IE Grossmann, CM McDonald 593–96 Austin: CACHE
    [Google Scholar]
  15. 15. 
    Latour P, Sharpe J, Delaney M 1986. Estimating benefits from advanced control. ISA Trans 25:13–21
    [Google Scholar]
  16. 16. 
    Harris TJ. 1989. Assessment of control loop performance. Can. J. Chem. Eng. 67:856–61
    [Google Scholar]
  17. 17. 
    Grossmann I. 2005. Enterprise-wide optimization: a new frontier in process systems engineering. AIChE J 51:1846–57
    [Google Scholar]
  18. 18. 
    Herring H. 2006. Energy efficiency—a critical view. Energy 31:10–20
    [Google Scholar]
  19. 19. 
    Patterson MG. 1996. What is energy efficiency? Concepts, indicators and methodological issues. Energy Policy 24:377–90
    [Google Scholar]
  20. 20. 
    Greening LA, Greene DL, Difiglio C 2000. Energy efficiency and consumption—the rebound effect—a survey. Energy Policy 28:389–401
    [Google Scholar]
  21. 21. 
    Siirola J, Edgar T. 2012. Process energy systems: control, economic, and sustainability objectives. Comput. Chem. Eng. 47:134–44
    [Google Scholar]
  22. 22. 
    Muske KR. 2003. Estimating the economic benefit from improved process control. Ind. Eng. Chem. Res. 42:4535–44
    [Google Scholar]
  23. 23. 
    Huang B, Shah S, Kwok E 1997. Good, bad or optimal? Performance assessment of multivariable processes. Automatica 33:1175–83
    [Google Scholar]
  24. 24. 
    Ko BS, Edgar TF. 2001. Performance assessment of multivariable feedback control systems. Automatica 37:899–905
    [Google Scholar]
  25. 25. 
    Huang B, Shah SL. 1999. Control Loop Performance Assessment: Theory and Applications London: Springer-Verlag
  26. 26. 
    Kadali R, Huang B. 2002. Controller performance analysis with LQG benchmark obtained under closed loop conditions. ISA Trans 41:521–37
    [Google Scholar]
  27. 27. 
    Gao J, Patwardhan R, Akamatsu K, Hashimoto Y, Emoto G et al. 2003. Performance evaluation of two industrial MPC controllers. Control Eng. Pract. 11:1371–87
    [Google Scholar]
  28. 28. 
    O'Dwyer A. 2016. Reducing energy costs by optimizing controller tuning. Proceedings of the 2nd International Conference on Renewable Energy in Maritime Island Climates253–58 Dublin: Dublin Inst. Technol.
    [Google Scholar]
  29. 29. 
    Kumar A, Baldea M, Edgar TF 2016. Real-time optimization of an industrial steam-methane reformer under distributed sensing. Control Eng. Pract. 54:140–53
    [Google Scholar]
  30. 30. 
    Mayne DQ, Rawlings JB, Rao CV, Scokaert PO 2000. Constrained model predictive control: stability and optimality. Automatica 36:789–814
    [Google Scholar]
  31. 31. 
    Shinskey FG. 1978. Energy Conservation Through Control New York: Academic
  32. 32. 
    White DC. 2012. Optimize energy use in distillation. Chem. Eng. Prog. 108:35–41
    [Google Scholar]
  33. 33. 
    Richalet J, Rault A, Testud JL, Papon J 1978. Model predictive heuristic control. Applications to industrial processes. Automatica 14:413–28
    [Google Scholar]
  34. 34. 
    Cutler CR, Ramaker BL. 1980. Dynamic matrix control—a computer control algorithm. Proceedings of the 1980 Joint Automatic Control Conference: An ASME Century 2 Emerging Technology Conference, Aug. 13–15, San Francisco New York: Am. Soc. Mech. Eng.
    [Google Scholar]
  35. 35. 
    Kano M, Ogawa M. 2010. The state of the art in chemical process control in Japan: good practice and questionnaire survey. J. Process Control 20:969–82
    [Google Scholar]
  36. 36. 
    US Energy Inf. Adm 2014. Manufacturing Energy Consumption Survey (MECS) Washington, DC: US Energy Inf. Adm https://www.eia.gov/consumption/manufacturing/data/2014
  37. 37. 
    Ganesh HS, Edgar TF, Baldea M 2016. Model predictive control of the exit part temperature for an austenitization furnace. Processes 4:53
    [Google Scholar]
  38. 38. 
