The prediction of potential energy and matter production from biomass pyrolysis with artificial neural network

Abstract

The potentiality determination of renewable energy resources is very important. The biomass is one of the alternative energy and material resources. There is great effort in their conversion to precious material but yet there is no generalized rule. Therefore, the prediction of the energy and material potentials of these resources has gained great importance. Also, the solution to environmental problems in real time can be found easily by predicting models. Here, the basic products of pyrolysis process, char, tar and gas were also predicted by artificial neural network modelling. The half of data obtained from real experimental process along with some content and proximate analysis were fed into artificial neural network modelling. After the training of the model with this data, the remaining half of the data were introduced into this artificial neural network model. And the model predicted the pyrolysis process products (char, tar and gaseous material). The predicted data and the real experimental data were compared. In addition, another aim of this study is to reduce the labour in identification and characterization of the pyrolysis products. For this purpose, a theoretical framework has also been sketched. The necessity of a generalized rule for generation of energy and matter production from biomass pyrolysis has been punctuated. As a result, the ANN modelling is found to be applicable in the prediction of pyrolysis process. Also, the extensive reduction in labour and saving in economy is possible.

Recent Modeling Approaches to Biomass Pyrolysis: A Review

“…Still, ANN may not be suitable for novel technologies like biomass pyrolysis or gasification where the amount of experimental data is limited. For the easy approximation of the pyrolysis or gasification data, a feed-forward network (FFN) and cascade-forward network (CFN) can be useful in predicting the relationship between multivariant, nonlinear biomass, and product yields. − …”

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

“…Such studies differ drastically in experimental systems ( Tithonia diversifolia weed biomass [ 63 ], low-density polyethylene [ 64 ], high-density polyethylene [ 68 ], safflower seed press cake [ 65 ], durian rinds [ 69 ], rape straw [ 70 ], coal gangue and peanut shell [ 71 ], pet coke [ 72 ], sewage sludge and peanut shell [ 73 ], sewage sludge and coffee grounds [ 74 ], vegetable fibers [ 75 ], rice husk and sewage sludge [ 45 ], sludge, watermelon rind, corncob, and eucalyptus [ 76 ], Sargassum sp. seaweed [ 77 ], cotton cocoon shell, tea waste, and olive husk [ 66 ], mechanoactivated coals [ 78 ], cattle manure [ 79 ], lignocellulosic forest residue and olive oil residue [ 80 ], cotton cocoon shell, fabricated tea waste, olive husk, and hazelnut shell [ 81 ]) and in some minor details, but the general concept remains the same. Thus, we illustrate it with the study by Kataki et al [ 63 ], who used a neural network model (4-14-1) to predict the product yield for the pyrolysis of dried weed biomass.…”

Modeling of Bio-Oil Production by Pyrolysis of Woody Biomass: Artificial Neural Network Approach

“…Table 1a and Table 1b present the literature contributions in this field. The use of wood waste [15,[23][24][25][26] and agricultural residues [16,17,27,28] as feedstock are common in the literature due to its low market value and availability [29,30]. The related variables with output can be used to reach more accurate results.…”

“…Merdun and Sezgin [24] reported that the models in more homogeneous characteristics (single or a few biomass types) and more related inputs with output may result in better performances. Aydinli et al [27] specified that in order to attain the more reliable models, the variables such as particle size and residence time as well as the ultimate analysis data can be considered as input parameters. The yields and composition of the products depend on the pyrolysis conditions, properties of biomass, and pyrolysis method.…”