Tramps in your toilet? – waste and consumption
'Waste avoidance', properly applied, will in almost any circumstances yield significant savings on energy and other consumables with little or no expenditure. Vilnis Vesma, senior consultant at energy specialist NIFES Consulting Group, introduces an unintrusive way of addressing the wasteful over-consumption of resources.
‘Energy saving’ still tends to imply the classical three-to-five-year-payback
capital project, with its attendant disruption, technical specialism, and perceived
risk. But managements today are less disposed than ever to contemplate these
‘hard’ solutions. So, this article introduces a desktop information-management
technique which, if properly applied, will in almost any circumstances yield
significant savings on energy and other consumables with little or no expenditure.
Such factors would include vehicle mileages, hours of darkness, production
throughputs, attendance figures, and how cold (or hot) the weather was. I am
happy to concede that it is sometimes difficult to pin down what factor affects
what, and to determine quantitatively what the link is between the two; but
the investment and effort necessary to establish these basic relationships will
be amply rewarded.
To take an obvious example: if we know the achievable mpg figure for a car,
then from the miles driven in any given month we can estimate what would be
a reasonable quantity of fuel to have used. Likewise, if we had taken the trouble
to establish how our electricity consumption varies with available daylight,
we could (having measured the hours of darkness over the month) estimate a reasonable
ration for electricity.
And so on, for every stream of consumption for which we have previously been
able to establish (a) an appropriate driving factor and (b) the formula by which
the two are related. Some anecdotes may give you a better feel of where this
is all leading.
The energy manager of Taunton Deane Borough Council noticed that patterns of
water and electricity consumption were spontaneously changing in the borough’s
public lavatories. On investigation he found one case of a technical fault in
the urinal flush controller; the remainder were caused by tramps dossing down
at night and triggering the automatic flushing systems.
Repeatedly aroused from their slumbers by Niagara Falls going off, they were
jamming matchsticks into the pushbuttons of the hand dryers in an effort to
keep warm. And there was one other exceptional circumstance: one of the lavatory
attendants, who was nesting in a service duct, had wired in a fan heater to
keep himself warm.
Another example is from a sewage works that ended up with the cleanest sewage
screens in the world. Raw sewage passed though a coarse screen between two open
chambers. Periodically, as the screen became fouled, the rise in upstream chamber
level triggered an electrically-powered rake, which scraped off the debris and
then (like a car’s windscreen wipers) parked at the end of its return stroke.
A loose bolt on the limit switch caused this parking action to fail, and the
5kW rake motor, plus about 8kW of associated pump and macerator motors, began
to run continuously instead of only occasionally.
Finally, one data processing centre in Swindon lost £9,000 over an eighteen-month
period because its heating and air conditioning systems had become locked in
contention through a minor control fault.
Nobody is immune from systems being incorrectly used or maintained, or leaking,
or going out of adjustment, or running when they are not needed. I have seen
a brand-new building exhibiting symptoms in at least three of these categories,
so what hope for a building which has had five, ten, or 20 years to develop
Again, it is possible to estimate the ‘correct’ ration of energy, water, etc,
if one has a measurable driving factor with a known relationship to consumption.
One classically difficult commodity to manage is gas, since its monthly consumption
is usually so very variable. How would you estimate an appropriate allocation
of gas at the end of each month? Paradoxically, it turns out that the demand
for space-heating fuel is relatively easy to gauge independently, through the
medium of degree-day data.
Published on the internet (www.DegreeDaysDirect.com) degree days provide an
index of how cold each month was in each region of the UK. Thus, in a month
which registers 300 degree days in a particular region, a given building will
have needed twice as much heat as in a month registering 150 degree days; three
times as much as in month registering 100 degree days; and so on.
Monthly fuel requirements for space heating are proportional to the monthly
degree-day value. The only slight complication is that there may also be a fixed
element of fuel consumption attributable to domestic hot water, catering, or
other non-weather-related uses. But this is not an insurmountable difficulty,
since we can say that the building will use so much fuel per month, plus so
much per degree-day.
This we can call its caracteristic pattern of consumption. Knowing the regional
degree-day figure for the month we can use this characteristic pattern to estimate
the fuel consumption target.
For example: suppose a building’s history of fuel consumption has been critically
examined and that its minimum achievable consumption characteristic has been
assessed as 3,200kWh per month (fixed) plus 45kWh per degree-day (the weather-dependent
component). Then suppose that in a particular month when the prevailing weather
yielded a resultant 275 degree days, the building used 17,700kWh. Is this good,
bad or indifferent?
Answer: the expected consumption for the month can be computed as 3,200 + (45
x 275) = 15,575kWh. As this is only 2,125kWh less than was actually used, we
might conclude that it is not too far adrift. We would repeat the assessment
each month, and would expect the deviation to be random; sometimes more than
expected, sometimes less. Sometimes a larger deviation, sometimes smaller.
The point is that we can make an objective assessment of the cost of each month’s
deviation from expected performance, and if it breaches a certain threshold
we would be prompted to take action. The same thinking can be applied to many,
if not most, other consumable commodities, since they are purchased for a purpose
and that purpose may be quantifiable (mileage, hours of darkness, etc). Figure
1 summarises the general principle.
Plotting week by week or month by month the quantity of resource consumed (a)
against the appropriate driving factor (b), which represents the work done,
one would expect to see the points exhibit a rational pattern (c) such that
more resource is consumed to do more work. An idealised straight line (d) can
be superimposed on the scattered points to represent an activity-related target.
High consumption (e) can then be quantified by reference to the ‘expected’ consumption.
There is a free government publication covering these principles, Fuel efficiency
booklet 13: waste avoidance methods, which goes on to describe a graphical method
called ‘cusum charting’ which enables its users to tease out information about
subtle, low-level adverse shifts in performance, working out when performance
changed for the worse, how much it is costing in the long run, and even sometimes
what the physical nature of the fault is. It can also be used to verify, objectively,
the effect of energy-saving measures.
The technique is relatively simple (within the reach of a competent advanced
spreadsheet user) but remarkably powerful. Most importantly, it is universally
applicable to all forms of consumable resource, not just energy and water; think
particularly about environmentally damaging ones like volatile organic compounds.
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