Patent paralysis

OK, so I may have a different perspective on patents than others. This is due largely to the fact that a couple of the professionals that had the most influence on my growth as a poop engineer had a less than positive view of patents.  To protect the innocent and to avoid accusations of slander I’m not going to name names, but one individual had his idea stolen by a large firm who then patented it and tried to stop him using his own idea. Thankfully it mostly failed and time has healed most of the wounds.  The other individual was an innovator beyond compare and his ethos was to keep developing new ideas and applications to stay ahead of the game. He didn’t have the time or inclination to “waste” money on patents.

At this point I should also give the background that my little world is mostly the design and upgrading of publicly-owned poop plants.

So, with this background, let me set out why I think patents can be a bad thing.

Why I don't like patents...

1. They stifle innovation

The very purpose of a patent is not to enable someone to produce something, but to prevent anyone else from doing it except you in order to have an advantage. If you patent an idea, no-one else is going to do it, and in our little poop-plant world that stifles acceptance and further development. In other realms where you’re mass-producing consumer goods or medicines I can see this is OK and fair, but in my space it’s really tough to get anyone to innovate, so patenting an idea can kill it pretty fast. I don’t think many equipment vendors get this.

2. POTWs can’t specify “one-of-a-kind” technology

Hand-in-hand with stifling innovation, or partly the cause, are the rules that prevent most public utilities from specifying unique technologies. This makes it VERY difficult to do anything new. On the other hand, having just 2 or 3 vendors competing in the same space can be a major boost. I actually spend quite some time comparing technologies and my job is made a whole lot easier if there is more than one of a type.

3. No-one likes lawyers

Now don't get me wrong, there are many fine and upstanding lawyers in the world, many of whom have been good friends of mine over the years. And we definitely need lawyers to help us uphold the law.  BUT in the litigious culture of the Western World (sorry, I mean where the "rule of law" prevails, yawn), as soon as you threaten to bring in lawyers, we all get a bit weird. Engineers in particular get very uneasy around lawyers and their word games.  Heck, we're straightforward thinking, problem solvers. Please don't try to trip us up with what we mean when use certain words or opinions.  I've read a couple of patents and the language in them is awful lawyer-speak, seemingly preventing anyone from doing anything anywhere, ever. 

Overcoming Patent Paralysis

OK, so I've bad-mouthed one of the main mechanisms for encouraging and protecting inventions since the 16th Century, so do I have any suggestions for a better way forward?  I'm not a lawyer (phew, you say), so perhaps I'm not qualified to comment, but here are a few ideas for alternative ways of driving innovation through new ideas and inventions without using patents to stop it...

A. Stay ahead of the game

I mentioned this already, but early in my career I worked for a company that developed some amazing online instrumentation including online respirometry which, to this day, no-one has ever come close to matching.  I didn't appreciate it at the time but the ideas produced in that small firm were way ahead of their time and I believe that they only patented one mechanical item out of all the ideas and innovations they produced.  The ethos of my boss was to just keep ahead of the game. It turns out he's still 20 years ahead of the game!

B. Go open source

This is a radical idea, but one used by Elon Musk.  Don't prevent others from using your ideas, but let them have a go too, then compete to win.  Particularly if your ideas are radical and maybe in a whole new domain of their own.  Opening up your ideas to others will help spur research and more ideas from which you and the others will all benefit. Isn't this how research is supposed to be done? Rather than hiding your ideas or locking them down so no-one else can develop them further, consider opening the black box and learning from your competitors.

C. Copyright, don't patent

Did I mention I'm no lawyer, so I probably can't comment fully on this, but there are protections under law for published materials and ideas that are covered by copyright.  I think there are some weird loopholes in US patent law that allow you to pinch ideas for the 1st year after they're published if you have good lawyers (the experience of my other colleague I mentioned earlier), but still, if you have an idea and publish it, then you have some protections, I think.  Doing this, plus considering the "open source" approach is really what research should be about, I think. But only if you want to encourage innovation!

OK, so equipment vendors and those of you who've patented a gazillion inventions, let me know why I'm off base as usual!

