Abstract
Globally, the provision of energy is becoming an absolute necessity. Biomass resources are abundant and have been described as a potential alternative source of energy. However, it is important to assess the fuel characteristics of the various available biomass sources. Soft computing techniques are presented in this study to predict the mass yield (MY), energy yield (EY), and higher heating value (HHV) of hydrothermally carbonized biomass using Gene Expression Programming (GEP), multiple-input single output-artificial neural network (MISO-ANN), and Multilinear regression (MLR). The three techniques were compared using statistical performance metrics. The coefficient of determination (R2), mean absolute error (MAE) and mean bias error (MBE) were used to evaluate the performance of the models. The MISO-ANN with 5-10 to 10-1 and 5-15-15-1 network architectures provided the most satisfactory performance of the three proposed models (R2 = 0.976, 0.955, 0.996; MAE = 2.24, 2.11, 0.93; MBE = 0.16, 0.37, 0.12) for MY, EY and HHV, respectively. The GEP technique’s ability to predict hydrochar properties based on the input parameters was found to be satisfactory, while MLR provided an unsatisfactory predictive model. Sensitivity analysis was conducted, and the analysis revealed that volatile matter (VM) and temperature (Temp) have more influence on the MY, EY, and HHV.
Highlights
The increasing energy demand has led to the need to find alternative energy sources that are affordable, widely available, and environmentally friendly
The points predicted with MISO-Artificial neural networks (ANN) fall largely within the 3% error line, while many of the points predicted by the Gene Expression Programming (GEP)
Mass yield, energy yield, and higher heating value are important hydrochar properties required for the analysis and design of any bioenergy systems
Summary
The increasing energy demand has led to the need to find alternative energy sources that are affordable, widely available, and environmentally friendly. Biomass is the most available renewable energy source, with a contribution. The study concluded that the emission reductions reported were due to an improved reaction between coal and biomass volatiles in a hot oxidizing atmosphere. A number of studies have been carried out on the pre-treatment of biomass by various researchers (Safarian et al 2019; Zhang and Pang 2019; Kambo and Dutta 2015). The type of feedstock and the preferred end product determines the type of pre-treatment method to be used (Kambo and Dutta 2015)
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