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Background The production of microalgal biofuels, despite their sustainable and renowned potential, is not yet cost-effective compared to current conventional fuel technologies. However, the biorefinery concept increases the prospects of microalgal biomass as an economically viable feedstock suitable for the co-production of multiple biofuels along with value-added chemicals. To integrate biofuels production within the framework of a microalgae biorefinery, it is not only necessary to exploit multi-product platforms, but also to identify optimal microalgal cultivation strategies maximising the microalgal metabolites from which biofuels are obtained: starch and lipids. Whilst nutrient limitation is widely known for increasing starch and lipid formation, this cultivation strategy can greatly reduce microalgal growth. This work presents an optimisation framework combining predictive modelling and experimental methodologies to effectively simulate and predict microalgal growth dynamics and identify optimal cultivation strategies. Results Microalgal cultivation strategies for maximised starch and lipid formation were successfully established by developing a multi-parametric kinetic model suitable for the prediction of mixotrophic microalgal growth dynamics co-limited by nitrogen and phosphorus. The model’s high predictive capacity was experimentally validated against various datasets obtained from laboratory-scale cultures of Chlamydomonas reinhardtii CCAP 11/32C subject to different initial nutrient regimes. The identified model-based optimal cultivation strategies were further validated experimentally and yielded significant increases in starch (+ 270%) and lipid (+ 74%) production against a non-optimised strategy. Conclusions The optimised microalgal cultivation scenarios for maximised starch and lipids, as identified by the kinetic model presented here, highlight the benefits of exploiting modelling frameworks as optimisation tools that facilitate the development and commercialisation of microalgae-to-fuel technologies.

In this work, we present the scale-up of a batch anaerobic fermentation system for the production of succinic acid from glycerol using A. succinogenes. The system has been successfully scaled up from an initial bioreactor working volume of 1 L (laboratory scale) to a working volume of 100 L (pilot scale). At the same time, we have developed a hybrid model, combining the intrinsic kinetics of the microbial growth, with a computational fluid dynamics model (CFD) of the bioreactor. The proposed model is able to predict the productivity drop, usually observed while scaling up a bioprocess. In our process, this is a result of the limitations on the mass transfer of CO2 between the gas and the liquid phase of the system. The model is successfully used to predict the amount of aeration needed in order to achieve increased succinic acid productivity. Using the model, the final succinic acid increased by 4.3%, and the succinic acid productivity increased by 8.5%, while the fermentation by-products decreased by approxiamtely 3% each.

Spatial control over features within multifunctional catalysts can unlock efficient one-pot cascade reactions, which are themselves a pathway to aviation biofuels via hydrodeoxygenation. A synthesis strategy that encompasses spatial orthogonality, i.e., one in which different catalytic species are deposited exclusively within discrete locations of a support architecture, is one solution that permits control over potential interactions between different sites and the cascade process. Here, we report a Pd doped hierarchical zeolite, in which Pd nanoparticles are selectively deposited within the mesopores, while acidity is retained solely within the micropores of ZSM-5. This spatial segregation facilitates hydrodeoxygenation while suppressing undesirable decarboxylation and decarbonation, yielding significant enhancements in activity (30.6 vs 3.6 mol dodecane mol Pd −1 h −1 ) and selectivity (C 12 :C 11 5.2 vs 1.9) relative to a conventionally prepared counterpart (via wet impregnation). Herein, multifunctional material design can realise efficient fatty acid hydrodeoxygenation, thus advancing the field and inspiring future developments in rationalised catalyst design. Hierarchical ZSM-5 boosts fatty acid hydrodeoxygenation by compartmentalization of catalytic sites. Separating acid sites within micropores and metal nanoparticles in mesopores provides control over the reaction and reduces unwanted side reactions.

