(46b) Smart Plant Applications in Crude Distillation Units

Smart Plant represents an approach to delivering optimal performance of plant assets. It focusses on the intelligent use of plant data and predictions of performance to make decisions that deliver operating benefits. These can be classified in terms of economic, environmental, safety and quality. Crude units form the heart of an oil refinery and their operation drives overall refinery profitability. Dwindling margins and tighter regulations make the adoption of new technologies and concepts even more important, so operators can maintain any possible competitive edge. This paper presents our ideas and experiences of Smart Plant applications in refinery crude distillation units.

Smart plant is a form of intelligent manufacturing. There are a number of key elements within a Smart Plant system that need to be considered to ensure smart decisions are made.

The Smart Plant approach requires a sensor network. The associated instrumentation provides the data landscape from which conclusions are drawn regarding performance. However, such data sources are prone to uncertainty, for example as a result of broken sensors, faulty instruments, signal drift and breaks in the data chain. Data quality is measured in terms of frequency of records and uncertainty.

Data values need to be validated and reconciled. There are several methods to doing this, and our approach has centred on using State Estimation technology via a Kalman filtering approach. Based on a high-fidelity, mathematical model of the process, this approach enables appropriately accurate estimates to be made of unknown variables by estimating probability distributions for measured variables. PSE’s gPROMS ProcessBuilder environment based on the equation-oriented gPROMS platform is used to facilitate such an assessment. Dynamically changing data are gathered and a full set of operating parameters can be calculated which describe the current process operation.

In fact, this paper shows how State Estimation is applied in real time. The key principle is to have the detailed physical model of the process to provide an integrated environment and then conduct process simulation trials.

With the data now validated and a time-averaged set of performance operating parameters defined, the model can proceed and the process optimised, generating set-points that can be fed to the control system directly or to an APC application. Hence, model-based Supervisory control is achieved.

We also now highlight a further objective of Smart Plant applications - multi-focussed optimisation. The scope of the optimisation can be steady-state focussed, minimising overall cost, emissions or other parameters such as flaring. We can also perform dynamic process optimisation - for example how to most efficiently transition from one operating state to another, for example as feedstock changes.

Another objective of the Smart Plant approach is asset optimisation, where design, operations and maintenance aspects are all considered in the analysis. This topic is achieving more focus now because of the realisation that, an optimised system will very quickly become inefficient if it goes off-line, for whatever reason, and production is reduced or stopped. Hence, the attributes of maintenance programmes must be considered.

Finally, the way the results of Smart Plant applications are disseminated is very important. Pushing information to stakeholders and visualisation of performance through Key Performance Indicator (KPI) tracking is very helpful to adopting such improvement advice.

An industrial case study is presented of our experience using the gPROMS platform to optimise crude distillation units. It shows how operating costs are optimised alongside maintenance costs, in real time, to provide an overall optimised crude unit. The example uses real-time data acquisition and state-estimation techniques to ensure data integrity and informed decisions. Operators then make decisions on process improvements with confidence, realising significant cost savings in the process. The results are presented to users via data visualisation tools and KPI dashboards, ensuring appropriate dissemination of results and realisation of the benefits.