Key Performance Indicators (KPIs) are important for monitoring the performance in the industry. They can be used to identify poor performance and the improvement potential. KPIs can be defined for individual equipment, subprocesses, and whole plants. Different types of performances can be measured by KPIs, for example energy, rawmaterial, control & operation, maintenance, etc.
Benchmarking KPIs with KPIs from similar equipment and plants is one method of identifying poor performing areas and estimating improvement potential. Actions for performance improvements can then be developed, prioritized and implemented based on the KPIs and the benchmarking results. An alternative to benchmarking, which is described in this paper, is to identify the process signals that are strongest correlated with the KPI and then change these process signals in the direction that improves the KPI. This method has been applied to data from a combined heat and power plant and a suggestion are given on how to improve boiler efficiency.
Most of the KPIs can be applied to individual equipment, sub-processes, and whole plants.
2.1 Energy KPIs
The energy could be in different forms for example electricity, gas, coal, oil, biomass, steam, etc. The
produced output could e.g. be in units of tons/h, m3 /h, units/h, etc.
x Energy output / Energy input
x Energy input / Produced output
2.2 Raw-material KPIs
Raw-materials may not only be the main raw-material for a plant, it could also be water, chemicals,
x Raw-material input / Produced output
x Emission / Produced output
x Waste deposit / Produced outpu
2.3 Operation KPIs
The main Operation KPI is the Overall Equipment Effectiveness (OEE)  and its individual parts.
x Percentage of scheduled operation time, over a time period, e.g. a day, week, month, etc.
x Percentage of actual uptime of the scheduled time, over a time period.
x Percentage of production rate of max rate for produced product type, over a time period.
x Percentage of full quality products of the production, over a time period.
2.4 Control performance KPIs
Control performance may influence product quality, production speed, equipment wear, etc.
x Number of control loops in manual mode / total number of control loops
x Variance of control error (set-point – measured value)
x Settling time after a set-point change
2.5 Maintenance KPIs
Too little maintenance causes an excessive number of unplanned stops resulting in lost production and
emergency maintenance. Too much maintenance causes large maintenance costs and lost production
during each planned maintenance.
x Maintenance costs / Produced output over a time period.
x Maintenance time / Produced output over a time period.
x Number of alarms over a time period.
x Same KPIs as for Operation KPIs and some of the Equipment KPIs presented below.
2.6 Planning KPI
Planning and scheduling impacts how well plant capacity is utilized. Since deriving the optimal
production plan and comparing it with actual production, is not within the scope of the KPI calculations.
Instead a KPI based on adherence to plan is suggested.
x Integrated sum of only positive values of (planned – actual production) over a time period.
This KPI is not improved if production catches up later.
2.7 Inventory and buffer utilization KPIs
Large inventories are expensive, too small inventories may cause production disturbances. Buffer tanks
should dampen disturbances, if they don’t they are either used in the wrong way or they could be replaced by a pipe.
x Throughput rate / Average Inventory
x Variance of buffer level
x Share of time buffer level is > 95% or <5% over a time period.
2.8 Equipment KPIs
These KPIs can be used to follow the condition of equipment and in some cases also predict when
maintenance will be required.
x Different types of efficiencies e.g. heat transfer rate of heat exchangers, pump/fan efficiency,
drying efficiency, etc.
x Equipment wear (based on e.g. operating hours, speed, load, startups).
o Number of valve openings for a valve or total valve opening travel distance.
x Vibration amplitude of an equipment.
x Measured – Predicted performance
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