Improving Energy and Sustainability with Cloud-Based Services
A “copilot in the cloud” enables collaboration between process plants and service providers to improve operations.
Energy improvement offers enormous potential for variable cost reduction and improved environmental performance. These types of improvements can be fast tracked and amplified via internet-based collaboration, but there is a risk that outsourced services can lead to a hollowing out of site expertise. Other potential issues include failure to account for the impacts of energy-improvement projects on process throughput, yield or reliability.
Collaborative services with first-principles engineering models and analytics can address these issues. One way in which they can is by simultaneously taking into account both process optimization and energy-use reduction to improve plant and human performance.
Most process plants do not operate at peak efficiency, resulting in substantial added costs and increased emissions. One of the main areas for improvement is often energy use. This is illustrated in figure 1, which compares energy use against a thermodynamically and economically achievable minimum. This example uses an energy metric dubbed the index of best technology. The index is a calculated value based on an optimized process configuration. The optimum target energy benchmark is defined as 100.
Once the optimum is defined, the actual energy-use index for the plant being surveyed is calculated as the ratio of actual energy use divided by the target, in percentage. For example, if the plant is using twice as much energy as the benchmark, then the index is 200. In effect, the index value compares current energy use against the best available technology in the market.
Even relatively efficient plants typically use significantly more energy than the optimum benchmark. Figure 1 shows a trend of the energy-use index for several hundred sites. For example, in the refining and upstream industries, even the best performers (right-hand end of the scale) have a best technology index well above 100. In numerical terms, this represents an opportunity for significant improvements.
Typical savings potential projects that require little or no capital expenditures are:
- Energy savings and CO2 reduction of 3 to 11 percent.
- Production increase of 1 to 2 percent.
- Maintenance cost reduction of 1 to 2 percent.
- Availability increase of 1 to 2 percent.
These figures easily can be doubled for capital projects.
For a typical refinery or petrochemical plant, energy costs are $200 to $300 million per year, so expected savings often amount to tens of millions of dollars. Unlike investing in additional capacity or changing the product mix — both of which entail risk due to reliance on predictions of market conditions — energy savings always increase profits. In addition, energy systems often constrain processes and throughput, a hidden cost that can be considerable.
For example, process compressors can be limited by a turbine drive’s capacity and efficiency. Steam and condenser operating conditions, or degradation of the turbine, can mean the drive reaches its limit before the compressor does. In another example, unit throughput can be limited by the amount of heat a process furnace is able to deliver. Energy-related bottlenecks often constrain throughput of high margin processes by 2 to 3 percent.
Process plants often struggle to address these and other issues due to a lack of skills or awareness, or simply inadequate bandwidth due to firefighting day-to-day problems. This can prevent improvements from being identified, realized or sustained.
Operating companies typically have turned to internal or external consultants to perform studies. These deliver some value but often have a patchy record of long-term sustainability and improvement. Outsourcing services provided via the internet, also known as cloud-based solutions, are helping to ensure sustainable improvement where other methods may have failed in the past.
Head-to-Head Comparison: On-Premise vs. Outsourcing vs. Collaboration
Cloud-based monitoring services are seeing wider use, especially as a means of connecting site data with remotely located experts. This allows processors to receive ready support when the in-house workforce is limited either in number or skill set.
With a skills shortage being a consistent industry issue, these types of services can offer value and productivity gains by globalizing monitoring, maintenance and support activities. Third-party service providers often have access to analytical and simulation tools that help to analyze and resolve site problems.
FIGURE 1. One measure of plant energy efficiency is to compare the ratio between the current site (or unit) energy consumption divided by the optimum site (or unit) energy consumption, on a scale where 100 equals optimum performance.
Before engaging a service provider, however, plant owners should ensure that:
- There is no dependency on one particular supplier.
- In-house expertise is not hollowed out.
- Supplier recommendations and expertise are independent in nature and not just a means to sell a particular product.
It is important to emphasize that not all cloud-based monitoring services are created equal. They can be divided into three basic types, defined where the tools and expertise reside:
- Traditional, on-premises tools.
- Outsourced services.
- Collaboration services.
Traditional On-Premise. In this type of service, the analysis tools and models as well as the experts that run them are all at the plant site. The operator analyzes plant data and equipment performance. If he decides that something needs to be done — for example, getting someone to inspect a machine, or ordering chemicals — the operator communicates with external suppliers. This system works well if the site capabilities are extensive, but it may be vulnerable if expertise is lost.
Outsourced Services. In this configuration, the data is exported to a third-party service provider running their own analysis on the results. Once analysis is complete, the service provider provides feedback and recommendations to the plant. This type of system is effective, at least in the short term, because the expert recommendations can add substantial value. One drawback is that the service does little to build internal plant capabilities. Because there may be little transparency in how the answers were achieved, it can reduce the insight and understanding levels of the on-site teams.
Collaboration Services. The data and tools — often, digital twins — are hosted in shared-access storage on the internet, and they are accessible to both the operating site and third-party experts. Because the models, analytics and dashboard are accessible to all, site engineers can investigate and learn for themselves in collaboration with the service provider. This provides the benefit of expert skills to fill gaps on-site while also developing operator skills.
