Control Chart Methodology for Evaluating CEMS Data

Louis NicholsUS EPA Clean Air Markets Division;

Emissions Monitoring BranchNichols.Louis@epa.gov

Intro

Control charts are an effective tool for identifying unusual shifts in a parameter that should be fairly constant for a given operating condition

One use is to detect bias from probe leaks in dilution monitoring systems. Calibrations do not necessarily detect system leaks.

EPA reference method systems utilizing the dilution probes can have air in-leakage (harder to control-chart) – check for unexplained CO2 shifts (see previous RATAs)

EPA CO2 Control Chart Auditing

EPA runs the control chart audit on CO2 data quarterly so as to minimize in the future the amount of data that could be called into question

This audit is run in an “ad-hoc” manner after the data has been submitted to EPA.

EPA continues to refine this technique to “weed out” false positives.

EPA also looks to use this sort of auditing on other parameters as appropriate to identify questionable data

Example Hourly CO2 Data

Hourly CO2 Concentration Data

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Data Used for Analysis

CAMD uses CO2 concentrations used as the control parameter given its relatively low variability in any given load band

Use normal load data – avoid start-up, shut-down, and very low loads

Avoid substitute data (part 75 only) – use method of determination code (MODC) of “01” – measured data only

Need date of completion for last CO2 RATA

Data Preparation Identify load bin to evaluate

– CAMD batch evaluation uses the most used load bin (load bin with highest % usage)

– Other load bins can be evaluated as desired

Minimum Criteria for Daily Data:– CAMD discards all CO2 data for days with less than six quality

assured measured values in the load bin of interest

Determining Baseline Mean and Control limits

Baseline data is compiled from the 30 calendar days following a successful CO2 RATA (more if less than 15 days meet the minimum criteria)

The following values are calculated for the baseline period:– The daily average CO2 concentration, (ACi);– The baseline mean CO2 concentration, (AAB); and– The standard deviation of the daily average CO2 values over the baseline

period, (SDB) The control limits are set based on the standard deviation of the

baseline data.

Normal Distribution

Determining Control Limits

EPA uses AAB ± 3 SDB as the audit level as 99.7% of the daily averages should fall within this range.

Therefore:– The upper control limit is:

XUCL = AAB + 3 SDB; and

– The lower control limit is:

XUCL = AAB - 3 SDB

EPA recommend that sources use AAB ± 2 SDB as a control warning level

Evaluating Daily Data Against the Baseline

Compare each daily average CO2 concentration value to the calculated control limits.

Flag any day where the average CO2 value is outside of the control limits. – High and Low deviations are tracked separately

Whenever 7 or more consecutive daily averages are out-of-bounds (consistently high or low) the data for the unit is flagged as having developed a potential bias, and possible sampling system leak.

Identify Data Outside of the Control LimitsDaily Average CO2 Concentration Control Chart

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Baseline DATA Warning UCL Warning LCL Conc OOB (V)

Leak Detection and Prevention of Data Loss (i.e., Avoiding the audit letter)

EPA encourages all facilities with CO2 CEMS data to follow the procedures described in this presentation to identify early any questionable data as an added QA step.

If you see the CO2 drop for more than a few days, investigate to find the cause and take appropriate action.

EPA recommends investigation at the 2 level In such cases, consider the data validity for other

parameters that might also need to be invalidated. Document all findings.