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9 Control of distillation processes

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Advanced Process Engineering Control
This chapter is in the book Advanced Process Engineering Control
9 Control of distillation processesDistillationis a separation technique of miscible liquids. It is based on theboilingpoint(more precisely on thevolatility) difference of components and usually appearsat the end of a technological flux. Therefore, the characteristics and behavior of up-stream operations (reactions, extractions, etc.) may significantly affect its operatingspecifications. A well-designed and implemented automated control system (ACS) hasto be robust against upstream (and any other) disturbances. Distillation columns ex-hibit relatively slow and delayed dynamic responses as compared to other units be-cause of the high number of mass transfer stages that are generally involved. Thisfact combined with the numerous other factors affecting distillation raises specialcontrol issues and requires more attention during design of ACS configurations [1, 2].9.1 Economic constraints of distillationOne major objective of distillation is to deliver products of prescribed purity (quality),which is a design constraint. Since distillation is associated with large heating and coolingdemands, minimization of operational costs a major operating objective, that must be en-forced by the control systems. Prescribed purity/quality specifications can be achieved byeither using a rigorous process control (enables direct production) or by mixing productsof different purities/qualities. However, the latter option is not necessarily economicallyprofitable since the energy spent to obtain the high-purity product is lost.Given that most products have minimal quality requirements (think about gasolineproducts with different octane number categories), product quality and price are notdirectly correlated. Products outrunning minimal specifications do not sell better or re-sult in improved incomes. The diagram in Fig. 9.1 demonstrates that the maximal in-come is obtained by the minimal required purity that still enables product marketing atthe desired class. By comparing Grades 1 and 2 products, it is obvious that separationcosts increase with purity monotonically, whereas the product price remains piecewiseconstant. Yet, if the prescribed Grade 1 quality is not achieved, the product can be mar-keted as Grade 2 quality, resulting in cost penalizations that can lead to overall losses(gray areas in the Fig. 9.1). Therefore, the majority of distillation columns are operatedto slightly overdo the specifications so that the actual purity never falls under the pre-scribed value. For this reason, distillation columns may be equipped with buffer vesselsthat act as concentration equalizers. Expressing these facts in numbers means that if,for example, the product purity shows ±0.2% fluctuation, the concentration controllersetpoint should be with 0.2% above the desired value. The benzene-toluene separation,for instance, prescribes a toluene purity of 99.5%; thus, a ±0.2% fluctuation sets the con-troller to 99.7%. From an economic perspective, industrial practice showed that for thesetpoint of 99.7 instead of 99.5%, results in a 10% energy consumption increase. More-https://doi.org/10.1515/9783110789737-011
© 2023 Walter de Gruyter GmbH, Berlin/Boston

9 Control of distillation processesDistillationis a separation technique of miscible liquids. It is based on theboilingpoint(more precisely on thevolatility) difference of components and usually appearsat the end of a technological flux. Therefore, the characteristics and behavior of up-stream operations (reactions, extractions, etc.) may significantly affect its operatingspecifications. A well-designed and implemented automated control system (ACS) hasto be robust against upstream (and any other) disturbances. Distillation columns ex-hibit relatively slow and delayed dynamic responses as compared to other units be-cause of the high number of mass transfer stages that are generally involved. Thisfact combined with the numerous other factors affecting distillation raises specialcontrol issues and requires more attention during design of ACS configurations [1, 2].9.1 Economic constraints of distillationOne major objective of distillation is to deliver products of prescribed purity (quality),which is a design constraint. Since distillation is associated with large heating and coolingdemands, minimization of operational costs a major operating objective, that must be en-forced by the control systems. Prescribed purity/quality specifications can be achieved byeither using a rigorous process control (enables direct production) or by mixing productsof different purities/qualities. However, the latter option is not necessarily economicallyprofitable since the energy spent to obtain the high-purity product is lost.Given that most products have minimal quality requirements (think about gasolineproducts with different octane number categories), product quality and price are notdirectly correlated. Products outrunning minimal specifications do not sell better or re-sult in improved incomes. The diagram in Fig. 9.1 demonstrates that the maximal in-come is obtained by the minimal required purity that still enables product marketing atthe desired class. By comparing Grades 1 and 2 products, it is obvious that separationcosts increase with purity monotonically, whereas the product price remains piecewiseconstant. Yet, if the prescribed Grade 1 quality is not achieved, the product can be mar-keted as Grade 2 quality, resulting in cost penalizations that can lead to overall losses(gray areas in the Fig. 9.1). Therefore, the majority of distillation columns are operatedto slightly overdo the specifications so that the actual purity never falls under the pre-scribed value. For this reason, distillation columns may be equipped with buffer vesselsthat act as concentration equalizers. Expressing these facts in numbers means that if,for example, the product purity shows ±0.2% fluctuation, the concentration controllersetpoint should be with 0.2% above the desired value. The benzene-toluene separation,for instance, prescribes a toluene purity of 99.5%; thus, a ±0.2% fluctuation sets the con-troller to 99.7%. From an economic perspective, industrial practice showed that for thesetpoint of 99.7 instead of 99.5%, results in a 10% energy consumption increase. More-https://doi.org/10.1515/9783110789737-011
© 2023 Walter de Gruyter GmbH, Berlin/Boston

Chapters in this book

  1. Frontmatter I
  2. Preface to the second edition V
  3. Contents IX
  4. Part I: Advanced Process Control
  5. Introduction 3
  6. 1 Complex and nonconventional control systems 5
  7. 2 Model predictive control 40
  8. 3 Fuzzy control 81
  9. 4 Optimal control systems 97
  10. 5 Multivariable control 125
  11. 6 Plantwide control 141
  12. 7 Linear discrete systems and the Z transform 150
  13. Part II: Applied Process Engineering Control
  14. Introduction 183
  15. 8 Reaction unit control 185
  16. 9 Control of distillation processes 239
  17. 10 Control of absorption processes 268
  18. 11 Control of extraction processes 277
  19. 12 Control of evaporation processes 286
  20. 13 Control of drying processes 294
  21. 14 Control of crystallization and filtration processes 311
  22. 15 Case studies 334
  23. 16 Problems and exercises 410
  24. Index 419
Readers are also interested in:
https://doi.org/10.1515/9783110789737-011

Chapters in this book

  1. Frontmatter I
  2. Preface to the second edition V
  3. Contents IX
  4. Part I: Advanced Process Control
  5. Introduction 3
  6. 1 Complex and nonconventional control systems 5
  7. 2 Model predictive control 40
  8. 3 Fuzzy control 81
  9. 4 Optimal control systems 97
  10. 5 Multivariable control 125
  11. 6 Plantwide control 141
  12. 7 Linear discrete systems and the Z transform 150
  13. Part II: Applied Process Engineering Control
  14. Introduction 183
  15. 8 Reaction unit control 185
  16. 9 Control of distillation processes 239
  17. 10 Control of absorption processes 268
  18. 11 Control of extraction processes 277
  19. 12 Control of evaporation processes 286
  20. 13 Control of drying processes 294
  21. 14 Control of crystallization and filtration processes 311
  22. 15 Case studies 334
  23. 16 Problems and exercises 410
  24. Index 419
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