Data Quality

Primodal has found that facilities are increasingly operating and logging data from a number of online in situ sensors. Post-processing of that data to ensure its validity is expensive. Primodal has developed an expertise and a number of software tools to analyse the quality of data with an aim to minimize or eliminate that validation effort. Owners should feel confident that the real-time data they have is accurate and Primodal has the tools to do just that. You’ve made an investment, now let Primodal help you protect it.

On-Line Data Quality

How accurate is your real-time data?

Logging of in situ sensor data is becoming more cost effective each year, but the value of that data can only be realized if it has integrity. Your sensor infrastructure may be logging vast amounts of raw data (every few seconds in some cases), but validation of the data is often done on an ad hoc basis. To be truly reliable and accurate, validation of the data should occur in real time, rather than during a post-processing period that may occur at irregular intervals.

Costly consequences arise when clean data is needed for control purposes (and reliability is doubtful). Similarly, time and resources are often wasted when analyses are performed on unvalidated historical data sets.

Primodal’s software tools validate data in real-time. These comprehensive uni- and multi-variant tools can be integrated directly into your SCADA system to ensure that validation of the data is done as the data is being stored.


Primodal is committed to ensuring that ‘clean’ data is being stored and when problems are detected, appropriate alarms are triggered.


This means better, cheaper maintenance scheduling and better, cheaper data.

Historical Data Evaluation

Is it clean enough to tell an accurate story?

Many facilities are increasingly using and logging data from a number of different sources. This infrastructure logs vast amounts of raw data, but that data may not have been validated at any time as typically, a systematic approach to the data evaluation is not in place.

Primodal has developed an expertise and a number of software tools to analyse the quality of historical data with an aim to minimize that validation effort. Owners should feel confident that their data is accurate and that their data tells an accurate story. Primodal has the tools to help. You’ve made an investment, now let Primodal help you maximise the information you can extract from that historical data.


Your data contains a great deal of information about the wastewater treatment plant operation and performance variability but, in many cases, confidence in that information is lacking due to the inherent errors in that raw data.

When a significant investment is made in lab and sensor technology (and this is the case in most facilities) a small and incremental investment in data reliability modelling makes all the difference, not only to data quality but subsequent decision-making and future investment.

Uncertainty Analysis

What is your risk of failure?

Traditional wastewater treatment design has involved the use of safety factors to account for unknowns and natural variations in the water to be treated. This has lead to overly conservative designs and underloaded plants that are sometimes difficult to operate. More and more frequently nowadays, designers are relying on models to validate designs, in an effort to deliver smaller cost effective systems. However, all too often inexperienced use of these models without a clear quantitative understanding of the uncertainties in that model has led to inappropriate designs by reputable firms.

In an effort to better quantify the risks in the engineering decision process, Primodal is leading the efforts for coupling dynamic modelling to explicit uncertainty analysis. Primodal has proven this innovative approach effective in the field. By quantifying the uncertainties, Primodal is able to provide insights into plant performance for a variable and uncertain future. This provides you with an opportunity to realise cost savings and/or process improvements and is the next quantum leap in model-based design. Safety factors have served their purpose, but blindly applying them is inconsistent with what we know today. The reality is that we now have the tools to better quantify the uncertainties in a design and as a result we can offer an analysis that quantifies your process risk of failure.

The benefits are clear and include things such as the:

  • Evaluation of the reliability of plant to meet future effluent targets
  • Real time control and effect on process performance
  • Evaluation of process change impacts and/or the addition of new unit processes
  • Optimisation of CAPEX / OPEX investments
  • Equipment optimisation (i.e. pump sizing, blower sizing and turn down capabilities..)