A Value-based Approach to Innovation

Innovation is a very popular topic these days. A quick Google search on the word "innovation" shows that the use of the word over time has gone through the roof in the back half of the 20th Century until now:
Use over time for: innovation

I've read in many places (such as here) that the word innovation hasn't always had the positive connotation it has today, but in previous centuries was a derogatory term akin to the idea of "winging it" where you had to improvise because you hadn't planned properly or hadn't thought things through.

In the modern use of the term it's really supplanted the word "invention" to simply mean "coming up with something new." To be honest I prefer the word "invention" in the engineering world because it carries the idea of actually planning something, but hey, we don't always get to choose the buzzwords doing the rounds these days!

Innovation = Risk?

I love new ideas and new technologies, and, in my own poop-treatment world, I'm bowled over by the number of initiatives, businesses and people driving innovation. However, what I don't see is a lot of discussion on handling the risks associated with innovation, or - more specifically - a way of gauging whether the risk of the innovation is worth it in the long run.  I don't want to be a party-pooper when it comes to innovation, but I also hate to see us innovating just for the sake of innovation.

The Concept of Value

I've been through a number of "value engineering" (VE) exercises, both as the evaluator and also as the recipient of the evaluation.  It can be a frustrating process but I think it's a worthwhile exercise.

A fundamental concept in VE, is that value is quality over cost, or benefit over cost. So in principle you can improve the top line of quality or benefit and increase value with the same cost, or marginal cost increase, or simply look to reduce costs whilst trying to maintain the desired quality.  In the equation, we can use actual currency for each of the parameters if the quality can be quantified in monetary terms to give value in actual dollars, euros etc. More often though, quality is a non-currency improvement (e.g. more flow can be treated) or even a non-numerical benefit (e.g. more reliable, or more flexible).

So this equation can be used to assess value quantitatively or qualitatively.

Innovation Value

So, how about extending the concept of value to innovation i.e. an "Innovation Value" in which we see the value we get from innovation in terms of improved quality and/or lower cost, but include a term that takes account of risk too?  The equation would be something like:

This gives us now a way to conceptualize the extra value we get from innovating whilst also acknowledging and maybe even quantifying risk factors.

If the innovative idea has low or no risk, then Risk = 1.0 and we assess the idea the same way as any VE idea.

If the innovative idea has a high risk, then Risk > 1.0 and we can crank that factor up according to some scale that takes into account the risk profile.

I guess we can also think about innovation as a way to reduce risk, in which case Risk < 1.0 i.e. the innovative idea is less risky than the status quo.

Quantifying Risk - Conventional Approach

Risk is commonly assessed using a matrix of severity (or consequence) versus likelihood:

In this matrix, items in the "Acceptable" range can be scored with the Risk at or near 1.0, then items in the yellow range have increasingly larger Risk values from, say, 1.1 up to 1.5, and then items in the red zone can have larger multipliers of 2 or 3, or perhaps are simply designated "not acceptable," depending on the appetite for risk?   

It might look something like this...

Quantifying Technology Innovation Risk

Now the conventional approach to quantifying risk is subjective but very flexible.  In thinking about poop treatment and technology innovations, I can think of three factors that might influence how we score risk: Deviation from the Norm; Development Level; and Complexity.

1. Deviation from the Norm

How far does the innovation deviate from the "norm"? If the technology is simply an adaptation of an established technology or a simple add-on to something existing, then perhaps the risk is pretty low (Say a 1.0 - 1.2 in our matrix). However if this is a very different approach, using fundamentally different ideas and perhaps very different technologies that haven't been used in this particular application before, then it might be pertinent to use a higher risk factor (1.5 - 2.0).

2. Development Level

At what stage of development is the technology? The US government and others commonly use the "Technology Readiness Level" (TRL) to define how ready a technology is to full-scale application. Here's the version used by NASA:

So, if we have a technology that's already at TRL 9, we give it a risk factor near 1.0, but lower TRL values have increasing risk factors.

