Industrial Process Plants : : Global Optimization of Utility Systems / / Ravi Nath.

• Superior document: Title is part of eBook package: De Gruyter DG Plus DeG Package 2024 Part 1
• VerfasserIn: Nath, Ravi,
• Place / Publishing House: Berlin ;, Boston : : De Gruyter, , [2024]; ©2024
• Year of Publication: 2024
• Language: English
• Series: De Gruyter STEM
• Physical Description: 1 online resource (XVI, 241 p.)
• Other title: Frontmatter --; Preface --; Contents --; Units of Measure (UOM) --; 1 Introduction --; Part I: Model development --; Introduction --; 2 Modeling basics --; 3 Modeling boilers --; 4 Modeling rotating equipment --; 5 Modeling turbogenerators --; 6 Modeling the steam subsystem --; 7 Expanding the model --; 8 Modeling purchased energy contracts --; Part II: Model deployment --; 9 Offline optimization --; 10 Online, real-time optimization, RTO --; Appendix A: List of models --; Index
• Format: Mode of access: Internet via World Wide Web.
• ISBN: 9783111020679; 9783111332192
• DOI: 10.1515/9783111020679
• Access: restricted access
• Hierarchical level: Monograph
• Statement of Responsibility: Ravi Nath.

Removing the barriers to the Global Optimization of Plant Utility Systems by providing practical tools and techniques to deal with these unique challenges is the purpose of this book. The operating cost of a typical Plant Utility System of a typical Industrial Production Process Plant is enormous - often in tens if not hundreds of millions of dollars per Annum. With so much money at stake, one would expect that heroic optimization efforts would be made to reduce the operating cost, however such is usually not the case. One reason for this complacency is that Plant Utility Systems are usually "cost centers" in Process Plants and their operating cost is prorated amongst the various Production Units, so it suffers from what is at times referred to as "the tragedy of the commons". Another reason for this complacency is that the Plant Utility System structure is significantly different than that of other Production Units, as for flexibility and safety reasons it has a large spare capacity to meet increased utility demand during startups, shutdowns, and emergencies. The existence of a spare equipment necessitates optimization of discrete decisions, whereby traditional optimization techniques do not readily apply. Part of the problem is that the traditional engineering curriculum primarily emphasizes only one of the many optimization methods, called Non-Linear Programming (NLP). Although NLP can address large classes of optimization problems, it has fairly stringent requirements that all describing relationships (or functions) be continuous and have continuous derivatives. Additionally, in general, NLP only guarantees a local but not the global optimum. Another optimization method is particularly well suited for modeling Plant Utility Systems is called Mixed Integer Linear Programming (MILP). And unlike NLP, MILP methods can guarantee global optimum, which is very reassuring. MILP, however, does impose linearity requirements but as discussed in this book there are techniques to overcome this limitation.