FIGURE 2. Digitally replicating live plant operating data and economic data in the cloud allows cloud-based service providers to provide remote advice and assistance for improving plant operations in a collaborative manner.
To make the collaboration services approach work effectively, the service provider must have:
- A high degree of technical expertise with respect to process plant operations.
- Robust capabilities for online streaming and management of operations and maintenance data.
- Efficient, automated algorithms and technology to process the information and generate insights.
Due to its benefits, the collaborative approach is gaining favor at many operating facilities. As a result, it is being offered by an increasing number of third-party service providers.
Such programs create a high fidelity, molecular-enabled (kinetic) digital twin of the refinery, petrochemical plant or other process plant in the cloud (figure 2). The digital twin in the cloud gathers data from the plant’s distributed control systems (DCS), historians and laboratories. It also gathers information from other sources such as feedstock and energy pricing, including real-time markets. Energy supply, internal production and distribution costs thus can be optimized on a real-time basis. Energy-related key performance indicators (KPIs) and energy balances are calculated and displayed for monitoring purposes.
FIGURE 3. At one plant, reducing energy consumption using standard energy-management information systems (EMIS) calculations resulted in only $80,000 per year savings. Adding process optimization calculations to the analysis generated savings of $10.4 million per year.
This data is monitored and analyzed constantly by subject-matter experts with troubleshooting and optimization experience. They provide insights for improving plant performance in real time through cloud-based data sharing.
Such programs also provides predictive capabilities that improve upon purely reactive approaches. The molecular-enabled, digital twin is able to calculate equipment-health parameters that cannot be directly measured by sensors. This creates opportunities to identify and mitigate issues before they constrain or impact performance.
Reactive and proactive advice and recommendations are provided through an online, cyber-secured collaboration portal. Using a secure portal allows for real-time discussion and idea exchange among multiple external experts and plant-based engineering, operations, maintenance and planning groups. Advice and recommendations can be pushed out to plant personnel through emails and texts. Data and information also can be sent to existing plant-control and -monitoring systems.
These types of service agreements can improve day-to-day operations, and they can have longer-term positive impacts on maintenance and asset integrity. Cloud-based collaboration allows plants to circle back to production planning, enabling the plant to achieve its full potential at all times in the most efficient way possible.
Cloud-based collaboration also allows manufacturers to overcome barriers of time and geography. Co-pilots located in different time zones serve to provide a seasoned engineer on duty around the clock, acting as an energy watchdog.
FIGURE 4. A coordinated program between a European refinery and a cloud-based collaboration service provider, covering all aspects of energy management, resulted in an overall energy saving of 20 percent.
The following examples show how cloud-based collaboration can be used to improve process plant operations.
Solving Energy Problems by Adding Process Considerations
In a fluidized catalytic cracker (FCC) at a refinery, simplified energy performance indicators (EnPIs) drove the wrong behavior. In this FCC, there was an opportunity to reduce cooling water temperature by resolving an issue on the cooling towers. This colder cooling water could be used in one of two ways:
- Improve condenser vacuum and increase the efficiency of a condensing turbine, thus reducing steam demand and saving energy.
- Improve condenser vacuum and increase the efficiency of a condensing turbine to debottleneck the compressor being driven by the condenser.
Figure 3 compares what happens when only energy savings are considered vs. what happens when energy savings and process performance are analyzed together. The “energy benefit” case results in a small saving of steam, amounting to $80,000 per year, by improving the standard EnPI metrics of total energy use and specific energy consumption. In the “yield benefit” case, the unit severity increases. This is evidenced by the increased coke yield and the light, higher value material yield. The EnPIs of total energy use and specific energy consumption have increased, driven mainly by higher coke burn. However, when corrected for the improved process performance, the index of best technology decreases. The profitability is better, with more than $10 million per year in increased value. In this case, the index is aligned with the yield drivers and, therefore, does not penalize profit optimization.
In this example, optimization to generate operating targets — considering both energy and yield — were calculated using a single simulation platform with an integrated process and energy model.
Comprehensive Energy Management
A European refinery wanted to implement a phased approach to energy improvements. The first phase of the project involved a profit-improvement program focused on opportunities to improve yield and reduce energy. By using specialized software to analyze energy consumption and make changes via the control system, the plant cut energy consumption by 2.7 percent across the site (figure 4).
At the same time, the cloud-based monitoring services provider and the plant jointly participated in a strategic energy review to identify specific energy-efficiency improvement projects. Implementing these projects over a two-year period reduced energy use by 11.9 percent. Steam optimizer software then was added, driving energy consumption down another 4 percent. Finally, the team developed energy metrics to monitor performance of the entire plant, saving an additional 1 percent in energy costs. Overall, the four-year program reduced energy costs at the refinery by 20 percent.
In summary, the potential value from reducing energy consumption is quite significant in most process plants. While on-site solutions can certainly help cut energy costs, human constraints may undermine the long-term effectiveness of consultancy studies using technical software tools.
To address these issues, several companies offer cloud-based collaboration. Such service providers are available to maintain the performance of the digital twins, help interpret data and make recommendations, working with process plant personnel so they can take appropriate action.