A catalytic process unit can perform as expected, it can underperform, or it can perform better than expected. The important thing here is to monitor the performance of the unit regularly, to normalize the data, and to utilize the available performance of the unit to maximize the margin on the process, and in case the unit is underperforming to investigate swiftly how to restore profitable operation.
Unit monitoring should be done regularly (say weekly) by the catalyst user looking after the unit on a daily-basis. Nevertheless, we find that the depth of the analysis varies and that many catalyst users rely on the infrequent (quarterly) unit monitoring offered by the catalyst supplier. The catalyst supplier will look if the unit is performing as promised, give some recommendations, and give a forecast for remaining cycle length. A catalyst user should be interested in a more frequent and more in-depth analysis on how to deploy the remaining activity of the catalyst for profit maximization. To better assist the catalyst user, Catalyst Intelligence has developed a hydrotreater optimization tool, HydroScope, which can be used for unit monitoring and for performance predictions under different operating conditions. This model is easy to use and can be consulted daily or as often as the catalyst user wants. We also develop models for other catalytic processes. This allows catalyst users to make their own decisions about the catalyst deployment independent from the catalyst supplier.
In case a unit does not perform as expected, a root Cause Analysis (RCA) needs to be launched as soon as possible after first detection of the underperformance. Time is of the essence as every additional day or week delay may result in irreparable damage to the catalyst and thus a shorter catalyst cycle. In case you wait with the RCA until the unit is at end of run, there is nothing we can do to help you extend cycle length. The root cause may lie in the quality of the catalyst loaded, the loading procedures followed, the start-up or other factors determining catalyst efficiency. It is important to follow a systematic investigation strategy to eliminate potential causes one by one. The catalyst supplier will usually assist in the root cause investigation, specifying the data they would like to receive from the refiner or petrochemical plant. Our recommendation is to do unit monitoring regularly and to have the relevant data on catalyst performance at your finger-tips. Early detection of underperformance and fast track execution of the RCA allows for corrective actions to maintain catalyst activity and avoid a costly unit shutdown for catalyst change-out.
Catalyst Intelligence can assist in collecting the necessary information and developing a remedial action plan. In case the underperformance is due to actions that have occurred under the responsibility of the refiner or chemical plant it is important to find out what has happened and to ensure that such deviations from best practices do not reoccur during future operations. In case the underperformance is catalyst related, Catalyst Intelligence can assist the refiner in issuing a claim against the catalyst supplier, and finding a mutually acceptable solution.
Since 2016, we have assisted several customers with underperformance investigations and found several root causes such as (prolonged) operation outside the guarantee conditions of the supplier, wrong selection of a catalyst for the unit by a supplier, faulty distributor trays in a hydrocracker by process licensor, as well as other causes. In case of an underperformance, the financial consequences are always for the catalyst user e.g. the refiner or petrochemical plant. Whatever the cause of the underperformance, we will help you find it and we will help you fix it once and for all. It is important to monitor the performance of your units to optimise the operating margin of your refinery or petrochemical complex. This is accomplished by avoiding unnecessary costs and unlocking margin improvement opportunities.
See also our page about Catalyst Performance Prediction Model
