The problem of integrated design and control of bioprocess plants is considered. A pre®iously presented optimization approach for biochemical systems based on linear ( programming and modeling using the power law formalism the Indirect Optimization ) Method, IOM is extended. This method is enhanced in order to take into account both static and dynamic measures, and its use for the optimization of the integrated design of a bioprocess is illustrated. The chosen case study is a wastewater treatment plant, a bioprocess which typically presents controllability problems in real practice due to bad design methodologies. After defining an objecti®e function reflecting both in®estment ( ) costs and '' paracosts'' such as stability, flexibility, and controllability , a set of constraints determined by the system components and technical and economical factors is defined. A comparison of the results obtained with this new method and with a global optimization method re®eals that, in both cases, significant impro®ements in both controllability and cost reduction are achie®ed, although the global method yields somewhat better impro®ements. The ad®antages and limitations of both methods are e®aluated, concluding that the IOM, through its incorporation to a dynamic process simulator, can be successfully used to obtain, in a quick, inexpensi®e and interacti®e way, near-optimal integrated designs for bioprocess plants.
IntroductionThe proper design and operation of any microbial-based biotechnological process requires the quantitative description of the variables relevant for the system's kinetics. Once this information is available, it will be possible to derive an optimal process design and to attain its optimal operation. However, the nonlinear nature of these processes has impaired Ž such optimizations Vagners, 1983;Heinrich and Schuster, . 1996 . In order to circumvent these difficulties chemical and biochemical engineers have developed strategies to minimize or even avoid nonlinearities. Focusing on specific properties of chemical networks and by using prudent approximations, different techniques and approaches have been developed within the field of process engineering, which permits the process improvement through the right choice of operating Ž parameters Stephanopoulos, 1998;Stephanopoulos et al., . 1998 . Correspondence concerning this article should be addressed to N. V. Torres.One approach that fully acknowledges the nonlinearities, yet ultimately leads to linear optimization tasks, is based on a modeling framework called Biochemical Systems Theory Ž . Savageau 1969a,b; Voit, 2000 . The hallmark of this theory is the approximation of rate laws and other processes with products of power-law functions. Mathematically, this type of representation is very convenient and solidly grounded in Taylor's theory of approximating functions with polynomials. The Biochemical System Theory framework offers some choices for the formulation of biochemical systems, among which the most relevant for our present purposes is the S-sys-Ž...
