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Predicting the Higher Heating Value of Syngas Pyrolyzed from Sewage Sludge Using an Artificial Neural Network by Hongsen Li; Qi Xu; Keke Xiao; Jiakuan Yang; Sha Liang; Jingping Hu; Huijie Hou; Bingchuan Liu is a Environmental Science article available to read on EtoBox.
What is Predicting the Higher Heating Value of Syngas Pyrolyzed from Sewage Sludge Using an Artificial Neural Network about?
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-N. Moreover, the ANN model sho
Who reads Predicting the Higher Heating Value of Syngas Pyrolyzed from Sewage Sludge Using an Artificial Neural Network?
It is typically read by researchers, students, and practitioners in Environmental Science.
- Author
- Hongsen Li; Qi Xu; Keke Xiao; Jiakuan Yang; Sha Liang; Jingping Hu; Huijie Hou; Bingchuan Liu
- Publisher
- Springer Science and Business Media LLC
- Published
- 2019
- Language
- EN
- Field
- Environmental Science (Physical Sciences)