Before determining the EnPIs needed to adequately monitor and measure energy and
operational performance, you first need to determine the scope of each metric. Are you
attempting to track and manage performance at the plant, area, line, or machine level?
You need to determine what business questions you are trying to answer and determine
exactly what data points are needed to formulate the EnPIs to answer those questions.
The more comprehensive your plan to track energy within your facilities, the more
attention you will need to pay to systems, energy, and heat flows. This is especially true if
the products (outputs) of one process become feedstock or energy supplies (inputs) to
other processes. The simple way to think about this is to take an “outside-in approach.”
First, “draw a circle” around your plant and determine which EnPIs and underlying data
will be needed to track energy performance at the plant-wide level, whether comparing
this site to its peers or simply trying to show time series improvement in the plant. This
methodology should be repeated at successively deeper levels within the plant until you
have reached the desired level of granularity (e.g., area, unit, line, or equipment levels)
(see Figure 27.1).
There are two approaches to planning and selecting the set of EnPIs and underlying
measurements that will satisfy your business needs while balancing the practical need to
Enterprise
Region/division
Plant/location
Process area/
department
Machine/
meter
Figure 27.1. Levels of energy tracking within an enterprise.
HOW TO CHOOSE EnPIs 351
obtain reliable data in a cost-effective manner. The first approach, which is more of a topdown
approach, is to determine what business questions you are trying to answer and
then determining exactly which EnPI data points are needed to provide those answers at
each level within the site. This approach gives you the theoretically perfect set of data and
EnPIs. The second approach, which is more of a bottom-up approach, is to start with data
availability. Which meters and process measurements are readily available on a network
or data historian? Determine how closely these measurements match the theoretical set of
data established in the first approach. This more pragmatic approach will provide a road
map for the investment needed to close critical measurement gaps and help you to
formulate a list of missing instruments and meters. The cost/benefit analysis associated
with this approach should stimulate some spirited discussion with plant stakeholders to
arrive at the appropriate balance of data availability and infrastructure cost to obtain the
data needed to satisfy EnPI tracking requirements. It is often more useful to start with
EnPIs that are “directionally correct” rather than “theoretically perfect.”
To measure the energy performance associated with a specific functional area (e.g.,
machine, line, or process area), it is necessary to account for all of the energy flows in order
to create a meaningful EnPI. As mentioned earlier, the methodology involves drawing a
boundary around the functional area and making sure that all energy inflows and outflows
are metered/measured and quantified. This will provide a complete picture of energy and
operational performance when comparing this functional area with similar functional areas
either on one site or across your enterprise. Examples include measuring electrical energy
or volumes of natural gas or steam flowing into a system while measuring the outflows of
steam, compressed air, process gases, and so on from the system.
Ultimately, you are trying to measure and account for the complete “energy balance”
at each machine, line unit, system, or process area. While simple EnPIs, which do not
include all of the energy flows from a given area, can be created and add some analytical
value, the lack of a complete energy balance can restrict their use when trying to compare
functional areas and share best practices across different locations.
Another issue to consider in creating EnPIs is whether to calculate simple metrics
based on “energy in” divided by “production out” (or the inverse “production out”
divided by “energy in”) or to create efficiency metrics based on “total energy or work
in” divided by “total energy or work out.” Consider the following example to illustrate
this point:
Consider an air compressor with a kWh meter on the input and airflow totalizer on the
output. The simple EnPI would be total cubic feet of air produced per kWh of electricity
consumed. This might be effective if the compressor is fully loaded and runs in a consistent
mode of operation. However, a more complex measurement of “work out” divided by “work
in” may provide a better, more useful metric to ensure that the compressor is optimally
loaded and sequenced. For this later metric, more instrumentation is required. For example,
to determine the work done by the compressor, we need to know both the inlet and the outlet
pressures, in addition to airflow. This could be further enhanced by considering inlet
temperature and humidity content as well. The point is not to unnecessarily complicate the
EnPI metric, but rather to “right size” it to address the business/technical issue at hand in the
most cost-effective way possible