INSERCIONES Y DESCRIPCIÓN.

Extension of the theoretical perspective to modelling oxygen penetration into a pile can be achieved by a two phase analysis in which the gas transport system (the pores) are

considered a separate phase from the particles for analysis purposes, yet both phases are clearly inter-related. The parameters that link the gas transport phase with the particle phase are:

 Oxygen concentration in the pores surrounding a particle.

The micro-environment framework would apply equally as well to either phase, indeed Stępniewski‟s original derivation (Gliński & Stępniewski, 1985) was based on diffusion into a soil profile. The parameters used would have different values (Table 6-10), but the framework remains unchanged.

Table 6-10 – Changing parameters for applying micro-environment analysis to diffusion in the pile, as compared to its application to diffusion in a particle.

Parameter Particle Pile

Oxygen concentration Concentration in water Concentration in air

Diffusion coefficient Oxygen in water Oxygen in air

Energy density Density in substrate Density in pile

VOR Micro-environment _particle ∑(Micro-environments)*(1-FAS)

For application to a pile, the VOR of each particle size would need to be determined for each position down the pile, as pore oxygen levels would decrease with distance into the pile and this would reflect a correspondingly reduced oxygen penetration depth into the particle with distance into the pile. Micro-environments would exist in two intertwined phases (two dimensions):

 Pile: where at the interval time (ti) a new pile micro-environment would form;

based on the penetration of oxygen into the pile calculated with zero-order diffusion law solutions, using oxygen diffusion in air and a pile VOR which is an amalgam of particle VOR in lowered interstitial oxygen concentrations and FAS.

 Particle: where the oxygen concentration in the pores (determined as part of the pile dimension) is an input into the particle micro-environment calculations. There is a feed back loop between each micro-environment dimension, in that the VOR of the particle is dependent, in part, on the oxygen concentration in the surface of the particle (and hence pore oxygen concentration - from Henry‟s law), while the oxygen

concentration in the pore is dependent on the VOR of the particle.

Hence, the computational load increases greatly for a full determination of a pile performance, but the logic framework remains as above.

From the micro-environment perspective, broad trends could be predicted for the time course of oxygen penetration into a pile. These arise from three elements that can be identified from micro-environment analysis:

1) The differing composting time course of particles at different oxygen

concentrations. That is, different CO2 in the particle micro-environment thickness

calculation.

2) The offset in start time of aerobic composting for particles deeper in the pile (in micro-environment notation tm = m*ti).

3) The combined effect on the composting time course of a particle of both an increasing interstitial oxygen concentration (increasing composting rate), and decreasing VOR as substrate is oxidised (decreasing composting rate).

The time course for the 1cm cubical dog sausage particle at three oxygen levels can be seen in Figure 6-16.

Figure 6-16 – The modelled time course of energy production of dog sausage 1 cm cubical particle with three different interstitial oxygen concentrations (mg L-1).

Figure 6-16 does not include the offset in aerobic composting start time that would occur with particles deeper in the pile. Neither does it include the steadily increasing interstitial oxygen concentration that would occur in a particle at depth in a pile. In an actual pile, the lower interstitial oxygen concentrations would tend to occur deeper in a pile and hence would be more likely to be offset than those closer to the surface that have high interstitial oxygen concentrations.

It is argued here that, if these two effects were incorporated into the composting time course of the lowest oxygen concentration graph in Figure 6-16 then the time course would look like the actual data curve in Figure 6-17.

To get the data in Figure 6-17 a 400 mm long cylindrical reactor, held vertically, was fitted with an array of five evenly spaced sensors. This was used to observe the downwards progress of the oxygen front into a composting pile. Three litres of a mixture of 0.8 cm dog sausage and old compost (i.e. the particle size trial mixture), but with additional dry

0 1 2 3 4 5 6 0 2 3 5 6 8 9 11 12 14 15 17 18 20 21 23 24 26 27 29 Day W L -1 0.009 0.005 0.001

old compost and sheep droppings, was placed in the reactor. A controlled airflow across the top of the reactor kept surface oxygen levels high while minimising evaporative cooling.

As the reactor was held in a housing at constant air temperature, the compost temperature changed over time. The composting time course of each sensor was determined by calculating the difference between the sensor‟s temperature and the average of all 5 sensors. The time course of the lower 4 sensors can be seen in Figure 6-17 (the uppermost sensor was very close to the surface of the pile and had a composting time course very close to the upper middle sensor – it is not shown for clarity).

The oxygen concentration component of the composting time course (shown in Figure 6-16) is apparent in Figure 6-17. The remaining differences can be understood from the

combined effect of the delay in oxygen reaching compost deeper in the pile and the steadily rising interstitial oxygen concentration over time.

-14 -9 -4 1 6 11 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 Day Tem per a ture di ff e re nce f rom a v e ra ge 0 C

Upper Middle Middle Lower Middle Base

Figure 6-17 - Temperature difference from the average (0C), of 5 sensors evenly spaced down 400 mm of compost. The only source of oxygen was what could diffuse down from the upper surface.