3. Complexity

Finally, is the innovation more or less complex (more to go wrong) than the conventional solution? If it has more parts and pieces that can go wrong, or relies on something more complex in order to make it work, then it should have a higher risk factor.  Conversely, if the innovation is inherently less complex than the conventional system, then it wouldn't be crazy to apply a factor of less than 1.0!

Pulling it all together

So, like a good engineer, let's take that simple equation, add in all my factors and come up with something way more complicated... well, maybe not too much more complicated (score me down on my innovation!) to give something like...

Hmm, I dunno, perhaps it needs a few exponents added to each factor to make it needlessly more complicated?!

Pondering on Corporate Growth vs Growing an Extended Family Business

I should start with the disclaimer that I do not have an MBA, and I'm no expert on business development.  However I have worked in several different offices around the world and have my ears open to what goes on in the business side of what I do for a living.

I've been reflecting upon the assimilation of engineering firms by larger firms, especially the takeover of CH2M by Jacobs that happened this past couple of weeks. In our growth-driven economy firms seek increased profits for shareholders, private or public, which drives certain behaviors.  One way to grow your stock price is through mergers and acquisitions. If done right, it benefits the stock holders in a big way.  However there will undoubtedly be some collateral damage as the now merged firm seeks to reduce overhead costs and keep the stock price up.  I've heard it said that a good merger is where 1+1 = 3 through synergies between the two original firms. I think that's unrealistic and you actually get 1+1 = 1.5 which is still better for either of the original "1" but less value than the sum of the two.  Then from that position of 1.5, you can continue to build your firm (with the added bonus of having removed one of your opponents!) Elementary math tells you 1+1 = 2, at best, so I think it's wishful thinking or downright dishonest to claim otherwise! In these mergers there will be financial winners for sure but there will necessarily be losers in terms of people losing their jobs and increased uncertainty for those remaining.

But I'm losing the train of my thought and the intent of this blog...

Something I've observed in many smaller businesses, particularly those that engage multiple family members, is that driving profits up is important but it is the means to an end and not an end in itself. The ultimate goal seems to be to create a better life for the owners, employees and their families. Now, you still have to work hard and make profits, but that's not the end goal.  Somewhere along the line, as they get bigger, these "mom and pop" firms lose their way and the means (profits) to the end (family health) becomes the end in itself and often the original end, suffers.  I wonder if there's a way to grow a firm but keep the attitude and focus on the health and vitality of the employees and the families it supports? Or perhaps the growth-driven economy doesn't allow that?

I love working for Black & Veatch.  Most of all I love working with the people in the firm. It does feel like an extended family in many ways. Whenever we hire someone new, or we do surveys of why it's a great place to work, the most common comment is that we have great people.  I'm not saying that as a means to inspire greater investment or growth of our stock prices. I'm saying that in and of itself.  Let's value people first, with an eye to keeping the money rolling in as a vibrant and financially profitable firm.  Do I need to make a legal disclaimer that these thoughts are my own and not those of my firm...? Or maybe the shift in business away from a trust-based model to a litigious one, could be my next topic for blogging! Did I mention I don't have an MBA? And yes, I know I'm naive!

Anyone else as naive as me?!

Reflections on WEFTEC 2016

I've just returned from the latest WEFTEC held in New Orleans. It was a great opportunity for me to catch up with colleagues and friends that I've gotten to know over the years. I went to my first WEFTEC in 1999 and except for one year when I was stuck on assignment in Singapore I'm pretty sure I've been to every WEFTEC since then. So I could blog about catching up with old friends, but instead I thought it would be interesting to reflect on what was NEW for me this year.  So here goes...

New Perspectives

Joe Whitworth

The opening session included an interesting presentation from Joe Whitworth of The Freshwater Trust.  It was refreshing to see someone who identifies themselves as an environmentalist wanting to work collaboratively rather than the typical obstructionist approach of well-meaning but naive and short-sighted groups. The majority of the people I work with want to protect and improve the environment and so you'd think that the likes of the Sierra Club and other environmental NGO's would be our advocates in improving treatment, but my own observation is that they're sue-happy and prevent real progress by pushing everything into the interminably slow US legal system.  When I was a kid doing environmental studies, the US EPA was held up as a great example of how to improve the environment through science-based investigation and laws.  Unfortunately the good things started in the 1980's now seem to be stagnating in the courts where the "process" is more important than actually doing what's best for the environment.

