Turning Redundancy into Profit by Optimising Existing Site Utility Systems a report by Yuhang Lou,1 Nan Zhang2 and Robin Smith3 1. Consultant; 2. Vice President; 3. Chief Executive Officer, Process Integration Ltd, Manchester


A utility system converts fuels to steam, electricity and shaft power to satisfy the demands of different processes on a site. The operating cost for the utility system usually accounts for a large amount of the total operating cost in a refinery. It is such a large cost that a small percentage reduction could be worth millions of dollars in terms of absolute savings.


One particular feature of site utility systems is the equipment redundancy. Because utility systems are subject to much uncertainty of operation across the site, there is usually significant redundancy in place. This redundancy allows the site to respond to changes in operation across the site, and accommodate maintenance of equipment and breakdowns. Maintaining the integrity of site operation is vital, but a significant amount of utility equipment redundancy is the penalty for this. However, this redundancy also presents opportunities to improve performance by managing its operation more effectively.


The fundamental idea for optimising operations in a site utility system is to exploit better management for system redundancy, so that the utility system can be operated in a more marginal manner. For utility systems, it is possible to achieve savings based on optimising operation only. Such an optimisation involves no shutdown, facility change or revamping, which means zero investment of capital.


Yuhang Lou is a Consultant at Process Integration Ltd. Her main specialisations include energy conservation,


refinery hydrogen network management and CO2 capture and storage (CCS). She has carried out many projects on energy conservation for site utility systems and refinery hydrogen optimisation. Professor Lou also contributes to research and development activities and software development at Process Integration Ltd.


Nan Zhang is a Vice President of Process Integration Ltd. He is also a Lecturer in Chemical Engineering at the University of Manchester. He has industrial experience as a process engineer with the China Petroleum & Chemical Corporation (SINOPEC). Professor Zhang completed his PhD in the Centre for Process Integration at the University of Manchester and graduated with a Bachelor of Science in Chemical Engineering from Tsinghua University in the People’s Republic of China.


Robin Smith is Chief Executive Officer of Process Integration Ltd. He is also a Professor at the University of Manchester where he is Director of the Centre for Process Integration. He has published widely in the field of process integration and is author of Chemical Process Design and Integration. He is a Fellow of the Royal Academy of Engineering, the Institution of Chemical Engineers and a Chartered Engineer. Professor Smith has industrial experience with Rohm & Haas and ICI and has acted extensively as a consultant to industry.


Methodology


Optimisation of site utility systems using mathematical programming is a well-established technology.1 this area;2


There has been extensive research in


however, it is not straightforward to apply this technology successfully in practice. A utility system is usually very complex in a real process site. To model such a complex system, there must be certain simplifications to describe it mathematically. However, if the model is over-simplified, it might fail to reflect real situations and end up making incorrect predictions. Therefore, maintaining accuracy in modelling while simplifying a complicated system is a very important aspect of utility system modelling. It is important to ensure that optimisation scenarios are practical for implementation. Optimisation scenarios derived directly from software must be fully integrated with practical constraints before implementation.


To apply operational optimisation to an existing site utility system, a systematic approach with a series of steps is required. These steps are shown below and in Figure 1.


Step 1 – Plant Data Collection


Generally speaking, there are four types of data to be collected: system structural data to determine the system configuration, operating data for key individual facilities, operation limits for various important facilities and relevant economic data.


Step 2 – Modelling


To model an existing site utility system, first the basic configuration must be modelled. This would include certain simplifications based on actual systems, but the simplified configuration must still be able to represent the actual system accurately.


To simplify a site utility system in modelling, one strategy is to combine certain facilities. The operations for such facilities are usually not adjustable. As long as such a simplification could still reflect site demands, it would not affect the decision-making in adjusting the operation of a utility system. As for key equipment such as boilers, steam turbines and gas turbines, it is not suggested to make simplifications, as this could affect the final judgement in producing optimisation scenarios.


Once the overall configuration of the site utility system is set up, the next step is to produce individual models for important facilities, including boilers, steam turbines and gas turbines, etc.3,4


The models must allow


exploitation of part-load performance of these items and should reflect true equipment performance accurately.


Another aspect to include in site utility system modelling is different operating cases. For example, different process operations could result in different requirements in steam and power and power tariffs could also affect the operation of a site utility system.


50 © TOUCH BRIEFINGS 2011


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