In this week’s edition of the resistance to the effective use of instrumentation Oliver Grievson examines the subject of data management and the phenomenon of data richness versus information poverty and how this causes a resistance to the effective use of instrumentation
The modern water utility, depending upon its size, will produce a vast plethora of data from its instrumentation everyday. Let us take the example of water company a. It has 500 wastewater treatment works with each of them having monitoring on the final effluent for BOD, Suspended Solids, Ammonia and flow . Converting this into numbers this is 2,000 instruments recording at a frequency of every 15 minutes (or 96 times a day. The number of individual readings of data is equal to a staggering 192,000. In practice of course not every treatment works is monitored with an instrument and not at a 15 minute interval but of course some treatment works have a lot more than four instruments. The point of this quick calculation is that the water industry worldwide produces individual pieces of data numbering into the billions of different analysis everyday of the year. It would be fair to say that the industry is “data rich.”
The practicality of this is that it is virtually impossible to economically analyse all of this data! So what is the solution?
Quite simply tailor the data and convert it into the information that you need and for your end customer (within the business). So what does this look like?
The diagram shows typical stakeholders and is not meant to be all inclusive but includes groups of people who need to see different amounts of data and different amounts of information and different levels of information. The billing department needs to measure what the consumption of consumers is and bill them appropriately. The operators at the treatment works needs to see how the process is working and make adjustments as necessary.
In real terms though what does this actually mean?
Let’s take a typical wastewater treatment works with an activated sludge plant. The treatment process would typically have an aeration source, MLSS measurement, dissolved oxygen measurement, RAS pumps, RAS suspended solids, flow measurement and maybe some quality measurement as well in the main reactor. If the settlement tanks are included as well then rotation sensors and sludge blanket detection can be added as well, if the belmouths are actuated then the amount that the actuator is open. Additionally add the amount of sludge wasted.
Using the exercise that we performed earlier our activated sludge plant has 10-15 instruments measuring every 15 minutes or 960 -14,400 different pieces of data a day. This is an underestimate! In order for the operators to operate this work data analysis becomes the majority of that person’s day. In reality what can be done? With a bit of data manipulation there are only a few pieces of information that the operator actually needs reducing the data to a more manageable level.
|Inlet Flow||Average & Peak Flow (Inlet & RAS)|
|RAS FLow||Sludge Age|
|SAS Flow||Average Mixed Liquor Concentrations|
|MLSS Concentration||Low, Average & High DO Concentrations|
|RAS Concentration||Amount of Sludge Wasted (kg)|
|DO Concentration||Average & Peak Sludge Blanket Level|
|DO Control Valve position||SSVI3.5|
|Settlement Tank Blanket Levels||Solid Flux Ratio (Actual v design)|
|Effluent Ammonia Concentrations||Total & average load|
|Effluent Solids Concentrations|
|and so on…|
|Amount of data: 14,400 figures||Pieces of Information: 15|
This is a grossly simplified example and the point being is that the difference between amount of data that is produced versus the amount of information that is needed to run the treatment process is vast.
Now to convert the data into information is an incredibly large task, especially to tailor it to the different stakeholders and it is this enormity of the task to convert the data that it is produced by instrumentation and converting it to information that creates the resistance to the effective use of information.
There are some companies in the industry that are attempting this task at this very time updating the systems that they have to take into account the different need of the stakeholders and of course the company and it will be these companies that hold the advantage in the future.