My good friend and visionary in the field of water, Dr Sudhir Murthy gave an excellent presentation on research and innovation for water utilities, though I have to say that Bernhard Wett stole the show in his introduction to Sudhir by describing him as "non-linear"! Not sure why that tickled me so much, but maybe it's because he's such an intriguing and interesting fella that's impossible to label him. Nice one Bernie!

Poop-talk radio!

New Experience

Thanks to WaterOnline.com I had the opportunity to do a radio interview on the topic of Big Data. I wasn't quite sure what to expect going in to the interview but it actually good fun. Big Data, the Internet of Things, Smart Utilities and the like, are hot topics that are gaining a lot of interest with a whole bunch of people. See my previous blog for more info...

New Responsibilities

This year I took over the reins of the Municipal Resource Recovery Design Committee (MRRDC).  This is one of the largest committees of WEF and is tasked with upholding the quality of design for treatment facilities. Amongst many things, we are heavy involved in the iconic "Manual of Practice #8" or MOP8, which is used extensively in North America for treatment plant design. We also help develop other technical documents and workshops for WEFTEC.  My predecessor, Dr Art Umble, did an excellent job as the previous chair, ending up being recognized as a Fellow of WEF.  I jokingly said that my main job was to not screw up the good things he started! Hopefully I'll do a little better than that.

New Technologies

No self-respecting poop engineer could blog without mentioning the cool technologies in the exhibit hall. Aerobic Granular Sludge (AGS) is causing quite a buzz in the poop-treatment industry and my firm is in the mix teaming with Royal HaskoningDHV, the developers of the Nereda process. Another technology causing a stir is GE's new "Zeelung" membrane aerated biofilm reactor (MABR) process which, on paper, can give up to 100% oxygen transfer efficiency (OTE) and in practice is giving at least 50%.  This is a game-changing technology in my book as it at least doubles the energy efficiency. Watch this space for more (or check out the video below)!

New Cullinary Delights!

Oh boy, if you're ever in New Orleans, you must try the Trinity restaurant. In a city known for good food, this has to be one that's near the top of the pile.  (Which reminds me, I must go to TripAdvisor and leave a review - 6 stars out of 5, I'm thinking). We had a dinner with a client group on Monday evening at which I had a crab-filled beignet for starter, a fabulous pork shank for main and possibly the best desert I've ever had to finish (some kind of macadamia nut desert with a subtle caramel sauce and mango - I can't even begin to describe it adequately!). On Wednesday night I was invited out by the good folks at S::CAN for a dinner and to my delight, they picked the same restaurant!  This time I was able to try their lamb chops which I swear had chocolate in the sauce (some kind of molé perhaps?) - it was equally awesome!

So another WEFTEC is done.  I think it was one of the best for me personally and hopefully for the 20,000 others who attended.  Let's see if WEFTEC 2017 in Chicago can top it!  

Get M.A.D. to be smart!

In a couple of weeks I'm heading off to WEFTEC, the major annual North American conference for poop engineers like myself. This year I'll be participating in a breakfast meeting where we'll discuss my firm's offerings for smart analytics which sits under the umbrella of our Smart Integrated Infrastructure group.

For the breakfast meeting I persuaded my colleagues to take their lead from a recent book produced by Pernille Ingildsen and Gustaf Olsson, called "Smart Utilities: Complexity Made Simple". Their excellent book uses a very simple but useful mnemonic to help us get our minds our the most important aspects of instrumentation and control in the water industry.  Quite simply it is M.A.D.!