    Worrell E, Martin N, Price L 2000. Potentials for energy efficiency improvement in the US cement industry. Energy 25:1189–214
    [Google Scholar]
  39. 39. 
    Worrell E, Galitsky C, Masanet E, Graus W 2008. Energy efficiency improvement and cost saving opportunities for the glass industry ENERGY STAR Guide Energy Plant Manag., Berkeley Natl. Lab Berkeley, CA:
  40. 40. 
    Richalet J. 1993. Industrial applications of model based predictive control. Automatica 29:1251–74
    [Google Scholar]
  41. 41. 
    Åström KJ, Hägglund T, Hang CC, Ho WK 1993. Automatic tuning and adaptation for PID controllers—a survey. Control Eng. Pract. 1:699–714
    [Google Scholar]
  42. 42. 
    Bauer M, Horch A, Xie L, Jelali M, Thornhill NF 2016. The current state of control loop performance monitoring—a survey of application in industry. J. Process Control 38:1–10
    [Google Scholar]
  43. 43. 
    Thornhill NF, Hägglund T. 1997. Detection and diagnosis of oscillation in control loops. Control Eng. Pract. 5:1343–54
    [Google Scholar]
  44. 44. 
    Thornhill NF, Oettinger M, Fedenczuk P 1999. Refinery-wide control loop performance assessment. J. Process Control 9:109–24
    [Google Scholar]
  45. 45. 
    Desborough L, Miller R. 2001. Increasing customer value of industrial control performance monitoring—Honeywell's experience. In Chemical Process ControlVI: Assessment and New Directions for Research 98:169–189 New York: Am. Inst. Chem. Eng.
    [Google Scholar]
  46. 46. 
    Bonavita N. 2013. Can process automation increase energy efficiency. ? Hydrocarb. Proc. 92:71–75
    [Google Scholar]
  47. 47. 
    Lang L, Montes G, Mahlstadt JR 2011. Better control-loop management lowers energy costs. Hydrocarb. Proc. 90:43–45
    [Google Scholar]
  48. 48. 
    Harris TJ, MacGregor JF, Wright J 1980. Optimal sensor location with an application to a packed bed tubular reactor. AIChE J 26:910–16
    [Google Scholar]
  49. 49. 
    Alonso AA, Kevrekidis IG, Banga JR, Frouzakis CE 2004. Optimal sensor location and reduced order observer design for distributed process systems. Comput. Chem. Eng. 28:27–35
    [Google Scholar]
  50. 50. 
    Doyle FJ III 1998. Nonlinear inferential control for process applications. J. Process Control 8:339–53
    [Google Scholar]
  51. 51. 
    Kadlec P, Gabrys B, Strandt S 2009. Data-driven soft sensors in the process industry. Comput. Chem. Eng. 33:795–814
    [Google Scholar]
  52. 52. 
    Rawlings JB, Amrit R. 2009. Optimizing process economic performance using model predictive control. Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences 384119–38 Berlin: Springer
    [Google Scholar]
  53. 53. 
    Ellis M, Durand H, Christofides PD 2014. A tutorial review of economic model predictive control methods. J. Process Control 24:1156–78
    [Google Scholar]
  54. 54. 
    Amrit R, Rawlings JB, Biegler LT 2013. Optimizing process economics online using model predictive control. Comput. Chem. Eng. 58:334–43
    [Google Scholar]
  55. 55. 
    Gopalakrishnan A, Biegler LT. 2013. Economic nonlinear model predictive control for periodic optimal operation of gas pipeline networks. Comput. Chem. Eng. 52:90–99
    [Google Scholar]
  56. 56. 
    Huang R, Zavala VM, Biegler LT 2009. Advanced step nonlinear model predictive control for air separation units. J. Process Control 19:678–85
    [Google Scholar]
  57. 57. 
    Caspari A, Faust JM, Schäfer P, Mhamdi A, Mitsos A 2018. Economic nonlinear model predictive control for flexible operation of air separation units. IFAC-PapersOnLine 51:295–300
    [Google Scholar]
  58. 58. 
    Wang Y, Puig V, Cembrano G 2017. Non-linear economic model predictive control of water distribution networks. J. Process Control 56:23–34
    [Google Scholar]
  59. 59. 
    Huang R, Harinath E, Biegler LT 2011. Lyapunov stability of economically oriented NMPC for cyclic processes. J. Process Control 21:501–9
    [Google Scholar]
  60. 60. 
