Trends in 2023: Making Smart Decisions
The petrochemical processing industry is constantly evolving, it can be difficult to keep track! As downstream operators begin to modernize operations, the way they work is changing. There is an ever-growing reliance upon technological advancements essential to remain competitive and profitable.
With development throughout 2022, a few trends show potential to be game changing as we follow their progress throughout 2023. This is particularly true when it comes to reliability and maintenance. Where does the peak of this technological terrain lie? Smart refineries. Fine-tuned petrochemical production at your fingertips. Designed to leverage the latest advances in automation, analytics, and information technology to optimize operations and maximize value, a ‘smart refinery’ can be defined by cumulative benefits of serval different operational technologies used in tandem.
To understand the potential of a smart refinery, it is important to consider the challenges that they are intended to solve. Let’s examine a hypothetical smart refinery in 2023. We will begin by considering the current state of the downstream oil and gas industry and the challenges that a smart refinery can address. These include a rise in global demand, increased production costs, and a need for greater efficiency and safety.
How might my smart refinery better tackle these industry pain points? By using automated systems to monitor and control production processes, direct targeted real time maintenance, and reduce waste, demand can be met flexibly and a concise action plan for waste reduction can both reduce OPEX and step in the direction of meeting net zero target. Compared to traditional management of workflows, a smart refinery can also facilitate better planning and scheduling of activities, resulting in faster execution and improved safety.
To create this (hypothetical) successful smart refinery, there are several key technologies and processes that need to be in place. However, as you slowly integrate state of the art tech, building up to an optimized ‘smart refinery’, hurdles must be overcome which disproportionately disadvantage smaller scale producers. These include upfront costs, data security, and regulatory compliance. Additionally, the potential for disruption should be considered, as a smart refinery can result in significant changes to existing processes for which a technologically adept workforce would have to be trained (a time consuming and expensive process). In other words, in many cases it is essential to have a roadmap in place which emphasizes a gradual shift towards optimized operations, offers support to workers and lays strong foundations to continue development.
Predictable Maintenance Deep Dive
As mentioned above, smart refineries use a consortium of different technologies. These include real-time analytics, advanced automation, and predictive maintenance. Here, we will focus on the latter, predictive maintenance, one of the major downstream industry buzz words flying about right now). As operation technology running in real time converges with digital tools, asset management is becoming a whole other ball field.
PdM is expected to become increasingly prevalent in refinery operations, allowing for targeted maintenance to be done at the earliest stages of potential system failures. Major operators globally are scaling up such maintenance solutions; Shell, for instance, is deploying AI predictive software to monitor and maintain more than 10,000 pieces of equipment across its asset base — one of the largest roll outs in the energy industry. ExxonMobil, Chevron and BASF have similar plans, spurring manufacturing companies such as Siemens to expand their portfolios into this space.
So, what does Predictive Maintenance (PdM) do?
A unique capacity to detect the root cause of system failures means that issues can be rectified before they cause any major disruption. In other words, companies can reduce downtime and increase overall efficiency. PdM draws on a variety of data sources when used to optimize refinery operations. These sources include sensor data from Industrial IoT (IIoT) enabled technology, maintenance management systems (CMMS), and critical equipment. Sensor data from IIoT technology can provide real-time insights into the performance and condition of equipment, while CMMS can provide information on work orders and maintenance schedules. Additionally, data from critical equipment can be used to identify potential issues and ensure proper performance. All this data can be combined with machine learning algorithms and predictive analytics to create a bespoke PdM strategy. Over the past 5-year, technology has come a long way, now more powerful and accessible, we are finally starting to tap into the full extent of data’s power.