Poorly designed and overly simplistic energy performance indicators (EnPIs) often drive energy savings at the expense of product yield or quality. However, a well-designed energy management information system (EMIS) can minimize energy cost without impacting production and, in some cases, can even enhance process performance.
Traditional energy monitoring applications mainly focus on improving energy-side key performance indicators for fired boilers and heaters efficiencies, energy intensity, utilities’ marginal cost, etc. These monitoring applications rely on inputs from various process measurement instruments, with temperature leading the way, to verify performance.
However, covering an expanded range of production parameters — including energy supply, demand and recovery, product quality and process yields — requires integration of the process with energy simulation, monitoring and optimization tools. This article shows how to overcome traditional barriers to energy saving by using rigorous process simulations to monitor performance and determine optimum operating targets for improving both energy and process performance.
Figure 1. Most sites, even relatively efficient ones, don’t perform close to the optimum performance benchmark.
The Energy Opportunity
Energy is the largest controllable operating cost at most process plants. A typical refinery or petrochemical plant may spend $200–300 million/y on energy — so cutting just 3% in energy cost can save $6–$9 million/y. Such energy savings always result in direct bottom-line benefits, unlike adding capacity or changing product mix, which depend on anticipated market conditions.
Energy production and distribution systems often constrain processes. For example, a process compressor can be limited by its turbine drive’s capacity and efficiency, so steam and condenser operating conditions or degradation of the turbine can mean the drive reaches its limit before the compressor does. In another example, the amount of heat a process furnace is able to deliver can restrict unit throughput. Energy-related bottlenecks often curb throughput of high-margin processes by 2–3%.
One challenge is understanding the amount of potential energy improvement. Plants typically compare themselves against their peers. However, this comparison only is meaningful if the leaders are highly efficient.
An alternative approach is to compare energy use against a thermodynamically and economically achievable minimum. Our company has developed an energy metric called the Best Technology (BT) index. The target BT index is calculated based on an optimized process configuration including reactor conditions, number of distillation column trays, etc., as well as pinch analysis for heat recovery and R-curve analysis for utility delivery. This enables the specification of all equipment for maximum efficiency.
Pinch analysis is a methodology for reducing energy consumption of processes by calculating thermodynamically feasible energy targets. R-curve analysis determines the hypothetical ideal utility system and fuel utilization for power and steam generation.
Repeating these optimization calculations for a range of feedstocks, operating severities and product yields determines a relationship between optimum energy use and process performance. The optimum target energy benchmark is defined as 100.
The actual BT index is calculated as the ratio of actual energy use divided by the target, in %. For example, if the plant is using twice as much energy as the benchmark, then its BT index is 200%. This index basically compares current energy use against that of the best available technology in the market.
Even relatively efficient plants typically use significantly more energy than the BT benchmark. Figure 1 shows a trend of the BT index for several hundred sites arranged in descending order along the x axis. In the refining and upstream industries, even the best performers (right-hand end of the scale) have a BT index well above 100%. There have been many instances of top performers saving 10–15% of energy, worth $20–30 million/y. Similar percentage reductions for CO2 emissions also are achievable.
Most EMIS software packages focus only on the energy supply side (for example, the efficiency of production of steam and power for use in the process), so their EnPIs don’t reflect the impact of feedstock effects or process yield. For instance, if energy consumption increases, they can’t indicate whether this stems from inefficiency, lower quality feedstock or the demands of higher quality products. These software packages may monitor equipment performance but often miss the chance to switch an item of equipment off when its output isn’t needed to support production.
EMIS software can become out of date and may get misused, and plant personnel may fail to exploit its full value. Consequently, sites don’t always act upon advice and recommendations provided by the EMIS because it’s seen as irrelevant.
An EMIS frequently doesn’t address the interaction of energy and production yield. Many plants highly integrate their energy systems with production processes, so changes in one area impact other areas significantly.
Complicating the problem are changes in staffing, particularly the loss of veteran staff and the push to adopt leaner operations, making it more difficult for work processes and practices to catch up with technology.
Nevertheless, many companies still use a traditional EMIS approach. This produces energy cost savings but can miss some opportunities by not considering the combined effects of energy use and process performance.
An Improved Approach
Adding process considerations can solve EMIS problems. For instance, simplified EnPIs drove the wrong behavior in a fluidized catalytic cracker (FCC) at a refinery. In this FCC, an opportunity existed to lower cooling water temperature by resolving an issue on the cooling towers. This colder cooling water would improve condenser vacuum and increase the efficiency of a condensing turbine, providing benefits in one of two ways:
1. reducing steam demand and saving energy; or
2. debottlenecking the compressor being driven by the condenser.
Figure 2. Data from the control system and historian enable experts to make recommendations for saving energy and improving process yield.