In this study, we propose a set of nonlinear differential equations to model the dynamic growth of avascular stage tumors, considering nutrient supply from underlying tissue, innate immune response, contact inhibition of cell migration, and interactions with a chemotherapeutic agent. The model has been validated against available experimental data from the literature for tumor growth. We assume that the size of the modeled tumor is already detectable, and it represents all clinically observed existent cell populations; initial conditions are selected accordingly. Numerical results indicate that the tumor size and regression significantly depend on the strength of the host immune system. The effect of chemotherapy is investigated, not only within the malignancy, but also in terms of the responding immune cells and healthy tissue in the vicinity of a tumor.

Evolutionary, pattern forming partial differential equations (PDEs) are often derived as limiting descriptions of microscopic, kinetic theory-based models of molecular processes (e.g., reaction and diffusion). The PDE dynamic behavior can be probed through direct simulation (time integration) or, more systematically, through stability/bifurcation calculations; time-stepper-based approaches, like the Recursive Projection Method [Shroff, G. M. & Keller, H. B. (1993) SIAM J. Numer. Anal. 30, 1099-1120] provide an attractive framework for the latter. We demonstrate an adaptation of this approach that allows for a direct, effective (\"coarse\") bifurcation analysis of microscopic, kinetic-based models; this is illustrated through a comparative study of the FitzHugh-Nagumo PDE and of a corresponding Lattice-Boltzmann model.

Low salinity waterflooding has proven to accelerate oil production at core and field scales. Wettability alteration from a more oil-wetting to a more water-wetting condition has been established as one of the most notable effects of low salinity waterflooding. To induce the wettability alteration, low salinity water should be transported to come in contact with the oil-water interfaces. Transport under two-phase flow conditions can be highly influenced by fluids topology that creates connected pathways as well as dead-end regions. It is known that under two-phase flow conditions, the pore space filled by a fluid can be split into flowing (connected pathways) and stagnant (deadend) regions due to fluids topology. Transport in flowing regions is advection controlled and transport in stagnant regions is predominantly diffusion controlled. To understand the full picture of wettability alteration of a rock by injection of low salinity water, it is important to know i) how the injected low salinity water displaces and mixes with the high salinity water, ii) how continuous wettability alteration impacts the redistribution of two immiscible fluids and (ii) role of hydrodynamic transport and mixing between the low salinity water and the formation brine (high salinity water) in wettability alteration. To address these two issues, computational fluid dynamic simulations of coupled dynamic two-phase flow, hydrodynamic transport and wettability alteration in a 2D domain were carried out using the volume of fluid method. The numerical simulations show that when low salinity water was injected, the formation brine (high salinity water) was swept out from the flowing regions by advection. However, the formation brine residing in stagnant regions was diffused very slowly to the low salinity water. The presence of formation brine in stagnant regions created heterogeneous wettability conditions at the pore scale, which led to remarkable two-phase flow dynamics and internal redistribution of oil, which is referred to as the \"pull-push\" behaviour and has not been addressed before in the literature. Our simulation results imply that the presence of stagnant regions in the tertiary oil recovery impedes the potential of wettability alteration for additional oil recovery. Hence, it would be favorable to inject low salinity water from the beginning of waterflooding to avoid stagnant saturation. We also observed that oil ganglia size was reduced under tertiary mode of low salinity waterflooding compared to the high salinity waterflooding.

Background The intracellular environment is a complex and crowded medium where the diffusion of proteins, metabolites and other molecules can be decreased. One of the most popular methodologies for the simulation of diffusion in crowding systems is the Monte Carlo algorithm (MC) which tracks the movement of each particle. This can, however, be computationally expensive for a system comprising a large number of molecules. On the other hand, the Lattice Boltzmann Method (LBM) tracks the movement of collections of molecules, which represents significant savings in computational time. Nevertheless in the classical manifestation of such scheme the crowding conditions are neglected. Methods In this paper we use Scaled Particle Theory (SPT) to approximate the probability to find free space for the displacement of hard-disk molecules and in this way to incorporate the crowding effect to the LBM. This new methodology which couples SPT and LBM is validated using a kinetic Monte Carlo (kMC) algorithm, which is used here as our \"computational experiment\". Results The results indicate that LBM over-predicts the diffusion in 2D crowded systems, while the proposed coupled SPT-LBM predicts the same behaviour as the kinetic Monte Carlo (kMC) algorithm but with a significantly reduced computational effort. Despite the fact that small deviations between the two methods were observed, in part due to the mesoscopic and microscopic nature of each method, respectively, the agreement was satisfactory both from a qualitative and a quantitative point of view. Conclusions A crowding-adaptation to LBM has been developed using SPT, allowing fast simulations of diffusion-systems of different size hard-disk molecules in two-dimensional space. This methodology takes into account crowding conditions; not only the space fraction occupied by the crowder molecules but also the influence of the size of the crowder which can affect the displacement of molecules across the lattice system.

