Design of Flexible Utility Systems
Systematic methods and tools for managing the complexity
Tools Integration - CAPE Methods & Tools (T4-10P)
Keywords: flexible utility systems, transient analysis, dynamic models
In the process industry, the costs of utilities, such as heating, cooling and power, make a significant contribution to the costs. Utility systems design is one of the most challenging topics in process engineering. The complexity of equipment networks and choice of operating conditions present significant challenges to optimise utility systems under industrial conditions.
Under variable conditions, such as different electric power tariffs, changing load in processes and variable ambient temperature, different operating scenarios can be proposed. But there are transient periods in which utility systems transfer from one scenario to another. Flexible utility systems should transfer between such scenarios smoothly and safely. In this paper, transient behaviour is studied to design a flexible utility system. Transient models are developed for ‘slow’ utility equipment. For fast-response equipment, steady and quasi-steady models can give adequate representation.
A case study is presented to show how transient analysis helps to design a flexible utility system. Firstly, an optimisation problem is set up, including, identification of constraints and objectives. Then steady state operating scenarios can be obtained from conventional procedures based on steady state models, including the site configuration, equipment design and operating scenarios. Transient behaviour of the utility system is examined based on dynamic modelling and simulation. Finally, steady operating policies are optimised to maximise the system efficiency while variation of steam conditions (flowrate, pressure) are kept within acceptable bounds.
Transient analysis can be applied to check the feasibility of utility system design, analyze the flexibility and reliability of utility systems and design the operating strategies in both steady operation and transient periods. Understanding transient better can lead to cost savings through less standby capacity.
The support from an EC Marie Curie Research Training Network "Towards Knowledge-Based Processing Systems (PRISM)" Contract No: MRTN-CT-2004-512233 is gratefully acknowledged.
Presented Thursday 20, 13:30 to 14:40, in session Tools Integration - CAPE Methods & Tools (T4-10P).