Conventional EMIS calculations for Option 1 show a small savings of steam, amounting to $80,000/y, by improving the standard EnPI metrics of total energy use and specific energy consumption.
For Option 2, the EnPIs of total energy use and specific energy consumption increase, driven mainly by higher coke burn. However, when corrected for the improved process performance, the BT index decreases. Profitability is dramatically better, with more than $10 million/y increased value. The BT index is aligned with the yield drivers and, therefore, won’t penalize profit optimization.
In this example, a single simulation platform with an integrated process and energy model performed the optimization to generate operating targets, considering both energy and yield. The resulting targets were embedded in the EMIS optimizer software.
Such an EMIS integrates energy production and supply with process modeling and optimization (Figure 2).
In closed-loop mode, the EMIS sends recommendations directly to the control system to adjust the energy system or process. It also produces energy-related reports and actionable recommendations for operators, a form of open-loop control.
Updating a standard open-loop EMIS to one with closed-loop optimization capabilities can produce dramatic improvements. For example, Air Liquide achieved impressive results at its Bayport, Texas, site. That facility produces oxygen, nitrogen and hydrogen, and supplies customers along the Texas and Louisiana Gulf Coast via an extensive pipeline network. It is Air Liquide’s largest industrial gas complex in the world.
After using an open-loop EMIS, including its optimization capabilities, for several years, the plant decided to implement a closed-loop EMIS to cover the steam system, cogeneration and boilers — plus the extraction/condensing turbines. The new EMIS allowed Bayport to operate in an optimum manner throughout the day, despite price variations in electricity supply every 15 minutes.
Closing the loop was like having the plant’s best engineer on duty 24/7/365 acting as an energy watchdog. It produced the lowest energy cost within process constraints against a moving target created by the need to meet customer product requirements and changing energy prices. In addition, the implementation of each closed-loop variable incorporated reliability assurance .
Figure 3. This European refinery project resulted in an about 20% overall energy savings.
A European refinery also achieved substantial benefits. KBC worked with the plant to implement a phased approach to energy improvements. The first phase involved a profit improvement program focused on opportunities to increase 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% across the site (Figure 3).
At the same time, KBC and the plant jointly participated in a strategic review to identify specific energy efficiency improvement projects. Implementing these projects over a two-year period reduced energy use by 11.9%. Then, addition of steam optimizer software enabled the plant to drive energy consumption down another 4%. Finally, the team developed energy metrics to monitor performance of the entire plant, saving an additional 1% in energy costs. Overall, the four-year program reduced energy costs at the refinery by 20%.
As these examples show, an on-site EMIS with optimization modeling software certainly can help cut energy costs. However, local staffing problems (i.e., fewer and less-experienced personnel) common at many facilities can undermine its value. No one at the plant may understand what the software is trying to tell plant personnel.
These types of situations can be addressed using the power of the cloud to allow collaboration beyond traditional boundaries of time and location. Data and recommendations of the local EMIS are fed to the cloud, enabling experts at the EMIS vendor to guide process plant personnel in taking appropriate action.
For example, KBC’s Visual MESA energy management system and its Petro-Sim modeling software, along with the control system’s process historian, can all feed data to the cloud. Then, KBC’s Co-Pilot service allows our experts to access and analyze the data, to make recommendations and reports — providing the plant with the expertise and insight needed to improve operations.
Update Your EMIS
Today, plants face a compelling need to reduce energy costs and improve yields without extensive and expensive equipment modifications — while ensuring energy enhancements don’t adversely affect process performance, and ideally improve it.
Improvements needed in EMIS software to address these issues include:
• process simulation to monitor performance and determine optimum operating targets by considering both energy and process performance;
• updated EnPIs with well-defined targets to track energy performance in a consistent way while minimizing feedstock and yield effects;
• site-wide energy management and optimization of utilities to deliver results and recommendations to the right people at the right time; and
• cloud-based support from the EMIS vendor to provide performance management and expert troubleshooting to resolve complex issues in real time.
Initial results of such an integrated approach show benefits can be substantial. Achieving 3–10% cuts in energy consumption or carbon emissions often is possible without capital investment in new equipment. Where energy systems are constraining process performance, sites have realized 1–3% increases in throughput or yield, with the synergy between process and energy optimization leading to benefits far greater than considering either in isolation.
1. Reid M. and Reitmeier, T., “Closing the Loop With Visual MESA. The Transition of Air Liquide’s Real-Time Utility Optimizer to Closed Loop at the Bayport, Texas Facility,” Industrial Energy Technology Conference, New Orleans (May 2011).
TIM SHIRE is product manager, Co-Pilot program, for KBC Advanced Technologies, a Yokogawa company, in Northwich, U.K. CARLOS RUIZ is product manager, energy management systems, for KBC Advanced Technologies in Buenos Aires, Argentina. Email them at [email protected] and [email protected].