رویکرد ترکیبی مبتنی بر بهینه سازی شبیه سازی برای طراحی بهینه فرآیندهای بیوتکنولوژی تک محصوله

In this work, we present a systematic method for the optimal development of bioprocesses that relies on the combined use of simulation packages and optimization tools. One of the main advantages of our method is that it allows for the simultaneous optimization of all the individual components of a bioprocess, including the main upstream and downstream units. The design task is mathematically formulated as a mixed-integer dynamic optimization (MIDO) problem, which is solved by a decomposition method that iterates between primal and master sub-problems. The primal dynamic optimization problem optimizes the operating conditions, bioreactor kinetics and equipment sizes, whereas the master levels entails the solution of a tailored mixed-integer linear programming (MILP) model that decides on the values of the integer variables (i.e., number of equipments in parallel and topological decisions). The dynamic optimization primal sub-problems are solved via a sequential approach that integrates the process simulator SuperPro Designer® with an external NLP solver implemented in Matlab®. The capabilities of the proposed methodology are illustrated through its application to a typical fermentation process and to the production of the amino acid L-lysine.

Because of their potential to produce high-value products in human health and care, bioprocesses have recently gained wider interest. The recent boost in competitiveness for customers and new products experienced in this sector has created a clear need for modeling and optimization tools to assist decision-makers in the early stages of the process development. A bioprocess is a special type of chemical process that produces biochemical products (e.g. antibiotics, proteins, amino acids, etc.) from microorganisms or enzymes. Bioprocesses share some common features with general chemical processes, but differ in their kinetics of product formation, process structure (unit operations and procedures) and operating constraints (Heinzle, Biwer, & Cooney, 2006a). Optimization approaches devised so far in biotechnology have primarily focused on the bioreactor step. Cuthrell and Biegler (1989) optimized a fed-batch reactor for penicillin production with a solution strategy based on successive quadratic programming (SQP) and orthogonal collocation on finite elements. Carrasco and Banga (1997) addressed the dynamic optimization of batch and fed-batch reactors using stochastic optimization algorithms. More recently, Banga, Moles, Balsa-Canto, and Alonso (2005) introduced a new solution method for this problem based on control parameterization, whereas Sarkar and Modak (2005) proposed the use of genetic algorithms in this context. For an extensive review of dynamic optimization of bioreactors, the reader is referred to Banga, Moles, Balsa-Canto, and Alonso (2003). Another area related with the bioreactor step that has received attention in the literature is the optimization of metabolic networks. Raghunathan, Pérez-Correa, and Biegler (2003) addressed the data reconciliation and parameter estimation problems in metabolic networks, whereas Guillen-Gosálbez and Sorribas (2009) and Pozo et al. (2010) have proposed deterministic global optimization techniques for kinetic models of metabolic networks that assist in biotechnological and evolutive studies. In contrast to these approaches, the optimization of complete bioprocesses considering all their individual steps has received very little attention to date. This can be attributed to the fact that these problems lead to complex formulations that integrate structural and operating decisions, some of which change over time. To the best of our knowledge, the work by Groep, Gregory, Kershenbaum, and Bogle (2000), is the only one that addressed the optimization of a entire bioprocess (i.e., production of an intracellular enzyme alcohol dehydrogenase). This pioneering work has two main limitations: (i) it assumed a fixed plant topology; and (ii) it applied a simple sensitivity analysis to optimize the operating variables of the process that is not guaranteed to converge to a local (or global) optimum. Hence, it seems clear that the rich theory available for synthesizing standard chemical process flowsheets has not been applied to the same extent to their biochemical counterparts. In fact, the design of bioprocess flowsheets is nowadays typically accomplished by empirical and/or intuitive methods such as rules of thumb or simple heuristics (Petrides et al., 1996, Koulouris et al., 2000, Wong et al., 2004 and Petrides et al., 2006) that are likely to lead to sub-optimal process alternatives. With this observation in mind, the aim of this paper is to present a systematic tool for the design of bioprocesses that relies on the combined use of simulation and optimization techniques. More precisely, the design task is formulated as a mixed-integer dynamic optimization (MIDO) problem, which is solved by a hybrid simulation-optimization decomposition method that exploits the complementary strengths of optimization tools (i.e., nonlinear programming, NLP, and mixed-integer linear programming, MILP) and commercial bioprocess simulators (i.e., SuperPro Designer®). Our methodology has been tested using two different examples: a typical fermentation process and the production of the amino acid L-lysine.

This work has introduced a systematic strategy to assist in the development of biotechnological processes that allows to optimize the operating conditions and topology of the entire bioprocess. The proposed method relies on a reduced space MIDO algorithm that integrates commercial process simulators (SuperPro Designer®) with optimization tools (Matlab® and GAMS®). The capabilities of the method presented have been tested in two biotechnological examples: a typical fermentation process, and the production of the amino acid L-lysine. From numerical results, we concluded that it is possible to significantly improve the economic performance of bioprocesses by optimizing them as a whole. Particularly, larger benefits can be attained by properly adjusting the operating conditions and equipment sizes of all the units embedded in the flowsheet. One of the main advantages of our approach is that it makes use of a standard bioprocess simulation package that implements the main process and economic equations. This largely simplifies the modeling and economic analysis of the whole plant, allowing for the optimization of a wide range of bioprocess facilities.