Whey is an abundant effluent in the production of cheese and casein. The biotechnological utilization of this economically important and nutritive source is limited mainly because of the presence of high percentages of lactose. This disaccharide has poor solubility, which can cause crystallization and insufficient sweetness in dairy food; additionally, part of the adult population suffers from associated lactose intolerance diseases. There are several methods to determine lactose such as spectrophotometry, polarimetry, infrared spectroscopy, titrimetry and chromatography. However these methods are tedious and time-consuming due to long sample preparation. These disadvantages stimulated the development of an enzymatic lactose biosensor. It employs two immobilized enzymes, beta-galactosidase and glucose oxidase and the quantitative analysis of lactose is based on determination of oxygen consumption in the enzymatic reaction. The influence of temperature on the biosensor signal was experimentally studied. It was observed that a nonlinear relationship exists between the electric response of the biosensor - provided by CAFCA (Computer Assisted Flow Control & Analysis - ANASYSCON, Hannover) - and lactose concentration. In this work, attempts were made to correlate these variables using a simple nonlinear model and multilayered neural networks, with the latter providing the best modeling of the experimental data.

The RPN indicates how potentially difficult it is for a given system to achieve the desired performance robustly. It reflects both the attainable performance of a system and its degree of directionality. Two new indices, RPN ratio and RPN difference are introduced to quantify how realizable a given desired performance can be. The predictions made by RPN are verified by closed-loop simulations. These indices are applied to quantify the IO-controllability of the quadruple-tank process.

The method presented here offers an effective and time saving tool for multivariable controller design. The relation between controller complexity and closed loop performance can easily be evaluated. The method consists of five steps: 1. A desired behavior of the closed loop system is specified. Considering the nonminimum phase part of the process model the closed loop attainable performance is determined. 2. The process model and the attainable performance are scaled by the RPN-scaling procedure. 3. This defines an "ideal" scaled controller, which is usually too complex to be realized. 4. The frequency response of the ideal scaled compensator is approximated by a simpler one with structure and order chosen by the user. 5. Since the approximation in frequency response is performed with the scaled system, it is necessary to return to the original system’s units.

The Pressure Swing Adsorption (PSA) units have been used as a low cost alternative to the usual gas separation processes. Its largest commercial application is for hydrogen purification systems. Several studies have been made about the simulation of pressure swing adsorption units, but there are only few reports on the optimization of such processes. The objective of this study is to simulate and optimize an industrial PSA unit for hydrogen purification. This unit consists of six beds, each of them have three layers of different kinds of adsorbents. The main impurities are methane, carbon monoxide and sulfidric gas. The product stream has 99.99% purity in hydrogen, and the recovery is around 90%. A mathematical model for a commercial PSA unit is developed. The cycle time and the pressure swing steps are optimized. All the features concerning with complex commercial processes are considered.