Uncertainty arises in many large-scale distributed industrial systems, needing efficient computational tools. Uncertainty propagation techniques have been developed and applied including power series expansions (PSE) and polynomial chaos expansions (PCE). However, such fast low-order approximate models generate errors and, in general, require prior knowledge about uncertainty distribution. In this work, the recursive projection method (RPM) was adopted to accelerate the computation of steady state solutions of complex large-scale dynamic systems. These accelerated models including uncertainty were subsequently utilised in an efficient Bayesian global optimisation framework. The performance of the proposed robust optimisation framework was demonstrated through an illustrative example: a tubular reactor where an exothermic reaction takes place.

Cupriavidus necator DSM 545 can utilise glycerol to synthesise poly(3-hydroxybutyric acid) under unbalanced growth conditions, i.e., nitrogen limitation. To improve poly(3-hydroxybutyric acid) (PHB) batch production by C. necator through model-guided bioprocessing or genetic engineering, insights into the dynamic effect of the fermentation conditions on cell metabolism are crucial. In this work, we have used dynamic flux balance analysis (DFBA), a constrained-based stoichiometric modelling approach, to study the metabolic change associated with PHB synthesis during batch cultivation. The model employs the ‘minimisation of all fluxes’ as cellular objectives and measured extracellular fluxes as additional constraints. The mass balance constraints are further adjusted based on thermodynamic considerations. The resultant flux distribution profiles characterise the evolution of metabolic states due to adaptation to dynamic extracellular conditions and provide further insights towards improvements that can be implemented to enhance PHB productivity.

Background Flux balance analysis is traditionally implemented to identify the maximum theoretical flux for some specified reaction and a single distribution of flux values for all the reactions present which achieve this maximum value. However it is well known that the uncertainty in reaction networks due to branches, cycles and experimental errors results in a large number of combinations of internal reaction fluxes which can achieve the same optimal flux value. Results In this work, we have modified the applied linear objective of flux balance analysis to include a poling penalty function, which pushes each new set of reaction fluxes away from previous solutions generated. Repeated poling-based flux balance analysis generates a sample of different solutions (a characteristic set), which represents all the possible functionality of the reaction network. Compared to existing sampling methods, for the purpose of generating a relatively “small” characteristic set, our new method is shown to obtain a higher coverage than competing methods under most conditions. The influence of the linear objective function on the sampling (the linear bias) constrains optimisation results to a subspace of optimal solutions all producing the same maximal fluxes. Visualisation of reaction fluxes plotted against each other in 2 dimensions with and without the linear bias indicates the existence of correlations between fluxes. This method of sampling is applied to the organism Actinobacillus succinogenes for the production of succinic acid from glycerol. Conclusions A new method of sampling for the generation of different flux distributions (sets of individual fluxes satisfying constraints on the steady-state mass balances of intermediates) has been developed using a relatively simple modification of flux balance analysis to include a poling penalty function inside the resulting optimisation objective function. This new methodology can achieve a high coverage of the possible flux space and can be used with and without linear bias to show optimal versus sub-optimal solution spaces. Basic analysis of the Actinobacillus succinogenes system using sampling shows that in order to achieve the maximal succinic acid production CO 2 must be taken into the system. Solutions involving release of CO 2 all give sub-optimal succinic acid production.