    Amrit R, Rawlings JB, Angeli D 2011. Economic optimization using model predictive control with a terminal cost. Annu. Rev. Control 35:178–86
    [Google Scholar]
  61. 61. 
    US Energy Inf. Adm 2020. How much energy is consumed in U.S. residential and commercial buildings? FAQs, US Energy Inf. Adm Washington, DC: https://www.eia.gov/tools/faqs/faq.php
  62. 62. 
    Cole WJ, Powell KM, Edgar TF 2012. Optimization and advanced control of thermal energy storage systems. Rev. Chem. Eng. 28:81–99
    [Google Scholar]
  63. 63. 
    Rawlings JB, Patel NR, Risbeck MJ, Maravelias CT, Wenzel MJ, Turney RD 2018. Economic MPC and real-time decision making with application to large-scale HVAC energy systems. Comput. Chem. Eng. 114:89–98
    [Google Scholar]
  64. 64. 
    Ma J, Qin SJ, Salsbury T, Xu P 2012. Demand reduction in building energy systems based on economic model predictive control. Chem. Eng. Sci. 67:92–100
    [Google Scholar]
  65. 65. 
    Ma J, Qin SJ, Salsbury T 2014. Application of economic MPC to the energy and demand minimization of a commercial building. J. Process Control 24:1282–91
    [Google Scholar]
  66. 66. 
    Bengea SC, Kelman AD, Borrelli F, Taylor R, Narayanan S 2014. Implementation of model predictive control for an HVAC system in a mid-size commercial building. HVAC&R Res 20:121–35
    [Google Scholar]
  67. 67. 
    Touretzky CR, Baldea M. 2014. Nonlinear model reduction and model predictive control of residential buildings with energy recovery. J. Process Control 24:723–39
    [Google Scholar]
  68. 68. 
    Yoon JH, Baldick R, Novoselac A 2014. Dynamic demand response controller based on real-time retail price for residential buildings. IEEE Trans. Smart Grid 5:121–29
    [Google Scholar]
  69. 69. 
    Bhatia T, Biegler LT. 1996. Dynamic optimization in the design and scheduling of multiproduct batch plants. Ind. Eng. Chem. Res. 35:2234–46
    [Google Scholar]
  70. 70. 
    Terrazas-Moreno S, Flores-Tlacuahuac A, Grossmann IE 2007. Simultaneous cyclic scheduling and optimal control of polymerization reactors. AIChE J 53:2301–15
    [Google Scholar]
  71. 71. 
    Prata A, Oldenburg J, Kroll A, Marquardt W 2008. Integrated scheduling and dynamic optimization of grade transitions for a continuous polymerization reactor. Comput. Chem. Eng. 32:463–76
    [Google Scholar]
  72. 72. 
    Du J, Park J, Harjunkoski I, Baldea M 2015. A time scale-bridging approach for integrating production scheduling and process control. Comput. Chem. Eng. 79:59–69
    [Google Scholar]
  73. 73. 
    Costandy JG, Edgar TF, Baldea M 2018. A scheduling perspective on the monetary value of improving process control. Comput. Chem. Eng. 112:121–31
    [Google Scholar]
  74. 74. 
    Prabhu VV, Jeon HW, Taisch M 2013. Simulation modelling of energy dynamics in discrete manufacturing systems. Stud. Comput. Intell. IFAC Proc 45:740–45
    [Google Scholar]
  75. 75. 
    Mouzon G, Yildirim MB, Twomey J 2007. Operational methods for minimization of energy consumption of manufacturing equipment. Int. J. Prod. Res. 45:4247–71
    [Google Scholar]
  76. 76. 
    Rager M, Gahm C, Denz F 2015. Energy-oriented scheduling based on evolutionary algorithms. Comput. Oper. Res. 54:218–31
    [Google Scholar]
  77. 77. 
    Pattison RC, Touretzky CR, Johansson T, Harjunkoski I, Baldea M 2016. Optimal process operations in fast-changing electricity markets: framework for scheduling with low-order dynamic models and an air separation application. Ind. Eng. Chem. Res. 55:4562–84
    [Google Scholar]
  78. 78. 
    Tsay C, Kumar A, Flores-Cerrillo J, Baldea M 2019. Optimal demand response scheduling of an industrial air separation unit using data-driven dynamic models. Comput. Chem. Eng. 126:22–34
    [Google Scholar]
  79. 79. 
    Otashu JI, Baldea M. 2019. Demand response-oriented dynamic modeling and operational optimization of membrane-based chlor-alkali plants. Comput. Chem. Eng. 121:396–408
    [Google Scholar]
  80. 80. 
