Increase Refinery Availability by Predicting Failures Before They Happen

Increase Refinery Availability by Predicting Failures Before They Happen

Refineries and petrochemical plants have always faced competition, but this is increasing, due to the falling oil prices and other industry changes. With an abundance of data about their operations, refineries and petrochemical plants can use this information to increase their availability, providing them with an additional competitive advantage. With the influential ERTC conference taking place this week, we decided to explore how improved technology can help refineries and petrochemical plants become cutting edge “plants of tomorrow,” which will positively affect plant availability.

Maintaining high availability provides plants with a strong competitive lead, but there are many other benefits. One of those advantages is improved profitability. According to research conducted by Elsevier, “unplanned disruptions are expensive. The cost of a large-scale incident could run to hundreds of millions of dollars.” Another reason high availability is so important is that it improves the overall safety of the plant, thus preventing injuries or even death; and when crises are averted, the refinery or plant avoids the ensuing negative publicity.

Plant fires and explosions can cause major damage, but there are many other failures that can occur during production processes leading to production loss and quality issues. For example, bolted joint and seal failure due to excessive vibration or relief valve chattering can lead to mechanical integrity failure.

Even Small Failures Affect Availability

Catastrophic incidents can be extremely costly to refineries and petrochemical plants. Elsevier explained that the “cost of missed production for an average U.S. refinery has been estimated at between $340,000 - $1.7 million per day.” However, a failure does not have to be catastrophic to be problematic, and there are insidious losses from minor failures that reduce productivity, and they are practically untraceable using traditional methods. These problems, if not treated, can eventually lead to massive breakdowns.

One way to lower the probability of such challenges is through more thorough and proactive maintenance, such as in-depth inspection programs or increasing scheduled maintenance. However, even if it is economically feasible, in many cases a complete inspection cannot be possible. Some areas of refineries and petrochemical plants are not considered critical, and do not even have maintenance programs in place. For example, as researched by the LMA Onshore Energy Business Panel, in one refinery, “a steam turbine generator tripped off load but continued to rotate and overspeed to the point of destruction. It turned out that the non-return valve was found to be stuck open, but there was no testing and maintenance program in place for this critical non-return valve because it was not considered safety-critical.”

While some failures occur during maintenance (i.e. something is turned off which triggers a failure elsewhere), most failures happen while the refinery is in normal operation. However, because everything in process manufacturing is connected to everything else, the reason for failure is not always evident. For instance, according to LMA, “a water freeze in piping was partially attributed to a non-functioning water level transmitter. The water level instrument was not identified as being an IPL (independent protection layer).” To understand the problem properly, a holistic view rather than a partial one is needed.

As you can see from even these few examples (and there are hundreds more), there are plenty of things that can go wrong in a refinery or petrochemical plant, and a new method is needed to predict these failures. Traditionally, plants have operated with the logic “if something is broken, fix it.” Now, thanks to predictive analytics the paradigm can change to: “something is a little ‘off,’ let’s investigate and prevent it from deteriorating further.”

Leveraging Plant Data for Increased Availability

Refineries and petrochemical plants collect mountains of data, so why not leverage it to improve availability? With sensors tracking almost every element of the plant, and vast quantities of data collected in the historian, it is important to make sense of it all. However, current technological advances in predictive analytics can be applied to this data, predicting (and thus preventing) many potential failures. By identifying anomalies in the plant’s data and applying algorithms to determine the relevance of these anomalies, plant operations managers can focus on predictive monitoring and maintenance, rather than simply doing “preventive” maintenance according to a predetermined schedule or fixing things that have already broken.

Significant progress has been made in other industries by using Artificial Intelligence and Machine Learning for predictive monitoring, but solutions designed for discrete industries are not effective enough for refineries or petrochemical plants, unless they are adapted to the unique needs of process industries.

What to Look for in Predictive Monitoring

Artificial Intelligence and Machine Learning are popular buzzwords, but an effective predictive monitoring solution for refineries and petrochemical plants will go far beyond mere anomaly detection using AI and ML. Some of the key aspects to watch out for include:

Cover everything in the plant, not just selected equipment. It can be tempting to cut corners by focusing on just the “important” areas of the plant. However, with every aspect of the plant connected to every other, you don’t know where the next failure will show up, and you can’t afford to miss it.

Bridge the gap between anomalies in the data to true failures in the plant. It’s the connection between the different parameters, never just one anomaly that predicts a failure. An effective solution will cluster the anomalies it finds, and will associate the identified issue with a specific area of the plant, to shorten time to resolution.

Avoid alert fatigue. If the system sends out too many alerts, operators will simply start to ignore them, and the important ones may get lost. A useful system will select just the vital alerts to send to the operators, so that each one will be treated with the attention it deserves.

Rapid implementation. Once the plant management has decided that it is important to implement a predictive monitoring system, time is of the essence. Employees’ time is important. Everyone is busy. Short implementation time assures getting back to core business quickly.

No need for a data scientist. The operators or process managers know the plant inside and out, and they are the ideal people to investigate the alerts and resolve them.

On-premise or cloud offering. Some companies will prefer cloud implementations to offload the IT burden, others will prefer on-premise, so it is important that the option the company prefers is available to them.

Improving availability and overall equipment effectiveness by using predictive monitoring has many benefits. Not only can it make your plant more competitive, but it can keep your employees and the surrounding areas safe from unwanted explosions, fires, and other hazardous issues. This can ultimately help you to avoid negative publicity and propel your plant into the future of Industry 4.0. The answer is to predict failures before they happen.

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