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BackgroundAcid hydrolysis is a popular pretreatment for removing hemicellulose from lignocelluloses in order to produce a digestible substrate for enzymatic saccharification. In this work, a novel model for the dilute acid hydrolysis of hemicellulose within sugarcane bagasse is presented and calibrated against experimental oligomer profiles. The efficacy of mathematical models as hydrolysis yield predictors and as vehicles for investigating the mechanisms of acid hydrolysis is also examined.ResultsExperimental xylose, oligomer (degree of polymerisation 2 to 6) and furfural yield profiles were obtained for bagasse under dilute acid hydrolysis conditions at temperatures ranging from 110°C to 170°C. Population balance kinetics, diffusion and porosity evolution were incorporated into a mathematical model of the acid hydrolysis of sugarcane bagasse. This model was able to produce a good fit to experimental xylose yield data with only three unknown kinetic parameters ka,kb and kd. However, fitting this same model to an expanded data set of oligomeric and furfural yield profiles did not successfully reproduce the experimental results. It was found that a “hard-to-hydrolyse” parameter, α, was required in the model to ensure reproducibility of the experimental oligomer profiles at 110°C, 125°C and 140°C. The parameters obtained through the fitting exercises at lower temperatures were able to be used to predict the oligomer profiles at 155°C and 170°C with promising results.ConclusionsThe interpretation of kinetic parameters obtained by fitting a model to only a single set of data may be ambiguous. Although these parameters may correctly reproduce the data, they may not be indicative of the actual rate parameters, unless some care has been taken to ensure that the model describes the true mechanisms of acid hydrolysis. It is possible to challenge the robustness of the model by expanding the experimental data set and hence limiting the parameter space for the fitting parameters. The novel combination of “hard-to-hydrolyse” and population balance dynamics in the model presented here appears to stand up to such rigorous fitting constraints.

Sugarcane bagasse is an abundant and sustainable resource, generated as a by-product of sugarcane milling. The cellulosic material within bagasse can be broken down into glucose molecules and fermented to produce ethanol, making it a promising feedstock for biofuel production. Mild acid pretreatment hydrolyses the hemicellulosic component of biomass, thus allowing enzymes greater access to the cellulosic substrate during saccharification. A particle-scale mathematical model describing the mild acid pretreatment of sugarcane bagasse has been developed, using a volume averaged framework. Discrete population-balance equations are used to characterise the polymer degradation kinetics, and diffusive effects account for mass transport within the cell wall of the bagasse. As the fibrous material hydrolyses over time, variations in the porosity of the cell wall and the downstream effects on the reaction kinetics are accounted for using conservation of volume arguments. Non-dimensionalization of the model equations reduces the number of parameters in the system to a set of four dimensionless ratios that compare the timescales of different reaction and diffusion events. Theoretical yield curves are compared to macroscopic experimental observations from the literature and inferences are made as to constraints on these "unknown" parameters. These results enable connections to be made between experimental data and the underlying thermodynamics of acid pretreatment. Consequently, the results suggest that data-fitting techniques used to obtain kinetic parameters should be carefully applied, with prudent consideration given to the chemical and physiological processes being modeled.

Sugar (or more specifically sucrose) is one of the major food carbohydrate energy sources in the world. It is used as a sweetener, preservative, and colorant in baked and processed foods and beverages and is one of lowest cost energy sources for human metabolism. On an industrial scale, sucrose is produced from two major crops-sugarcane, grown in tropical and subtropical regions of the world, and sugar beet, grown in more temperate climates. Sugarcane, however, accounts for the vast majority of global sugar production. For much of the history of sugarcane production, sugar was a scarce and highly valued commodity. Sugarcane processing focused on extracting sucrose as efficiently as possible for the lucrative markets in the United Kingdom and Europe. The potential for the production of alternative products from sugarcane, however, has long been recognized. The key process by-products including bagasse, molasses, mud, and ash have all been investigated as a basis for the production of alternative products (Rao 1997, Taupier and Bugallo 2000). Sugarcane is believed to have originated in southern Asia, and migrated in several waves following trade routes through the Pacific to Oceania and Hawaii and through India into Europe. Sugarcane was introduced and spread through the Americas following the expansion by British, Spanish, and Portuguese colonies in the 15th and 16th centuries (Barnes 1964). While various methods of juice extraction and sugar production have been used over centuries to produce sugar, substantial innovations in sugar chemistry and processing technologies throughout the 18th and 19th centuries have formed the basis of modern sugar production methods (Bruhns et al. 1998). Dramatic improvements in processing efficiency, sugar quality, and automation and control characterized sugar processing throughout the 20th century.

Mathematical models are presented for the cultivation of seaweed. These relate to a mathematics-in-industry project to grow seaweed crops to consume by-products from commercial ethanol production. An initial model illustrates the process. Then, the potential is demonstrated with a more detailed feasibility study and a simple financial model. The growth of seaweed with time is described using various models utilising differential equations. These include factors such as solar radiation and the nitrogen content of the seaweed.