Bioprocesses are increasingly used for the production of high added value products. Microorganisms in bioprocesses to mediate or catalyze necessary reactions. This makes highly nonlinear and governing mechanisms complex. These complex can be modeled by a metabolic network that comprises all interactions within cells microbial population present bioprocess. The current state art bioprocess contr...

The application of fuzzy reasoning techniques and neural network structures to model-based predictive control (MPC) is studied. First, basic structures of MPC algorithms are reviewed. Then, applications of fuzzy systems of the Takagi-Sugeno type in explicit and numerical nonlinear MPC algorithms are presented. Next, many techniques using neural network modeling to improve structural or computat...

An input and output model is used for the development of a model based predictive control framework for linear model structures. Diierent MPC algorithms which are based on linear state space models or linear polynomial models t into this framework. A new identiication horizon is introduced in order to represent the past.

The aim of this paper is to demonstrate dissolved oxygen and nitrate control in a wastewater treatment-plant, and an integrated wastewater system, using model predictive control. This control is of importance in maintaining the conditions required for aquatic organisms and so, the entire dynamics must be considered: the urban sewer system, water treatment plant, and river itself. The integrated...

Diabetes is recognized as a group of heterogeneous disorders with the common elements of high blood glucose concentration and glucose intolerance, due to insulin deficiency and impaired effectiveness of insulin action. The most common types of diabetes are diabetes mellitus type 1 and diabetes mellitus type 2, which does not necessarily require insulin injections. Type 2 constitutes about 85 to...

In this paper we present a method of hybrid predictive control (HPC) based on a fuzzy model. The identification methodology for a nonlinear system with discrete state-space variables based on combining fuzzy clustering and principal component analysis is proposed. The fuzzy model is used for HPC design, where the optimization problem is solved by the use of genetic algorithms (GAs). An illustra...