ArticleEnvironmental science and pollution research international2020
Predicting the higher heating value of syngas pyrolyzed from sewage sludge using an artificial neural network.
Article in Environmental science and pollution research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Sludge pyrolysis is a complex process including complicated reaction chemistry, phase transition, and transportation phenomena. To better evaluate the use of syngas, the monitoring and prediction of a higher heating value (HHV) is necessary. This study developed an artificial neural network (ANN) model to predict the HHV of syngas, with the process variables (i.e., sludge type, catalyst type, catalyst amount, pyrolysis temperature, and moisture content) as the inputs. In the first step, through optimizing various sets of parameters, a three-layer network including 8 input neurons, 15 hidden neurons, and 1 output neuron was established. Then, in the second step, an ANN model has been successfully used to predict the HHV of syngas, with a fitting correlation coefficient of 0.97 and a root mean square error (MSE) value of 14.62. The relative influence of input variables showed that the pyrolysis temperature and moisture content were the determining factors that affected the HHV of syngas. The results of optimization experiments showed that when temperature was 895 °C and the moisture content was 45.63 wt%, the highest HHV can be obtained as 438.22 kcal/m
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