Understanding optimal process conditions is an essential step in providing high-quality fuel for energy production, efficient energy generation, and plant development. Thus, the effect of process conditions such as the temperature, time, nitrogen-to-solid ratio (NSR), and liquid-to-solid ratio (LSR) on pretreated waste pine sawdust (PSD) via torrefaction and solvolysis is presented. The desirability function approach and genetic algorithm (GA) were used to optimize the processes. The response surface methodology (RSM) based on Box–Behnken design (BBD) was used to determine the effect of the process conditions mentioned above on the higher heating value (HHV), mass yield (MY), and energy enhancement factor (EEF) of biochar/hydrochar obtained from waste PSD. Seventeen experiments were designed each for torrefaction and solvolysis processes. The benchmarked process conditions were as follows: temperature, 200–300 °C; time, 30–120 min; NSR/LSR, 4–5. In this study, the operating temperature was the most influential variable that affected the pretreated fuel’s properties, with the NSR and LSR having the least effect. The oxygen-to-carbon content ratio and the HHV of the pretreated fuel sample were compared between the two pretreatment methods investigated. Solvolysis pretreatment showed a higher reduction in the oxygen-to-carbon content ratio of 47%, while 44% reduction was accounted for the torrefaction process. A higher mass loss and energy content were also obtained from solvolysis than the torrefaction process. From the optimization process results, the accuracy of the optimal process conditions was higher for GA (299 °C, 30.07 min, and 4.12 NSR for torrefaction and 295.10 °C, 50.85 min, and 4.55 LSR for solvolysis) than that of the desirability function based on RSM. The models developed were reliable for evaluating the operating process conditions of the methods studied.

The reaction kinetics of solid fuel is a critical aspect of energy production because its energy component is determined during the process. The overall fuel quality is also evaluated to account for a defined energy need. In this study, a two-step first-order reaction mechanism was used to model the rapid mass loss of pine sawdust (PSD) during torrefaction using a thermogravimetric analyzer (Q600 SDT). The kinetic analysis was carried in a MATLAB environment using MATLAB R2020b software. Five temperature regimes including 220, 240, 260, 280, and 300 °C and a retention time of 2 h were used to study the mechanism of the solid fuel reaction. Similarly, a combined demarcation time (i.e., estimating the time that demarcates the first stage and the second stage) and iteration technique was used to determine the actual kinetic parameters describing the fuel’s mass loss during the torrefaction process. The fuel’s kinetic parameters were estimated, while the developed kinetic model for the process was validated using the experimental data. The solid and gas distributions of the components in the reaction mechanism were also reported. The first stage of the degradation process was characterized by the rapid mass loss evident at the start of the torrefaction process. In contrast, the second stage was characterized by the slower mass loss phase, which follows the first stage. The activation energies for the first and second stages were 10.29 and 141.28 kJ/mol, respectively, to form the solids. The developed model was reliable in predicting the mass loss of the PSD. The biochar produced from the torrefaction process contained high amounts of the intermediate product that may benefit energy production. However, the final biochar formed at the end of the process increased with the increase in torrefaction severity (i.e., increase in temperature and time).

In this study, cellulose nanocrystals (CNCs) were obtained from South African corncobs using an acid hydrolysis process. The delignification of corncobs was carried out by using alkali and bleaching pretreatment. Furthermore, the Box-Behnken Design (BBD) was used as a design of experiment (DOE) for statistical experimentations that will result in logical data to develop a model that explains the effect of variables on the response (CNCs yield). The effects (main and interactive) of the treatment variables (time, temperature, and acid concentration) were investigated via the response methodology approach and the obtained model was used in optimizing the CNCs yield. Surface morphology, surface chemistry, and the crystallinity of the synthesized CNC were checked using scanning electron microscopy (SEM), a Fourier Transform Infra-red spectroscopy (FTIR), and an X-ray diffraction (XRD) analysis, respectively. The SEM image of the raw corncobs revealed a smooth and compact surface morphology. Results also revealed that CNCs have higher crystallinity (79.11%) than South African waste corncobs (57.67%). An optimum yield of 80.53% CNCs was obtained at a temperature of 30.18 °C, 30.13 min reaction time, and 46 wt% sulfuric acid concentration. These optimized conditions have been validated to confirm the precision. Hence, the synthesized CNCs may be suitable as filler in membranes for different applications.

INTRODUCTION: Despite several studies carried out on the effects of the fuel properties of raw biomass on the final fuel properties of the biofuel after a thermochemical conversion, an identification and grading of various biomass types with respect to the level of their viability for pyro-gasification has not been established. OBJECTIVES: The primary objective of this study was to identify and rank eight waste wood feedstocks based on the suitability of their fuel properties for an efficient pyro-gasification using experimental data. METHODS: The wood samples were characterized using standard experimental procedures to determine their fuel properties. Five fuel evaluators relevant to the efficiency of a pyrogasification process, were developed. The experimental data collated for each sample was used to carry out an evaluation exercise of the samples under each of the five fuel evaluators. Finally, the result of this exercise was used to rank the wood samples based on their suitability as feedstock for pyro-gasification. RESULTS: The hardwoods such as the Eucalyptus and African mesquite exhibited high fuel ratios, heating value and energy density which was as result of their higher lignin content. However, they exhibited minimal char reactivity. Conversely due to its higher holocellulose-to-lignin ratio, the Bugweed exhibited high char reactivity but lower fuel ratio, heating value and energy density. In comparison to the literature, the experimental results in this study were somewhat consistent with those of other biomass samples previously reported. The Fuel characterization exercise reveals that no wood sample can be considered completely efficient for pyro-gasification. The Jacaranda was however ranked lowest across the board. CONCLUSION: The variations in the hierarchy of the samples under the different fuel evaluators due to the disparities in their fuel properties paves way for further studies on the blending of waste wood samples with contrasting fuel properties in different mix ratios. This would enable the production of feedstock with the right balance in fuel properties suitable for an efficient pyro-gasification process. This study provides stakeholders with a framework for blending different lignocellulosic biomass species for thermochemical conversion.

No abstract