Skip to main content Accessibility help
×
Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-10T01:34:03.315Z Has data issue: false hasContentIssue false

8 - Network Environment: Extensions

from Part III - Advanced Methods

Published online by Cambridge University Press:  01 May 2021

Christos T. Maravelias
Affiliation:
Princeton University, New Jersey
Get access

Summary

In this chapter, we discuss how to model additional processing features that may be present in a chemical facility. To keep the presentation simple, we illustrate how models based on a common discrete grid can be modified to account for these features. Continuous time models can also be extended to account for most of these features, but often lead to more complex and/or nonlinear formulations. We start, in Section 8.1, with the modeling of material consumption and production during the execution of a batch. In Section 8.2, we discuss the modeling of complex material storage and transfer activities. In Section 8.3, we present how to account for unit and task setups and task families. Finally, in Section 8.4, we present how to model unit deterioration and maintenance tasks.

Type
Chapter
Information
Chemical Production Scheduling
Mixed-Integer Programming Models and Methods
, pp. 193 - 215
Publisher: Cambridge University Press
Print publication year: 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Kondili, E, Pantelides, CC, Sargent, RWH. A General Algorithm for Short-Term Scheduling of Batch-Operations. 1. MILP Formulation. Comput Chem Eng. 1993;17(2):211227.CrossRefGoogle Scholar
Pantelides, CC, editor. Unified Frameworks for Optimal Process Planning and Scheduling. 2nd Conference on Foundations of Computer Aided Process Operations; 1994; Snowmass: CACHE Publications.Google Scholar
Gimenez, DM, Henning, GP, Maravelias, CT. A Novel Network-Based Continuous-Time Representation for Process Scheduling: Part I. Main Concepts and Mathematical Formulation. Comput Chem Eng. 2009;33(9):15111528.Google Scholar
Gimenez, DM, Henning, GP, Maravelias, CT. A Novel Network-Based Continuous-Time Representation for Process Scheduling: Part II. General framework. Comput Chem Eng. 2009;33(10):16441660.CrossRefGoogle Scholar
Velez, S, Maravelias, CT. Mixed-Integer Programming Model and Tightening Methods for Scheduling in General Chemical Production Environments. Ind Eng Chem Res. 2013;52(9):34073423.CrossRefGoogle Scholar
Jain, V, Grossmann, IE. Cyclic Scheduling of Continuous Parallel-Process Units with Decaying Performance. AlChE J. 1998;44(7):16231636.Google Scholar
Alle, A, Pinto, JM, Papageorgiou, LG. The Economic Lot Scheduling Problem under Performance Decay. Ind Eng Chem Res. 2004;43(20):64636475.CrossRefGoogle Scholar
Nie, Y, Biegler, LT, Wassick, JM, Villa, CM. Extended Discrete-Time Resource Task Network Formulation for the Reactive Scheduling of a Mixed Batch/Continuous Process. Ind Eng Chem Res. 2014; 53(44):1711217123.CrossRefGoogle Scholar
Biondi, M, Sand, G, Harjunkoski, I. Optimization of Multipurpose Process Plant Operations: A Multi-Time-Scale Maintenance and Production Scheduling Approach. Comput Chem Eng. 2017;99:325339.CrossRefGoogle Scholar
Liu, SS, Yahia, A, Papageorgiou, LG. Optimal Production and Maintenance Planning of Biopharmaceutical Manufacturing under Performance Decay. Ind Eng Chem Res. 2014;53(44):1707517091.CrossRefGoogle Scholar
Aguirre, AM, Papageorgiou, LG. Medium-Term Optimization-Based Approach for the Integration of Production Planning, Scheduling and Maintenance. Comput Chem Eng. 2018;116:191211.CrossRefGoogle Scholar
Xenos, DP, Kopanos, GM, Cicciotti, M, Thornhill, NF. Operational Optimization of Networks of Compressors Considering Condition-Based Maintenance. Comput Chem Eng. 2016;84:117131.Google Scholar
Wiebe, J, Cecilia, I, Misener, R. Data-Driven Optimization of Processes with Degrading Equipment. Ind Eng Chem Res. 2018;57(50):1717717191.Google Scholar
Wu, Y, Maravelias, CT, Wenzel, MJ, ElBsat, MN, Turney, RT. Predictive Maintenance Scheduling Optimization of Building Heating, Ventilation, and Air Conditioning Systems. Energy and Buildings, 2021; 110487.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×