Microbial enzymatic indices for predicting composting quality of recalcitrant lignocellulosic substrates.

Three different enzymes alkaline phosphatase, Urease and Dehydrogenase were measured during this study to monitor the organic matter dynamics during semi-industrial composting of mixture A with 1/3 sludge+2/3 palm waste and mixture B with ½ sludge+1/2 palm waste. The phosphatase activity was higher for Mix-A (398.7 µg PNP g h) than Mix-B (265.3 µg PNP g h), while Mix-B (103.3 µg TPF gd) exhibited greater dehydrogenase content than Mix-A (72.3 µg TPF g d). That could contribute to the dynamic change of microbial activity together with high amounts of carbonaceous substrates incorporated with the lignocellulosic. The gradual increase in the dehydrogenase from the compost Mix-A implies that high lignocellulosic substrate requires gradual buildup of dehydrogenase activity to turn the waste into mature compost. A higher pick of urease with a maximum activity of 151.5 and 122.4 µg NH-N g h were reported, respectively for Mix-A and B. Temperature and pH could also influence the expression of enzyme activity during composting. The machine learning well predicted the compost quality based on NH/NO, C/N ratio, decomposition rate and, humification index (HI). The root mean square error (RMSE) values were 1.98, 1.95, 4.61%, and 4.1 for NH /NO , C/N ratio, decomposition rate, and HI, respectively. The coefficient of determination between observed and predicted values were 0.87, 0.93, 0.89, and 0.94, for the r NH/NO, C/N ratio, decomposition rate, and HI. Urease activity significantly predicted the C/N ratio and HI only. The profile of enzymatic activity is tightly linked to the physico-chemical properties, proportion of lignocellulosic-composted substrates. Enzymatic activity assessment provides a simple and rapid measurement of the biological activity adding understunding of organic matter transformation during sludge-lignocellulosic composting.