    Wang X, Palazoglu A, El-Farra NH 2015. Operational optimization and demand response of hybrid renewable energy systems. Appl. Energy 143:324–35
    [Google Scholar]
  81. 81. 
    Otashu JI, Baldea M. 2018. Grid-level “battery” operation of chemical processes and demand-side participation in short-term electricity markets. Appl. Energy 220:562–75
    [Google Scholar]
  82. 82. 
    Tsay C, Baldea M. 2020. Integrating production scheduling and process control using latent variable dynamic models. Control Eng. Pract. 94:104201
    [Google Scholar]
  83. 83. 
    Kelley MT, Pattison RC, Baldick R, Baldea M 2018. An MILP framework for optimizing demand response operation of air separation units. Appl. Energy 222:951–66
    [Google Scholar]
  84. 84. 
    Pattison RC, Touretzky CR, Harjunkoski I, Baldea M 2017. Moving horizon closed-loop production scheduling using dynamic process models. AIChE J 63:639–51
    [Google Scholar]
  85. 85. 
    Huang X, Hong SH, Yu M, Ding Y, Jiang J 2019. Demand response management for industrial facilities: a deep reinforcement learning approach. IEEE Access 7:82194–205
    [Google Scholar]
  86. 86. 
    Zhang X, Hug G, Kolter Z, Harjunkoski I 2015. Industrial demand response by steel plants with spinning reserve provision. Proceedings of the 2015 North American Power Symposium (NAPS)1–6 Piscataway, NJ: IEEE
    [Google Scholar]
  87. 87. 
    Kelley MT, Baldick R, Baldea M 2019. Demand response operation of electricity-intensive chemical processes for reduced greenhouse gas emissions: application to an air separation unit. ACS Sustain. Chem. Eng. 7:1909–22
    [Google Scholar]
  88. 88. 
    Finn P, Fitzpatrick C. 2014. Demand side management of industrial electricity consumption: promoting the use of renewable energy through real-time pricing. Appl. Energy 113:11–21
    [Google Scholar]
  89. 89. 
    Baldea M, Daoutidis P. 2012. Dynamics and Nonlinear Control of Integrated Process Systems Cambridge, UK: Cambridge Univ. Press
  90. 90. 
    Christofides PD, Scattolini R, Muñoz de la Peña D, Liu J 2013. Distributed model predictive control: a tutorial review and future research directions. Comput. Chem. Eng. 51:21–41
    [Google Scholar]
  91. 91. 
    Negenborn RR, Maestre JM. 2014. Distributed model predictive control: an overview and roadmap of future research opportunities. IEEE Control Syst. Mag. 34:87–97
    [Google Scholar]
  92. 92. 
    Stewart BT, Venkat AN, Rawlings JB, Wright SJ, Pannocchia G 2010. Cooperative distributed model predictive control. Syst. Control Lett. 59:460–69
    [Google Scholar]
  93. 93. 
    Tatara E, Çınar A, Teymour F 2007. Control of complex distributed systems with distributed intelligent agents. J. Process Control 17:415–27
    [Google Scholar]
  94. 94. 
    Daoutidis P, Tang W, Jogwar SS 2018. Decomposing complex plants for distributed control: perspectives from network theory. Comput. Chem. Eng. 114:43–51
    [Google Scholar]
  95. 95. 
    Stankiewicz A, Moulijn J. 2000. Process intensification: transforming chemical engineering. Chem. Eng. Prog. 96:22–33
    [Google Scholar]
  96. 96. 
    Tian Y, Demirel SE, Hasan MF, Pistikopoulos EN 2018. An overview of process systems engineering approaches for process intensification: state of the art. Chem. Eng. Process. Process Intensif. 133:160–210
    [Google Scholar]
  97. 97. 
    Baldea M. 2015. From process integration to process intensification. Comput. Chem. Eng. 81:104–14
    [Google Scholar]
  98. 98. 
    Demirel SE, Li J, Hasan MF 2017. Systematic process intensification using building blocks. Comput. Chem. Eng. 105:2–38
    [Google Scholar]
  99. 99. 
    Carrasco JC, Lima FV. 2017. An optimization-based operability framework for process design and intensification of modular natural gas utilization systems. Comput. Chem. Eng. 105:246–58
    [Google Scholar]
  100. 100. 