M is for Measure

Firstly, and perhaps most importantly we need to focus on the measurements.  What do we need to measure (and where)?  How do we measure it? And what confidence do we have in that measurement?  It might seem simple to say we need to be measuring the right parameters in the right place but in my experience this is the place where we've fallen down over the years, particularly when it comes to measurements in the extremely fouling environment of wastewater.  All too often people have taken standard environmental monitors for water, streams or rivers or - worse still - lab instruments, and plonked them into poop water hoping they'll work.  Unfortunately wastewater is not forgiving.  In the late 1980's and early 1990's my old boss Dr John Watts did some great work in looking at the need for good calibration and validation in order to trust your data.  Unfortunately he didn't publish much internationally, but here is one paper on the topic. More recently Oliver Grievson in his review of activated sludge instrumentation to celebrate 100 years of AS gave a nod to John's work in producing an online respirometer (and I'm still waiting for someone to produce something nearly as good - maybe the ASP-CON?)

A is for Analyze

The focus of a lot of the buzz right now is on "smart analytics" and the ability of software developed in the Internet age to handle "big data." That's all pretty cool.  I always joke about wastewater having a problem with "crap data" rather than big data, but assuming we can figure out the "M" of measure, then there are now plenty of sophisticated tools to help us manage and analyze our data. All the big guys in IT are getting into this space, including the now famous Watson at IBM and now Microsoft with their PowerBI (I need to find time to play with that sometime as it looks pretty cool).

D is for Decide

OK, we have lovely measurements, producing pretty graphical representations of our big data... now what?  This will be the fun part. Right now, most systems I've seen, leave the decide step to the operator or plant engineer.  They have the expert knowledge which, coupled with insights from the advanced data analytics, are a powerful combination to help optimize a plant.  The step beyond this is to add in automated control actions based on the input from the smart analytics.  This is sort of like the jump to an autonomous vehicle which makes many people nervous but ultimately will give us the best performance overall.

Further Reading

I heartily recommend Pernille and Gustaf's book for anyone considering smart analytics.  As an introduction to it, you might also read Pernille's blog, "Why are "Smart Water Utilities" not already here?"

So, if you want to be smart... get MAD!

Model-ing Citizens

At the end of this month I'll be heading off to Europe for the latest wastewater process modeling conference - wwtmod2016 - which I'm sure will be as interesting as previous seminars in the series.  It's a pretty cool gathering of process engineers and/or modelers to dig into the process models that are a big part of what I do as a poop engineer.

In thinking about the seminar, I started to reflect on the fact that there are a lot of good wastewater process simulators on the market right now, 4 of which I'll talk about in this blog.  Before I get to them though, I have to comment on the fact that there are so many simulators in what is a pretty niche market.  If I think back to spreadsheets in the 1980s' there were several on the market - Lotus 123, Excel and Quattro Pro (the last being my fave for some time), but today the market is dominated by Excel alone (OK, there are a few diehards using OpenOffice, Numbers on the Mac or Google Sheets for simple collaborative tasks).  So how come there are so many good wastewater process simulators on the market right now? I'd like to suggest a few possible reasons:

1. Wastewater treatment is complex (and fascinating!) and so we need models to help us figure it out.
2. Wastewater modeling has strong "champions" who have really driven the ideas and industry in a positive direction.  In another blog I might list out several of these individuals, but from my own personal experience and because he's turning 60 soon I'll just mention Imre Takacs here.  Super nice fella with a real passion for modeling, including his latest venture with SUMO.
3. Wastewater modeling is cool.  Let's face it, producing diurnal graphs and "playing" with a virtual plant is way cool.  That's why I like it, right?

Simulators

OK, so here is a list of my personal favorite simulators.  They each have their particular strengths but I resist the temptation to say which is "best" despite being asked many times.  They're all good and useful tools.  Some do some things better than others and it's always a moving target as they each add new features. There are other simulators on the market too, but my exposure to them is limited, so the fact that I've not listed them is in no way a slight on their capabilities.  So here are, in order of my exposure to them and not in order of preference, my fave four!

