Coal Pyrolysis Distribution

<p>Coal pyrolysis is a complex process involving a large number of chemical reactions. The most accurate and up to date approach to modeling coal pyrolysis is to adopt the Distributed Activation Energy Model (DAEM) in which the reactions are assumed to consist of a set of irreversible first-order reactions that have different activation energies and a constant frequency factor. The differences in the activation energies have usually been represented by a Gaussian distribution. This thesis firstly compares the Simple First Order Reaction Model (SFOR) with the Distributed Activation Energy Model (DAEM), to explore why the DAEM may be a more appropriate approach to modeling coal pyrolysis. The second part of the thesis uses the inverse problem approach together with the smoothing function (iterative method) to provide an improved estimate of the underlying distribution in the wide distribution case of the DAEM. The present method significantly minimizes the error due to differencing and smoothes the chopped off parts on the underlying distribution curve.</p>

Coal Pyrolysis Distribution

<p>Coal pyrolysis is a complex process involving a large number of chemical reactions. The most accurate and up to date approach to modeling coal pyrolysis is to adopt the Distributed Activation Energy Model (DAEM) in which the reactions are assumed to consist of a set of irreversible first-order reactions that have different activation energies and a constant frequency factor. The differences in the activation energies have usually been represented by a Gaussian distribution. This thesis firstly compares the Simple First Order Reaction Model (SFOR) with the Distributed Activation Energy Model (DAEM), to explore why the DAEM may be a more appropriate approach to modeling coal pyrolysis. The second part of the thesis uses the inverse problem approach together with the smoothing function (iterative method) to provide an improved estimate of the underlying distribution in the wide distribution case of the DAEM. The present method significantly minimizes the error due to differencing and smoothes the chopped off parts on the underlying distribution curve.</p>

The closure temperature(s) of zircon Raman dating

Zircon Raman dating based on irradiation damage is a debated concept but not an established geo-/thermochronological method. One issue is the temperature range of radiation-damage annealing over geological timescales. We conducted isochronal and isothermal annealing experiments on radiation-damaged zircons between 500 and 1000 ∘C for durations between 10 min and 5 d to describe the annealing kinetics. We measured the widths (Γ) and positions (ω) of the ν1(SiO4), ν2(SiO4), and ν3(SiO4) internal Raman bands, and the external rotation Raman band at ∼974, 438, 1008, and 356 cm−1 after each annealing step. We fitted a Johnson–Mehl–Avrami–Kolmogorov and a distributed activation energy model to the fractional annealing data, calculated from the widths of the ν2(SiO4), ν3(SiO4), and external rotation bands. From the kinetic models, we determined closure temperatures Tc for damage accumulation for each Raman band. Tc ranges from 330 to 370 ∘C for the internal ν2(SiO4) and ν3(SiO4) bands; the external rotation band is more sensitive to thermal annealing (Tc∼260 to 310 ∘C). Our estimates are in general agreement with previous ones, but more geological evidence is needed to validate the results. The Tc difference for the different Raman bands offers the prospect of a multi-closure-temperature zircon Raman thermochronometer.