A major challenge for chemical plants is the cost associated with unscheduled machine downtime. Since companies do not share shutdown information publicly, it is often challenging to calculate asset failure at an industry-wide level. Nevertheless, according to Accenture’s ICIS Chemical Business, the missed profit opportunity alone on a major cracker shutdown in the US Gulf Coast was $1.4 MM/d per world-scale cracker. Furthermore, a calculation by the Aberdeen Group indicates that 2%–5% of production is lost in the petrochemical sector. It should be noted that the chemical industry has achieved productivity gains over the past few years, thereby raising the bar for incremental gains. Industrial analytics provide chemical plants with two important pieces of information. First, when an alert is generated with an accurate time to failure (TTF), plant maintenance staff can schedule repairs in a way that minimizes disruption to production. Secondly, root cause failure analysis helps limit the likelihood of the failure reoccurring elsewhere in the plant. A simple but powerful equation is driving the adoption of industrial analytics: lower machine downtime leads to higher yield rates and increased revenue. At present, many plant owners choose to over-invest in maintenance by using expensive, time-based preventive maintenance. The alternative of unscheduled downtime is both expensive and disruptive to operations. With advances in industrial analytics, chemical plants can receive early alerts of evolving failures, providing the opportunity to remediate before the degradation leads to a shutdown. In addition, root cause failure analysis maintenance crews can focus on the underlying reasons for failure. As a result, the life of a chemical plant asset can be extended. Of course, industrial analytics is only one element of an overall program to extend asset life. Plants may also need to reverse deterioration by performing heavy maintenance repairs and by using equipment as designed. Numerous factors will determine when the benefits of industrial analytics are fully realized. For example, many chemical plants are not capturing and storing the sensor data that is generated, which is the first step toward the implementation of machine learning-based solutions. Source: Hydrocarbon Processing, 2/2018, p.8.
TCGR Note: Of course this presents a solid case for industrial analytics, but it doesn’t present the other side of CAPEX, ROI and/or prioritize where analytics fits in. If the goal is to save 2% in reduced downtime or 2% on O&M budgets but the cost to maintain, analyze as well as pay for an expensive system needs to be considered. One needs to question is this money is better spent elsewhere.