Dissolved carbon dioxide and bicarbonate ions in fermentation broths can (independently) inhibit or promote microbial growth and productivity. In research facilities with a large number of fermenters, dissolved carbon dioxide sensors tend not to be used, and as a result this variable will generally go unmonitored, making the meaningful analysis of data more difficult. For aerobic fermentations, mass transfer of carbon dioxide can be described in an analogous way to oxygen transfer. The mass transfer coefficient for carbon dioxide is 0.89 times that for oxygen. The maximum dissolved carbon dioxide concentration as a function of exit gas composition is compared with the concentration obtained by assuming equilibrium between the broth and exit gas. The difference between these two concentrations is typically 20–40% of the equilibrium concentration. In large fermenters, a degree of plug flow behavior in the gas and the generally lower specific aeration rates will serve to produce a better approach to equilibrium than for research fermenters.

The presence of nonlinearities, e.g., stiction, hysteresis and backlash in a control valve limits the control loop performance. Stiction is the most common problem in spring-diaphragm type valves, which are widely used in the process industry. Though there have been many attempts (EnTech, 1998;Gerry and Ruel, 2001;Horch and Isaksson, 1998;Taha et al., 1996;Piipponen, 1996;McMillan, 1995) to understand the stiction phenomena and model it, there is lack of a proper model which can be understood and related directly to the practical situation as observed in a real valve in the process industry. This study focuses on the understanding, from real life data, of the mechanism that causes stiction and proposes a new data-driven model of stiction, which can be directly related to real valves. It compares simulation results generated using the proposed model with industrial data.

In continuous chemical processes, variations of process variables usually travel along propagation paths in the direction of flow. The aim of this study was to find a data-driven method for identifying the direction of variation propagation using historical process data. Transfer entropy is a recently proposed method based on the probability density function (PDF) that measures directionality of variation with respect to time. An industrial case study illustrates the method which detects the influence of a temperature controller on downstream temperature measurements. A reversal of directionality was noted during a disturbance and a physical explanation offered.

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