Application of Artificial Neural Networks for Identification of Catalysts Used in Thermogravimetry Lignocellulosic Biomass

• Published in: Chemical engineering transactions Vol. 65
• Main Authors: F.Z. Monteiro, I.C. Valim, R. De Siqueira, F.J. Moura, A. Grillo, B. Santos
• Format: Journal Article
• Language: English
• Published: AIDIC Servizi S.r.l 01-06-2018
• ISSN: 2283-9216
• DOI: 10.3303/CET1865089

In recent decades there has been a growing need to develop efficient processes in economic and energetic terms for the sustainable production of fuels and chemical products. In this context, it is important to understand the thermal degradation behaviour of different biomass in an inert atmosphere to investigate the breakdown of polymer chains into smaller chains, which could be converted into new products. Catalysts can be used in these breaking processes, since they generate good effects in accelerating the degradation of organic structures and with this increases the yield of bio-oil production. In this work the fiber of crushed and sifted green coconut shell, submitted to the thermogravimetry analysis (TG), was used as biomass. Types of a catalyst were incorporated into the biomass, based on cobalt ferrite, Fe2CoO4. The analysis revealed distinct profiles of mass loss of the green coconut fiber with and without the catalysts. To detect possible instrumentation and/or preparation failures of the experimental samples, models were developed with the help of artificial intelligence. The strategy chosen was the use of artificial neural networks (ANN) feedforward because it is a set of mathematical procedures that seek an intelligent way (inspired in by the network of biological neurons) to manage and analyze systems. The RNA model was developed from the import of the TG data to the MATLAB R2017a software, identifying data inputs/outputs. The inputs were loss of mass (mg), derived from the loss of mass and temperature (°C). The training algorithms were: Levenberg-Marquardt (trainlm) and Levenberg-Marquardt with Bayesian regularization (trainbr) that avoids over-adjustment. The activation functions used were the hyperbolic tangent (tansig) and logistic (logsig) to activate the intermediate layers of the model. The results of the model involving mass loss information were satisfactory to identify the type of catalyst with a topology of 3-6-4-1. The evaluation of the SSE error rates of 0.882 and the MSE of 6.85E-04, confirmed the good fit of the neural network, since the values are close to zero. Thus, the model can be used in conjunction with TG analysis, preventing possible measurement failures.