Laureano Jiménez

Microalgae-based biodiesel: A multicriteria analysis of the production process using realistic scenarios

Microalgae-based biodiesel has several benefits over other resources such as less land use, potential cultivation in non-fertile locations, faster growth and especially a high lipid-to-biodiesel yield. Nevertheless, the environmental and economic behavior for high scale production depends on several variables that must be addressed in the scale-up procedure. In this sense, rigorous modeling and multicriteria evaluation are performed in order to achieve optimal topology for third generation biodiesel production. Different scenarios and the most promising technologies tested at pilot scale are assessed. Besides, the sensitivity analysis allows the detection of key operating variables and assumptions that have a direct effect on the lipid content. The deviation of these variables may lead to an erroneous estimation of the scale-up performance of the technology reviewed in the microalgae-based biodiesel process. The modeling and evaluation of different scenarios of the harvesting, oil extraction and transesterification help to identify greener and cheaper alternatives.

The scope of application of multi-agent systems in the process industry: three case studies

Abstract It has been suggested that multi-agent systems (MAS) are specially adequate for the solution of problems with a dynamic, uncertain and distributed nature. Within industrial applications, there is a wide spectrum of problems with these characteristics, in particular those covering the modelling of artifacts, methodologies and organisations. Three case studies on the application of MAS in the process industry are presented. All of them relate to tools that are being developed to support very diverse core tasks in the process industry (and, by extension, the petroleum industry): • An intelligent search system composed of Internet information agents which are able to gather, compile and classify data available in web pages related to a specific technological domain. This search engine is the first step towards the construction of a knowledge management platform that will allow chemical process industries to improve their capabilities to monitor, predict and respond to technological trends and challenges. • A system to support the concurrent design of processes, to ease communication between engineers who perform design and keep them informed about the progress of the design process. • A tool to support the configuration of work teams. This tool will assist in the configuration of the most suitable team for a specific project. It takes into account the ideal size of the team (2 to n members); its specific composition (managers, engineers/scientists, assistants, etc.); and the proposed type of organisation (centralised, tree hierarchy, etc.). These case studies are representative of a large variety of the possible applications of agent based systems in the process industry.

Microalgae-based biodiesel: economic analysis of downstream process realistic scenarios.

Microalgae oil has been identified as a reliable resource for biodiesel production due to its high lipid productivity and potential cultivation in non-fertile locations. However, high scale production of microalgae based biodiesel depends on the optimization of the entire process to be economically feasible. The selected strain, medium, harvesting methods, etc., sorely affects the ash content in the dry biomass which have a direct effect in the lipid content. Moreover, the suitable lipids for biodiesel production, some of the neutral/saponifiable, are only a fraction of the total ones (around 30% dry base biomass in the best case). The present work uses computational tools for the modeling of different scenarios of the harvesting, oil extraction and transesterification. This rigorous modeling approach detects process bottlenecks that could have led to an overestimation of the potentiality of the microalgae lipids as a resource for the biodiesel production.

Optimal Planning of Supply Chains for Bioethanol and Sugar Production with Economic and Environmental Concerns

Abstract This work addresses the design of supply chains (SC) for sugar/ethanol production with economic and environmental concerns. The design task is formulated as a bi-criterion mixed-integer linear program (MILP) that simultaneously minimizes the total cost of the network and its environmental performance over the entire life cycle of the product (i.e., sugar and ethanol). The capabilities of our approach are highlighted through a case study based on a real scenario, for which a set of Pareto optimal alternatives is calculated.

Optimization and evolution in metabolic pathways: Global optimization techniques in Generalized Mass Action models

Cells are natural factories that can adapt to changes in external conditions. Their adaptive responses to specific stress situations are a result of evolution. In theory, many alternative sets of coordinated changes in the activity of the enzymes of each pathway could allow for an appropriate adaptive readjustment of metabolism in response to stress. However, experimental and theoretical observations show that actual responses to specific changes follow fairly well defined patterns that suggest an evolutionary optimization of that response. Thus, it is important to identify functional effectiveness criteria that may explain why certain patterns of change in cellular components and activities during adaptive response have been preferably maintained over evolutionary time. Those functional effectiveness criteria define sets of physiological requirements that constrain the possible adaptive changes and lead to different operation principles that could explain the observed response. Understanding such operation principles can also facilitate biotechnological and metabolic engineering applications. Thus, developing methods that enable the analysis of cellular responses from the perspective of identifying operation principles may have strong theoretical and practical implications. In this paper we present one such method that was designed based on nonlinear global optimization techniques. Our methodology can be used with a special class of nonlinear kinetic models known as GMA models and it allows for a systematic characterization of the physiological requirements that may underlie the evolution of adaptive strategies.

Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models

BackgroundDesign of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA) models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization.ResultsBased on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC) models that extend the power-law formalism to deal with saturation and cooperativity.ConclusionsOur results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task.

Analysis of residue curve maps of reactive and extractive distillation units

Abstract The aim of this work is to focus in the simultaneous analysis of reactive and extractive distillation, including the solvent recovery system. Residue curve maps can provide valuable insights and design assistance for a variety of separation processes, in which reactive distillation has been incorporated. These graphical techniques reveal the sensitivity of design options by giving us a visual representation over the whole composition space and assist the engineer to detect separation constraints. Simulation results show the reaction effect in the diagram topology: existence and position of nodes, saddles and distillation boundaries (separatrices or surfaces). Advanced distillation synthesis and design techniques, which are now supported by available software tools, were used to develop case studies for extractive distillation, entrainer influence in reactive systems and reactive distillation analysis with consecutive and/or competitive reactions.

Deterministic global optimization algorithm based on outer approximation for the parameter estimation of nonlinear dynamic biological systems

BackgroundThe estimation of parameter values for mathematical models of biological systems is an optimization problem that is particularly challenging due to the nonlinearities involved. One major difficulty is the existence of multiple minima in which standard optimization methods may fall during the search. Deterministic global optimization methods overcome this limitation, ensuring convergence to the global optimum within a desired tolerance. Global optimization techniques are usually classified into stochastic and deterministic. The former typically lead to lower CPU times but offer no guarantee of convergence to the global minimum in a finite number of iterations. In contrast, deterministic methods provide solutions of a given quality (i.e., optimality gap), but tend to lead to large computational burdens.ResultsThis work presents a deterministic outer approximation-based algorithm for the global optimization of dynamic problems arising in the parameter estimation of models of biological systems. Our approach, which offers a theoretical guarantee of convergence to global minimum, is based on reformulating the set of ordinary differential equations into an equivalent set of algebraic equations through the use of orthogonal collocation methods, giving rise to a nonconvex nonlinear programming (NLP) problem. This nonconvex NLP is decomposed into two hierarchical levels: a master mixed-integer linear programming problem (MILP) that provides a rigorous lower bound on the optimal solution, and a reduced-space slave NLP that yields an upper bound. The algorithm iterates between these two levels until a termination criterion is satisfied.ConclusionThe capabilities of our approach were tested in two benchmark problems, in which the performance of our algorithm was compared with that of the commercial global optimization package BARON. The proposed strategy produced near optimal solutions (i.e., within a desired tolerance) in a fraction of the CPU time required by BARON.

Identification of regulatory structure and kinetic parameters of biochemical networks via mixed-integer dynamic optimization

BackgroundRecovering the network topology and associated kinetic parameter values from time-series data are central topics in systems biology. Nevertheless, methods that simultaneously do both are few and lack generality.ResultsHere, we present a rigorous approach for simultaneously estimating the parameters and regulatory topology of biochemical networks from time-series data. The parameter estimation task is formulated as a mixed-integer dynamic optimization problem with: (i) binary variables, used to model the existence of regulatory interactions and kinetic effects of metabolites in the network processes; and (ii) continuous variables, denoting metabolites concentrations and kinetic parameters values. The approach simultaneously optimizes the Akaike criterion, which captures the trade-off between complexity (measured by the number of parameters), and accuracy of the fitting. This simultaneous optimization mitigates a possible overfitting that could result from addition of spurious regulatory interactions.ConclusionThe capabilities of our approach were tested in one benchmark problem. Our algorithm is able to identify a set of plausible network topologies with their associated parameters.

Knowledge Exploitation from the Web

In the framework of Knowledge Management, the Internet can be a valuable source of information to produce new Knowledge. Here, an ontology-based web search system to ease the enterprise managers in the process of discovering new knowledge from the documents in the Internet is introduced. By means of a graphical user interface, the user of the system supplies an ontology in RDF to describe the domain of interest, and sets up some predefined parameters in order to constrain the search corpus. A distributed intelligent process works to achieve the levels of quality and quantity about the results that the user established. Several ideas about how to use this system and its application to seven real domains are also supplied.