    Tian Y, Pistikopoulos EN. 2019. Synthesis of operable process intensification systems: advances and challenges. Curr. Opin. Chem. Eng. 25:101–7
    [Google Scholar]
  101. 101. 
    Nikačević NM, Huesman AE, Van den Hof PM, Stankiewicz AI 2012. Opportunities and challenges for process control in process intensification. Chem. Eng. Process. Process Intensif. 52:1–15
    [Google Scholar]
  102. 102. 
    Luyben WL, Yu CC. 2009. Reactive Distillation Design and Control Hoboken, NJ: John Wiley & Sons
  103. 103. 
    Sharma N, Singh K. 2010. Control of reactive distillation column: a review. Int. J. Chem. Reactor Eng. 8 https://doi.org/10.2202/1542-6580.2260
    [Crossref]
  104. 104. 
    Adrian T, Schoenmakers H, Boll M 2004. Model predictive control of integrated unit operations: control of a divided wall column. Chem. Eng. Process. Process Intensif. 43:347–55
    [Google Scholar]
  105. 105. 
    Kiss AA, Bildea CS. 2011. A control perspective on process intensification in dividing-wall columns. Chem. Eng. Process. Process Intensif. 50:281–92
    [Google Scholar]
  106. 106. 
    Donahue MM, Roach BJ, Downs JJ, Blevins T, Baldea M, Eldridge RB 2016. Dividing wall column control: Common practices and key findings. Chem. Eng. Process. Process Intensif. 107:106–15
    [Google Scholar]
  107. 107. 
    Weinfeld JA, Owens SA, Eldridge RB 2018. Reactive dividing wall columns: a comprehensive review. Chem. Eng. Process. Process Intensif. 123:20–33
    [Google Scholar]
  108. 108. 
    Baldea M, Edgar TF. 2018. Dynamic process intensification. Curr. Opin. Chem. Eng. 22:48–53
    [Google Scholar]
  109. 109. 
    Yan L, Edgar TF, Baldea M 2018. Dynamic process intensification of binary distillation via periodic operation. Ind. Eng. Chem. Res. 58:5830–37
    [Google Scholar]
  110. 110. 
    Yan L, Edgar TF, Baldea M 2019. Dynamic process intensification of binary distillation based on output multiplicity. AIChE J 65:1162–72
    [Google Scholar]
  111. 111. 
    Sakizlis V, Perkins JD, Pistikopoulos EN 2004. Recent advances in optimization-based simultaneous process and control design. Comput. Chem. Eng. 28:2069–86
    [Google Scholar]
  112. 112. 
    Sharifzadeh M. 2013. Integration of process design and control: a review. Chem. Eng. Res. Des. 91:2515–49
    [Google Scholar]
  113. 113. 
    Yuan Z, Chen B, Sin G, Gani R 2012. State-of-the-art and progress in the optimization-based simultaneous design and control for chemical processes. AIChE J 58:1640–59
    [Google Scholar]
  114. 114. 
    Tsay C, Pattison RC, Baldea M 2018. A pseudo-transient optimization framework for periodic processes: pressure swing adsorption and simulated moving bed chromatography. AIChE J 64:2982–96
    [Google Scholar]
  115. 115. 
    Mohideen M, Perkins JD, Pistikopoulos EN 1997. Towards an efficient numerical procedure for mixed integer optimal control. Comput. Chem. Eng. 21:S457–62
    [Google Scholar]
  116. 116. 
    Flores-Tlacuahuac A, Biegler LT. 2007. Simultaneous mixed-integer dynamic optimization for integrated design and control. Comput. Chem. Eng. 31:588–600
    [Google Scholar]
  117. 117. 
    Diangelakis NA, Burnak B, Katz J, Pistikopoulos EN 2017. Process design and control optimization: a simultaneous approach by multi-parametric programming. AIChE J 63:4827–46
    [Google Scholar]
  118. 118. 
    Tsay C, Pattison RC, Piana MR, Baldea M 2018. A survey of optimal process design capabilities and practices in the chemical and petrochemical industries. Comput. Chem. Eng. 112:180–89
    [Google Scholar]
  119. 119. 
    Zhou D, Zhou K, Zhu L, Zhao J, Xu Z et al. 2017. Optimal scheduling of multiple sets of air separation units with frequent load-change operation. Sep. Purif. Technol. 172:178–91
    [Google Scholar]
/content/journals/10.1146/annurev-chembioeng-092319-083227
Loading
/content/journals/10.1146/annurev-chembioeng-092319-083227
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error