GPS-X

For me, it all started with GPS-X 2.0 running on Unix on a HP computer.  That's real modeling!  They and everyone else shifted over to Windows which was more convenient, for sure, but our models slowed considerably until recent years as a result.  I still wonder if GPS-X running on modern machine with Linux wouldn't be the way to go!

The big plus for GPS-X is the user interface. Maybe it's because I started with this simulator, but I still love the ability to use sliders for control, set up graphs and have scenarios all in one interface.  It has it's quirks you need to learn like all the simulators, but in terms of being easy to run and adjust models, it's great.

BioWin

So, I find GPS-X one of the easiest simulators to run, but BioWin has deservedly won the reputation amongst design engineers for being the easiest to set up.  There are many engineer-friendly features in the model that make it the goto for many process engineers.  Another strength of the model is the biokinetic model (ASDM), which has been termed a "super model", because it carries all variables and all rate equations around all process units which makes it easier to ensure the mass balance holds.  They also pride themselves on having decent defaults for most parameters under most conditions. You should never use any model "out of the box" without knowing what you're doing but with BioWin you maybe don't have to move too far out of the box!

SUMO 

Here's the new kid on the block.  Having done his time with Hydromantis (producers of GPS-X) and Envirosim (BioWin folks), Imre struck out on his own to develop a whole new modeling platform from the bottom up.  It's pretty exciting to see the development.  What I've seen so far there are two main tenets guiding it's development: (1) modern user interface (pretty cool); and (2) ease of access to the biokinetic models.  The latter is the most exciting piece for me as it's allowing us to do a lot of investigations for WERF projects and other applications.  For those at WWTmod2016, you'll get to see some of this when my colleague Patrick Dunlap presents some initial modeling for one of those projects.

SIMBA#

Lastly but only in my own chronology as it's been around for years in Europe, is SIMBA#.  This simulator is very strong for anyone wanting to look at control.  It also has some nice energy features which they're continuing to develop and refine.  I'm thinking this "old kid on the block" may start to get some traction in North America in the next year or so.

So... what's your favorite wastewater process simulator (assuming you have one), and why?

(Copyright disclaimer... all graphics on this blog were taken from the software supplier's websites.  Please check out their sites for the original graphics and further info)

Bad data versus big data (or big bad data!!)

There's currently quite a buzz about "big data" and how water utilities might dig into all the data they collect in order to be "smarter." Several of my colleagues are investigating ways to do this under the banner of Smart Integrated Infrastructure (SII) and Smart Water Analytics. Pretty cool stuff. In a couple of conversations on the topic I half-jokingly said that wastewater doesn't have big data, it has crap data!  To avoid misunderstanding, I should clarify that by "crap" I'm referring to it being bad data and not just data describing the fecal material we treat!

Over the years I've been involved with various projects and discussions on generating and handling data in wastewater treatment. A few years ago I was involved in a couple of WERF Projects focused on developing Decision Support Systems (DSS) to prevent plant upsets, along with Dr Nancy Love and Advanced Data Mining (ADMi). The folks at ADMi did some nice data analytics to pick out anomalies that might indicate toxins in the plant influent, but one of the major hurdles we ran into was distinguishing anomalies due to toxins and anomalies due to measurement problems. This reminded me of what my ex-boss and mentor, Dr John Watts, used to drill into me which is you need to focus on good primary measurements in order to have confidence in your data. Wastewater is a tough place to try to do that! As I said, a lot of our data is bad.

So, here is my brain dump on some of the keys to making big data work in wastewater, and avoiding the pitfalls of bad big data (there's a tongue-twister there somewhere...)!

5 keys to making big data work

1. Focus on data quality rather than quantity

Starting from Dr Watt's sage advice to me years ago, and written up in one of his rare papers here, no amount of fancy analytics can overcome measurement errors, whether that's noise, drift or interferences.  You need to have confident in your primary sensors and analyzers otherwise your big data analytics will be crunching numbers that are meaningless and therefore any results you'll get will be useless.  Crap data = crap analytics!

