초록
<P><B>Abstract</B></P> <P>Biochemical methane potential (BMP) test is a key analytical technique to assess the implementation and optimisation of anaerobic biotechnologies. However, this technique is characterised by long testing times (from 20 to >100days), which is not suitable for waste utilities, consulting companies or plants operators whose decision-making processes cannot be held for such a long time. This study develops a statistically robust mathematical strategy using sensitivity functions for early prediction of BMP first-order model parameters, i.e. methane yield (B<SUB>0</SUB>) and kinetic constant rate (<I>k</I>). The minimum testing time for early parameter estimation showed a potential correlation with the <I>k</I> value, where (i) slowly biodegradable substrates (k≤0.1d<SUP>−1</SUP>) have a minimum testing times of ≥15days, (ii) moderately biodegradable substrates (0.1<k<0.2d<SUP>−1</SUP>) have a minimum testing times between 8 and 15 days, and (iii) rapidly biodegradable substrates (k≥0.2d<SUP>−1</SUP>) have testing times lower than 7days.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Early parameters prediction of BMP model is achieved by using sensitivity functions. </LI> <LI> Minimum testing time for early parameter estimation is related to kinetic constant. </LI> <LI> Balanced regression improves the accuracy of estimated parameters. </LI> </UL> </P>