(40a) Using an Operational Excellence Platform to Reduce Energy and Carbon Loss

Operational Excellence (OE) focusses on sustainable improvement across a business. It includes management systems, operating procedures and tools but critically, it requires a whole-company culture where all stakeholders have an opportunity to see the current operational status and provide their input to drive improvement. OE approaches must be able to adapt quickly to changes in operating constraints, economic climate and product demand and the insights of staff across the business can most often deliver advice on the adjustments required to maximise performance and return on capital across all assets.

Tools form a key tranche of any OE system but most often such tools do not provide the true value that is claimed, or even approach their true potential as tools for analysis, troubleshooting and improvement guidance. They tend to tell you where you are, and sometimes where you can get to but rarely do they tell you the path you should take to realise the true operating potential of your assets. OE tools are good at presenting the information on current performance for example displaying Key Performance Indicators (KPIs) but they often struggle to identify where the areas for improvement or gaps are, what the opportunity is and what can be done to close the gaps. It is left to the ingenuity of the engineering staff to come up with suggestions but they can only do this if they have the right experience and they are exposed to the raw performance data presented in a succinct way.

This paper describes how the powerful technology of modern process modelling tools can be combined together with the visualisation capabilities of web-based dashboards. An industrial case study is presented to highlight how these are used to reduce energy and carbon footprints. This combination dovetails well into an OE environment and it enables chemical engineering science to help drive improvement, alongside the wealth of the staff knowledge on site. The approach fits well when applied to fuel gas, hydrogen, steam and water systems, where it is used to reduce emissions, energy¸ material and water losses per each tonne of product - so-called greenhouse gas intensity, energy intensity, material loss intensity and water intensity - while maintaining the right economic balance. However, it can be used for any operating plant.

The approach starts with the current operating data, which are retrieved from the plant data historian, typically as daily-average values. The plant focus can be an individual operating unit but it is better to include the whole plant - the wider the scope the better will be the opportunity for improvement. Additional data such as costs and operating constraints should also be considered. A process simulation model is then developed which uses this data. The model could be in simpler spreadsheet form or could be built within one of the standard process simulation packages available on the market. The plant needs to be validated for consistency. Once the simulation model is working, it needs to be run in optimisation mode. This identifies the operating conditions that will give the best operating objective and give a target performance for each equipment item and plant. The objective is typically maximum profit but it could also be minimum emissions for example carbon dioxide. By comparing these calculated conditions and targets against actual, we can see straight away where the inefficiencies in operation are. An extra level of intelligent gap analysis can often be written into the model to identify why a particular element of the process is poorly operating and what the operator can do to improve it.

So far we have explained an approach to target the best plant performance and identify how to get from the current operating position to this optimum. But this in itself, although very good at a detailed engineering level, still does not represent a significant element in an OE culture. We need also to be able to present the data to the various stakeholders of the process in the right way.

Dashboard tools provide an effective way to convey this information. By linking the process model results with the dashboard and then providing access for all stakeholders, we can significantly leverage the results of the process modelling. Dashboard solutions provides different levels of access and information details based on login ID. Information can be presented at a very detailed level, where all heat and material balance and suggested operational changes can be viewed. Alternatively, the environment can be configured so that some users only see the main performance KPI’s which may be used for overall performance tracking.

In this paper, we present a case study to show energy and carbon reduction changes over time using an OE Platform comprising process simulation and optimisation models and a Benchmarking Dashboard. We show how it is used and how it presents the information required by the various process stakeholders, for example by operations staff to modify operation and improve performance, by management who may be more focussed on performance KPI’s and by the Sustainability Team to show performance changes over time.

A company culture where an OE plays a key role is very powerful. However, it needs tools and visualisations to empower individuals to drive that improvement. This paper describes how process optimisation and dashboard technologies are combined together within an OE framework to drive operating improvements and deliver real bottom line savings to maximise performance and return on capital across all assets.