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Analytics-driven energy management aids in carbon footprint reduction

Analytics-driven energy management aids in carbon footprint reduction

The discussion about climate change has been taking place for many years and is still a hot topic, now more than ever before. This debate has led to global initiatives to reduce carbon footprints, which is high on the agenda of almost every country's government. Regulations on a global, regional and local scale have been established to reduce greenhouse gas emissions, which heavily impacts the oil & gas industry. To achieve those goals and prove regulatory compliance, the industry is rapidly adopting the ISO 50001 standard to improve the organization's energy performance and make climate part of their corporate strategy.

Reducing your carbon footprint also has an overall profitability benefit. Within the oil & gas industry, energy is often one of the largest components of the company's cost structure, especially within refineries and petrochemical plants. Energy management to reduce costs is not new, but has become more important due to the imposed regulations. Most companies have formalized energy management programs and use automation and control technologies to help minimize energy costs. It is clear, however, that many companies need to take their efforts to the next level by monitoring and optimizing energy use in real time, and leveraging IIoT-generated data.

For many years process data has been captured in historians. All this data needs to be unlocked and leveraged for continuous improvement to lower the carbon footprint of the company. To some extent, data analytics have been utilized by large companies for their larger on-site energy issues. These time-consuming, centrally-led data modeling projects are less suited for process related optimization projects that require subject matter expertise. New tools put advanced analytics in the hands of subject matter experts such as process and field engineers. This allows them to handle 80% of energy related cases that contribute to the corporate goals for reducing the carbon footprint.

Energy Management 4.0

Global interest in Industry 4.0 has accelerated digital transformation in the process manufacturing industry, including the oil & gas sector. Many companies have engaged in technology pilots to explore options for reducing costs, increasing overall equipment effectiveness (OEE) or regulatory compliance. One of the best ways to leverage these new innovations is to apply advanced industrial analytics to production data generated by sensors. Every piece of data provides unique opportunities for improving energy efficiency. 

In general, energy savings can be achieved in various ways: through change in daily behavior (switching of the light), through installations of more energy efficient equipment, through equipment maintenance, or through process optimization and ensuring the use within the best operating zones. Process and asset performance optimization is probably the biggest area for energy savings, but requires a deeper understanding of operational process and asset data (available in the historian).

Analyze, Monitor and Predict W.A.G.E.S. consumption

Subject matter experts such as process, operations and maintenance engineers have deep knowledge of the production process. The major process related energy consumers include Water, Air, Gas, Electricity and Steam (WAGES) and can be directly or indirectly analyzed through all sensor data. The data can be descriptively analyzed to determine what has happened, which can provide a better understanding if a long period of performance can be assessed. Sometimes, certain issues happen only a couple of times per year, but can have a big impact on energy consumption (a trip causing a shutdown). Discovery analytics helps to understand what has happened and through diagnostic analytics the organization can start monitoring the performance of the site. 

Since asset performance is contextualized by the process they function in, the best operating zones or performance windows need to be extracted from actual process behavior rather than theoretical data. Based on the historical data, fingerprints with an energy consumption focus can be created to monitor good and bad behavior. Additionally, monitoring live operational performance can be used for predictive analytics: performance downstream is caused by behavior an hour or more upstream.

Practical Use Cases

There are already multiple instances where advanced analytics was successfully used to analyze, monitor and predict the process and asset performance of energy management.   

One example is related to energy consumption within the cooling water network. A large number of reactors were consuming cooling capacity from the utility network for cooling water. Sufficient cooling capacity is critical for many of these reactors, as thermal runaway could occur when the available capacity is insufficient. To avoid this undesirable situation, advanced analytics was set up to monitor the cooling capacity in real time. Early warnings were created and only triggered on actual problematic situations, avoiding false positive alarms that could be triggered by measurement noise or spikes in the data. Upon receiving a warning, there is ample time for the process engineer and operators to re-balance the reactors and deprioritize other equipment so that the critical ones can consume the maximal cooling capacity and overall energy consumption is within target boundaries. 

Another example is a predictive maintenance case for fouling of heat exchangers. In a reactor with subsequent heating and cooling phases, the controlled cooling phase is the most time-consuming. Fouling of the heat exchangers increases the cooling time, but scheduling maintenance too early leads to unwarranted downtime and scheduling too late leads to degraded performance, increased energy consumption and potential risks. To enable timely maintenance a cooling time monitor was set up, extending the asset availability, reducing the maintenance cost and safety risks. All these benefits, including controlled energy consumption, ultimately led to 1%+ overall revenue increase of the production line.

Continuous Improvement 4.0

In general, finding and solving root causes for process deviations and anomalies results in a more energy efficient operation. Monitoring the live production performance allows for control of various production parameters, including energy consumption. When the total energy consumption of a specific year is taken as a base line, monitoring of performance against corporate goals becomes possible. 

Besides the oil & gas industry, energy management is also important in other process industries. Covestro, a chemical company, initiated three major energy savings projects for their polyether plant in Antwerp as part of the energy savings goals and ISO50001 directives. Self-service industrial analytics solutions were implemented for online detecting (including root cause analysis and hypothesis generation), logging and explaining unexpected energy consumption and for comparing the results with the reference year 2013. Using specific formulas and calculated tags, various energy consumers are monitored and controlled. Through monitoring the performance against the reference year, it is shown that the energy consumption is effectively decreased year over year, meeting their corporate goals. More importantly, with a growing knowledge and insight into the production process, Covestro is continuously improving their overall performance.

Energy management is not new; many companies have a structured energy management program in place. New self-service analytics tools allow subject matter experts to analyse, monitor and predict process and asset performance, which can result in a huge contribution to meet the organizational carbon footprint goals. Especially when the low hanging fruit for energy savings has been picked and more knowledge is needed to improve operational performance, with the added benefit of improving overall profitability and increased safety.  

Edwin van Dijk is VP of Marketing with TrendMiner.

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