A 12-year-old planted woodlands Searsia lancea tree, grown on acid mine drainage for phytoremediation of polluted groundwater on gold and uranium mines in South Africa, was used in this research. The research describes the fuel-related characteristics and the influence of different operating conditions on the hydrothermal carbonization of the biomass and the combustion profiles of discard coal/biomass hydrochar pellets. The raw biomass was treated at temperatures ranging from 200−280 °C and residence time of 30−90 min. The hydrochar produced at 280 °C and residence time of 90 min had the highest calorific value of 29.71 MJ/kg compared to 17.23 and 16.73 MJ/kg obtained from the raw biomass and discard coal, respectively. Regression equations developed using the central composite design (CCD) indicated that the values obtained experimentally agree with the predicted values from the models for mass yield, calorific value, and ash content. The reactivity tests showed that the 100% hydrochar pellet had the highest reactivity and lowest ignition and burnout temperature compared to biocoal pellets and discard coal. The process water contained relatively low concentrations of major elements, and the study had shown that different high-grade biocoal pellets can be produced from the S. lancea tree.

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.

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 by 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 5-10-10-1 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.

The utilization of biomass as a solid fuel for co-firing has received great attention from boiler manufacturers as a clean coal technology (CCT) option. This research aimed to produce biocoal pellets, as a clean energy fuel, using hydrochar from trees planted to rehabilitate acid mine drainage (AMD) water and fine coal discards. The hydrochar was synthesized by hydrothermal carbonization of Searsia lancea harvested from AMD-contaminated land at a temperature of 280°C and a residence time of 90 minutes. It was blended with discard coal (-1 mm) at ratios of 25% and 50% hydrochar to produce different forms of solid pelletized biocoal (BC). The physicochemical and mechanical properties of each of the biocoal pellet blends were determined. The 100% hydrochar had the highest calorific value of 29.99 MJ/kg, while the raw discard coal had a calorific value of 16.73 MJ/kg. The ash content decreased from 42% in the discard coal to 25% in the blend of 50% coal and 50% hydrochar biocoal pellets. Biocoal pellets comprising 25% hydrochar and 75% discard coal (BC25HC/75COAL) displayed the best mechanical properties (compressive strength 3.06 MPa) of all the fuels, but the physicochemical properties were inferior to the BC50HC/50 COAL pellets. This research has demonstrated that hydrochar synthesized from a tree species planted for hydraulic control of AMD has the capability to act as a binder for improving the mechanical properties and energy characteristics of fine discard coal.

Biomass resources are gaining attention to address environmental issues, ensure energy efficiency, and ensure longterm fuel sustainability. The use of biomass in its raw form is known to present a number of issues, including high shipping, storage, and handling costs. Hydrothermal carbonization (HTC), for example, can increase the physiochemical properties of biomass by converting it into a more carbonaceous solid hydrochar with enhanced physicochemical properties. This study investigated the optimum process conditions for the HTC of woody biomass (Searsia lancea). HTC was carried out at varying reaction temperatures (200−280 °C) and hold times (30−90 min). The response surface methodology (RSM) and genetic algorithm (GA) were used to optimize the process conditions. RSM proposed an optimum mass yield (MY) and calorific value (CV) of 56.5% and 25.8 MJ/kg at a 220 °C reaction temperature and 90 min of hold time. The GA proposed an MY and a CV of 47% and 26.7 MJ/kg, respectively, at 238 °C and 80 min. This study revealed a decrease in the hydrogen/carbon (28.6 and 35.1%) and oxygen/carbon (20 and 21.7%) ratios, indicating the coalification of the RSM-and GA-optimized hydrochars, respectively. By blending the optimized hydrochars with coal discard, the CV of the coal was increased by about 15.42 and 23.12% for RSM-and GA-optimized hydrochar blends, respectively, making them viable as an energy alternative.