In order to gain confidence in your data, you need to do 3 things with your sensors/analyzers:

1. Clean them - wastewater is an extremely fouling environment an not the best place to put scientific equipment.  My experience has been that everyone underestimates how quickly sensors become fouled.  Go for auto-cleaning whenever possible and avoid installing anything in raw sewage or primary effluent unless you really need the measurement (see Key #2!) as these areas are particularly prone to fouling. Mixed liquor is actually an easier place to take measurements and final effluent the easiest of all!
2. Calibrate them - this is generally understood, though the frequency of calibration, particularly for sensors that tend to drift, is generally shorter than ideal.
3. Validate them - this is the piece that's overlooked by most instrumentation suppliers, I think. Analytics to validate the measurements, particularly during calibration is an area that needs much more attention.

Much of the work that Dr Watts did at Minworth Systems was focused on automating these 3 things and I've seen very few instruments come close to what he did 20 years ago!

2. Measure what matters most

I could probably make this blog an ode to John Watts and fill it with his anecdotes.  One of my favorites was one where a customer asked him to install a dissolved oxygen (DO) probe in an anoxic zone. He suggested it would be cheaper to install a wooden probe and write 0 mg/L on a fake display!  Maybe that's a little harsh, but the point is that we should only measure things that are useful to help us to run the plant and that we're actually going to use to make some decision. Generally we're lacking many important and basic measurements in our treatment plants (e.g. dissolved oxygen in the aerated basins, airflow to each aeration zone and electricity use by blowers), but we need to be careful in our enthusiasm not to swing to the other extreme and start measuring stuff that's interesting but not useful. You can spend some serious money measuring ammonia and nitrate all over a treatment plant, but unless you're actually using it for control, the measurements will eventually be ignored and the instrument neglected.  It's much better to have a handful of good instruments, positioned in locations where you're actually measuring something you can control, then there's motivation to keep those sensors running well (see Key#1!)

3. Think dynamics, not steady state

A lot of the design and operational guidance in text books and training materials have simple equations into which you plug a single number to get your answer (e.g. sludge age calculation or removal efficiency). Similarly, influent and effluent samples are usually flow-weighted or time-averaged composites (worse-still, grab samples!).  All this means that we're used to thinking and talking about average daily conditions.

Graphic showing difference between composite
 sample and continuous measurement
(Courtesy Dr. Leiv Rieger/WEF,
taken from WEF Modeling 101 Webcast)

However, the reality is that our treatment plants see significant daily variations in flows and concentrations and therefore we need to look at them as a dynamic system. This was first brought home to me when I was working on a plant in the UK doing biological phosphorus removal back in the late 1990's. We had an online phosphate analyzer taking measurements at the end of the aeration basin just prior to the clarifiers and we would see daily phosphate peaks of 1 or 2 mg/L every afternoon for just an hour or so, but the effluent composite sample measurements would be pretty consistently below 0.2 mg/L. To understand our wastewater treatment systems we need to measure their dynamics and then analyze that good data (having adhered to Keys #1 and #2, of course!!)  

4. Recognize different timescales

Hand-in-hand with dynamics is the need to think about different timescales:

• Diurnal (daily) variations
• Weekly trends (especially weekend versus weekday differences)
• Seasonal shifts

For each of these, the data analytics needs are quite different and need to be thought through properly. For diurnal variations, it's useful to compare one day to the next by maybe overlaying the dynamic data; for weekly trends we can do something similar over a 7-day horizon; for seasonal shifts we need to plot out long-term trends and compare them to temperature and maybe rainfall shifts.

5. Consider how to handle outliers and extraordinary events

This blog is getting long, so I'll try to wrap up this 5th key quickly!  In data analytics it's common practice to identify and eliminate outliers, assuming they're either "bad" measurements or not typical and therefore we can ignore them.  However, thinking back to my involvement in the WERF projects on DSS, a lot of what is done at wastewater treatment plants is trying to keep the process stable in response to abnormal events such as upsets from shock loads or toxins, or more typically responding to wet weather.  This means we need to identify these "outliers" but rather than throw them away, we need to decide how to respond. Maybe this is a topic for another blog?!!