From the birth of civilization, fuel and energy have formed the backbone as crucial components for economic growth. Historically and even now, owners have longed for optimization of their plant’s lifecycle cost, which is eventually a function of total installed cost and operational cost. For the latter, companies are continually seeking new opportunities to make existing processes more efficient by reducing their costs. Greater reliability and increased efficiency calls for more plant instrumentation to ensure continuous operation, and thus timely availability of spares is required.
Predicting the availability of the right spare at the right time can aid in reducing production losses and will help owners achieve operational excellence.
The oil and gas sector in India during FY17 had a capacity of 234.5 MMTPA; this rose to 247.5 MMTPA by year’s end. Refineries operate with big numbers, and crude prices over two decades have fluctuated from $32 to $140 per barrel, with a 52-week high of $70.80 and a 52-week low of $45.13.
The variations in crude prices affect gross refining margins (GRM) and subsequently the downstream market. The national oil company (NOC) and private companies do not have control over crude prices. They can only adopt “best practices” to reduce operational expenditures, implement innovative solutions, reduce maintenance costs, and avoid unplanned shutdowns, etc.
This column will address the latter actions – reducing maintenance costs by lowering inventory levels and using the reasons behind prior unplanned shutdowns as triggers to avoid future trips.
The long-relied-upon conventional approach is based on the reactive methodology – an instrument is fixed when it fails. Its recent incorporation of preventive maintenance techniques gives it a more-robust architecture to achieve reliability.
Though the conventional methodology remains popular, there are several possibilities for improvement. To name a few:
- Automatic placing of orders for spares (including technical and commercial bid evaluation, placement of purchase orders, expediting, etc.)
- Automatic procedure for notifying the storeroom to dispatch materials to the plant/unit
- In-built predictive triggers on inventory levels
The suggested Futuristic Methodology will utilize advance triggers from field instruments (predictive diagnosis), a preventive maintenance schedule, and a database of the failure history of instruments. It will also link the plant control system to enterprise resource planning (ERP) software for automating the process. Upon implementation, this new approach will:
- Automate the procedure of notifying the storeroom and the maintenance team in advance based on triggers. The triggers are either generated in the plant control system or through notifications for preventive maintenance in an ERP tool.
- Automate the procedure for placing orders for spares depending upon predetermined reorder levels and trigger-based advance requirement.
This is the era of smart instruments: i.e., instruments capable of detecting their own health status – of performing self-diagnostics. The instruments can continue to provide a valid output but may eventually lose their needed operability because of external or internal reasons. This may act as a trigger of a “would-be” requirement of maintenance prior to a scheduled one or an indication of a “would-be” trip.
To achieve greater reliability, the critical element is basic field instrument performance. If we further break down a reliable operating system to measurable chunks, we reach a finite level, and that is field instruments.
Historical data also substantiates that an instrument is sometimes the cause of a major shutdown and hence the reason for production losses. One worldwide example is the famous Buncefield Oil Explosion where a level instrument cost approximately £10 million to the owners.
Triggers are logged in the plant control system, which in turns handshakes with the ERP system to validate the reorder levels/reorder quantities. If the reorder level is met, then the ERP system will place a system-driven purchase order to the predetermined supplier. This will avoid the need for any human intervention for low-cost, high-moving spares, resulting in a reduction in service cost.
Possible identification of triggers
In an operational plant, there are many triggers for maintenance. Among these are:
- Diagnostic triggers: Operating plants can attain a high level of reliability and efficiency based on proactive or predictive maintenance. Smart instruments such as transmitters and primary sensors these days are capable of monitoring themselves and can communicate to the plant control system about their health. They can communicate, for example:
a. Sensor failure of a dual-element temperature transmitter
b. Choking of an impulse line of a pressure/flow transmitter
c. Changes in valve travel time
d. Abnormal current consumption
- Scheduled preventive maintenance (PM) triggers: All of the instruments have scheduled PMs; these notifications are generated within the ERP system. The PM work in executed per standard maintenance practice (SMPs), with the SMP identifying what parts need to be replaced – for example, O-rings or gaskets.
- Device failure history that gets logged in the ERP system – data specific to a tag can also be used to predict an upcoming failure.
An automated ordering process virtually eliminates the need for human intervention, which reduces chances of human error. Over a plant’s lifecycle, it also can lower indirect and direct costs involved in procurement. The availability of failure and maintenance history data at one’s fingertips for analysis can facilitate predictive maintenance, and predictive consumption of spares leads to optimum inventory and storage levels.