Predicting the higher heating value of syngas pyrolyzed from sewage sludge using an artificial neural network

Li, Hongsen; Xu, Qi; Xiao, Keke; Yang, Jiakuan; Liang, Sha; Hu, Jingping; Hou, Huijie; Liu, Bingchuan

doi:10.1007/s11356-019-06885-2

Cited by 23 publications

(7 citation statements)

References 48 publications

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“…Furthermore, a hyperbolic tangent sigmoid was used as an activation function in the hidden layer and for this task in the output layer, linear functions were employed. The performance of these functions has been proved in other research works [39,42,44,45,[89][90][91][92][93].…”

Section: Training and Testing Of The Ann-based Modelmentioning

confidence: 94%

Modeling of Hydrogen Production by Applying Biomass Gasification: Artificial Neural Network Modeling Approach

et al. 2021

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In order to accurately anticipate the proficiency of downdraft biomass gasification linked with a water–gas shift unit to produce biohydrogen, a model based on an artificial neural network (ANN) approach is established to estimate the specific mass flow rate of the biohydrogen output of the plant based on different types of biomasses and diverse operating parameters. The factors considered as inputs to the models are elemental and proximate analysis compositions as well as the operating parameters. The model structure includes one layer for input, a hidden layer and output layer. One thousand eight hundred samples derived from the simulation of 50 various feedstocks in different operating situations were utilized to train the developed ANN model. The established ANN in the case of product biohydrogen presents satisfactory agreement with input data: absolute fraction of variance (R2) is more than 0.999 and root mean square error (RMSE) is lower than 0.25. In addition, the relative impact of biomass properties and operating parameters on output are studied. At the end, to have a comprehensive evaluation, variations of the inputs regarding hydrogen-content are compared and evaluated together. The results show that almost all of the inputs show a significant impact on the smhydrogen output. Significantly, gasifier temperature, SBR, moisture content and hydrogen have the highest impacts on the smhydrogen with contributions of 19.96, 17.18, 15.3 and 10.48%, respectively. In addition, other variables in feed properties, like C, O, S and N present a range of 1.28–8.6% and proximate components like VM, FC and A present a range of 3.14–7.67% of impact on smhydrogen.

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Section: Training and Testing Of The Ann-based Modelmentioning

confidence: 94%

Modeling of Hydrogen Production by Applying Biomass Gasification: Artificial Neural Network Modeling Approach

et al. 2021

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“…Based on the weight values in Table 4, the effects of the input variables on the outputs were determined using Equation (8) as follows 20,40 :…”

Section: The Relative Influence Of Input Variables On Ann Model Outputsmentioning

confidence: 99%

A novel study on activated carbon production based on artificial neural network model: An experimental and artificial intelligence method approach

Wang

Yang²,

Yang

et al. 2022

Intl J of Energy Research

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“…Each ANN has one input layer with 11 variables, M (wt%), VM (wt% dry basis), FC (wt% dry basis), ash (wt% dry basis), C (wt% dry basis), O (wt% dry basis), H (wt% dry basis), N (wt% dry basis), S (wt% dry basis), gasifier temperature, T (°C) and air to fuel ratio (kgair/kgdrybiomass) with one hidden layer and one output. Indeed, there no clear rule to determine totally optimal structure for ANN and most of researchers in this field have been developed ANN models only with one hidden layer [6,8,26,[63][64][65]. Hence, we considered also one hidden layer but with various number of nodes to find the optimal structure by minimizing the Root Mean Square Error (RMSE) due to its capability to compare different ANN structure [66].…”

Section: Ann Modeling Conceptmentioning

confidence: 99%

Artificial neural network integrated with thermodynamic equilibrium modeling of downdraft biomass gasification-power production plant

Safarian

Saryazdi

Unnþórsson

et al. 2020

Energy

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Predicting the higher heating value of syngas pyrolyzed from sewage sludge using an artificial neural network

Cited by 23 publications

References 48 publications

Modeling of Hydrogen Production by Applying Biomass Gasification: Artificial Neural Network Modeling Approach

Modeling of Hydrogen Production by Applying Biomass Gasification: Artificial Neural Network Modeling Approach

A novel study on activated carbon production based on artificial neural network model: An experimental and artificial intelligence method approach

Artificial neural network integrated with thermodynamic equilibrium modeling of downdraft biomass gasification-power production plant

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