An integrated biorefinery framework for the coproduction of biofuels and chemicals: Experimental analysis, detailed modelling, optimization and life cycle analysis

In this work, a novel integrated biorefinery framework is introduced. A 'cradle to grave' analysis is developed by adding novel steps into a basic biodiesel process involving the valorization of waste streams to value-added chemicals. Insights are given towards a new bioconversion route of glycerol to succinic acid. An unstructured model of batch experiments at different conditions is constructed. Experimental results at the bench scale are used to estimate kinetic parameters and to validate model predictions. The developed model is used in optimization studies to compute the best initial conditions for batch as well as the optimal feeding profiles for fed-batch processes to maximize succinic acid productivity. Finally, the above process is incorporated into a biorefinery scheme. Simulation and optimization in conjunction with life cycle analysis (LCA) is performed to simultaneously improve its sustainability and its economics. Copyright © 2010, AIDIC Servizi S.r.l

Production of lipid-based fuels and chemicals from microalgae: An integrated experimental and model-based optimization study

Abstract Cultivation of microalgae is a promising long-term, sustainable candidate for biomass and oil for the production of fuel, food, nutraceuticals and other added-value products. Attention has been drawn to the use of computational and experimental validation studies aiming at the optimisation and the control of microalgal oil productivity either through the improvement of the growth mechanism or through the application of metabolic engineering methods to microalgae. Optimisation of such a system can be achieved through the evaluation of organic carbon sources, nutrients and water supply, leading to high oil yield. The main objective of this work is to develop a novel integrated experimental and computational approach, utilising a microalgal strain grown at bench-scale, with the aim to systematically identify the conditions that optimise growth and lipid production, in order to ultimately develop a cost-effective process to improve the system economic viability and overall sustainability. To achieve this, a detailed model has been constructed through a multi-parameter quantification methodology taking into account photo-heterotrophic biomass growth. The corresponding growth rate is based on carbon substrate concentration, nitrogen and light availability. The developed model also considers the pH of the medium. Parameter estimation was undertaken using the proposed model in conjunction with an extensive number of experimental data taken at a range of operating conditions. The model was validated and utilised to determine the optimal operating conditions for bench-scale batch lipid oil production

Anaerobic fermentation of glycerol: a platform for renewable fuels and chemicals

To ensure the long-term viability of biorefineries, it is essential to go beyond the carbohydrate-based platform and develop complementing technologies capable of producing fuels and chemicals from a wide array of available materials. Glycerol, a readily available and inexpensive compound, is generated during biodiesel, oleochemical, and bioethanol production processes, making its conversion into value-added products of great interest. The high degree of reduction of carbon atoms in glycerol confers the ability to produce fuels and reduced chemicals at higher yields when compared to the use of carbohydrates. This review focuses on current engineering efforts as well as the challenges involved in the utilization of glycerol as a carbon source for the production of fuels and chemicals

Sampling with poling-based flux balance analysis: optimal versus sub-optimal flux space analysis of Actinobacillus succinogenes

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. 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. 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 CO2 must be taken into the system. Solutions involving release of CO2 all give sub-optimal succinic acid production

LCA and LCC of emerging and incumbent technologies on energy harvesters

In this study, life cycle assessment and life cycle costing results about piezoelectric and thermoelectric materials for energy harvesters (EHs) are extracted from the literature and evaluated. This study serves as a basis for comparing current EHs with innovative EHs that will be developed within the Horizon 2020 FAST SMART project. FAST—SMART aims at increasing the performance of current EHs while reducing at the same time: The use of rare elements and toxic substances; resources and energy consumption; environmental impact and costs; paving the way for the adoption of new and more environmental-friendly systems for energy harvesting

Compost stream as a potential biomass for humic acid production: Focus on compost seasonal and geographical variability

Compost is a voluminous stream rich in humic and fulvic acids, which may be recovered as high-added value compounds. These soluble bio-based lignin-like polymeric substances (SBO) can be extracted through a completely green process developed at pilot scale, whose main core is the hydrolytic route in aqueous solutions at relatively mild temperature (< 140 °C) at ACEA Pinerolese Industriale premises. Due to their chemical-physical properties, the SBO compounds can be used with advantage for myriads of industrial applications, from the formulation of detergents to the production of agriculture biostimulants, answering the increasing demand for bio-compound utilization. In view of LIFECAB project (LIFE16 ENV/IT/000179), the characterization of starting materials and the derived compost has been performed over four seasons and over three European countries (Italy, Greece ad Cyprus). In view of establishing a relationship between SBO molecules and compost properties, this work is a challenging opportunity for assessing the compost variability and its temporal evolution during the composting process. Analyses of pH, salinity, total carbon, total nitrogen and C/N ratio, critically assessed by means of a statistical approach, provide important information about compost composition according to the season and to the local environmental conditions

Modeling of Stochastic Non-Linear Chemical Reaction Networks

University of Minnesota Ph.D. dissertation. June 2018. Major: Chemical Engineering. Advisor: Prodromos Daoutidis. 1 computer file (PDF); xi, 133 pages.Biological systems are wonderfully complex. In order to gain a better understanding on how the complexity dictates the biological functions and gives rise to different cellular phenotypes, it is important to investigate the underlying dynamic interactions of the biomolecular components. Traditionally, these system dynamics have been described using continuous deterministic mathematical models. However, small biomolecular systems are inherently stochastic. Indeed, fluctuations of molecular species are substantial in living organisms and may result in significant variation in cellular phenotypes. Since stochastic models view molecules as discrete chemical entities and the chemical events as random processes it is possible to better investigate the variability of cellular processes in detail and to gain fundamental insight. Event though, stochastic reaction models are useful tools to describe the behavior of many biological systems, there remains a lack of numerical methods available to calculate the stationary probability distributions of stochastic reaction networks. The Chemical Master Equation (CME), which is the most detailed mathematical model that can describe stochastic behaviors, has minimal contribution due to its complexity. This work emphasizes on solving CME by a set of differential equations of probability moments, called moment equations; especially by employing Zero-Information closure scheme (ZI closure scheme). ZI closure scheme calculates the stationary probability distribution of stochastic biochemical reaction networks. The method postulates that only a finite number of probability moments is necessary to capture all of the system's information, which can be achieved by maximizing the information entropy of the system. The work presented herein focuses on improving ZI closure scheme and resolve its numerical difficulties. The updated ZI closure scheme is a powerful tool for calculating stationary probability distribution for a variety of stochastic networks. Furthermore, we use the updated scheme to explore why deterministic and stochastic models of chemical reaction kinetics can give starkly different results when the deterministic model exhibits more than one stable solution. The closure scheme makes it possible to investigate the behavior of a system for a wide variety of volumes. For the first time, results for the mesoscopic region of stochastic networks are obtained with a numerical method. This work also focuses on creating approaches for calculating the time transient behavior of stochastic reaction networks, since most of the numerical methods focus only on stationary probability distribution. We first tackle the issue by linearizing the moment equations and calculating the Jacobian matrix around the stationary probability distribution. The calculations are accurate and significantly more efficient than stochastic simulation algorithms based on Gillespie's algorithms. However, the approach fails to predict oscillatory behaviors. To further improve this approach, we present a new alternative method based on equations that depend only on Lagrange multipliers. The Lagrange multiplier equations approach is able to calculate the time-transient behavior of stochastic reaction networks, based on the maximum entropy principle. This work is an innovative step towards solving stochastic networks for different time behaviors