Industry: Manufacturing - Lumber
Location: Maine, United States
No. of Sites: 6
Total Savings: $80,000 annually
Additional Benefits: Dedicated support from Enel X professional services team
A subsidiary of a large industrial conglomerate focused on processing Eastern White Pine lumber for retail building supply, home renovation, and industrial applications. The facility produces roughly 75 million foot board meters per year. The site is a single lumber yard with multiple meters for different buildings.
With extremely energy-intensive equipment like saw mills, kilns, planers, dust collectors, and conveyors, managing energy costs is a big priority for this lumber manufacturer. In addition to reducing overall consumption, with so much heavy equipment on site, peak demand charges were becoming a major operating expense. Without visibility into which equipment was causing demand spikes, it was difficult to implement remediation strategies. The facility also had a number of energy saving practices in place, but no way to determine the impact of these measures.
With energy costs driving a big percentage of their operating costs, the Company wanted to take control of peak demand charges, make sure that they weren’t missing additional opportunities for savings, and track the performance of already established procedures – something they couldn’t do without real-time access to their energy data. In 2013, the Company deployed Enel X’s energy intelligence software. To ensure that the efforts were successful out of the gate, the Company also engaged Enel X’s professional services team. A dedicated energy analyst regularly pored over the Company’s energy data and met with key stakeholders to identify anomalous energy usage and potential savings opportunities.
Savings Example 1: Peak Demand Management—with a Twist
Peak demand management is one of the easiest ways for large industrials and manufacturers to tame energy costs, but for this lumber manufacturer the solution wasn’t as simple as optimizing a building’s start-up schedule.
The Company has a large back-pressure turbine on-site that generates electricity from excess production of steam. To generate the steam, the Company uses excess wood chips from their facility as fuel. That process, while cost-effective, requires that the boiler is taken offline twice each day to clean the ash grates. As soon as the back-pressure turbine goes offline, the Company starts drawing that power from the grid, creating a demand spike more than 400 kilowatts (kW) above their normal demand. Armed with this data, the Company instituted a policy that the grates were to be cleaned at 8:00 pm, just outside the peak demand window. Doing so, would save the Company over $40,000 in annual savings. But the value of real-time data didn’t stop there. After reviewing the real-time data, Enel X’s professional services team noted that while the cleaning crew was usually adhering to the proposed schedule, on occasion - once or twice a month - they’d take the turbine off line a little early. While starting 15 minutes early didn’t seem like a big deal to the cleaning crew, it had a very real negative impact on the Company’s energy budget. The Company used the data to better communicate with the cleaning crew the importance of sticking to the maintenance schedule and to monitor operations to ensure that good policy translated to good practice.
Savings Example 2: Measuring Air Leak Reductions
Air leaks are one of the most common forms of energy waste; a leak as small as 1/16th of an inch can cost hundreds or thousands of dollars. To address this challenge, the Company initiated a comprehensive leak reduction program. With all of the air compressors tied to a single meter, it was straightforward to use Enel X’s software to benchmark the results and see improvements in real time. At the beginning of the project, it was estimated that the Company would save upwards of $15,000. The program is currently underway.
Savings Example 3: Maximizing Break Shut Downs
In conditioned space, we often see night and weekend setbacks as one of the most common opportunities for fine tuning, but in a heavy industrial site like this one, mapping energy consumption to production schedules and shift schedules often shines a light on energy waste. At this site, the crew was trained to shutdown most heavy equipment during lunch and scheduled coffee breaks. Using Enel X’s “compare to past” profiling ability, the Enel X analyst evaluated how consistently each shift was setting back during breaks. Using this data and working corroboratively with the operations team, the Company was able to establish more robust shut down protocols, regularly dropping an extra 100 kilowatts during break - a savings measure that translated into nearly $4,000 in annual savings.
In addition to savings, the Company values Enel X’s ability to help make the budgeting and forecasting process more predictable. Enel X’s software also allows users to align energy usage with real-time Locational Marginal Price (LMP) data to estimate their partially hedged costs.
The North American lumber industry is highly competitive, so every opportunity to track and cut costs translates directly into a competitive advantage. With Enel X’s software and professional services, this company was able to realize $80,000 of annual savings.
With Enel X’s software, achieving savings isn’t just a one-and-done activity. This company monitors its real-time data to find new opportunities to save as well as ensure established practices at the facility continue to drive results. Key stakeholders also use the software to budget and forecast energy costs and prevent potentially expensive charges. For instance, Enel X’s software is configured to alert key operational staff if the facility is about to approach a new peak demand.
Expertise from Energy Analysts
By engaging with Enel X’s professional services at the start of the engagement, this Company was able to stay focused on what it does best: processing fresh cut trees and turning them into valuable lumber. Enel X’s team provided the dedicated set of eyes on real-time energy consumption, trends, peak demand charges, and other areas for operational improvements, working as an extension of the Company’s internal operations team.