To develop a useful fermentation process model, it is first necessary to identify which batch operating parameters are critical in determining the process outcome. To identify critical processing inputs in large databases, we have explored the use of Decision Tree Analysis with the decision metrics of Gain (i.e., Shannon Entropy changes), Gain Ratio, and a multiple hypergeometric distribution. The usefulness of this approach lies in its ability to treat "categorical" variables, which are typical of archived fermentation databases, as well as "continuous" variables. In this work, we demonstrate the use of Decision Tree Analysis for the problem of optimizing recombinant green fluorescent protein production in E. coli. A database of 85 fermentations was generated to examine the effect of 15 process input parameters on final biomass yield, maximum recombinant protein concentration, and productivity. The use of Decision Tree Analysis led to a considerable reduction in the fermentation database through the identification of the significant as well as insignificant inputs. However, different decision metrics selected different inputs and different numbers of inputs to classify the data for each output.

This paper presents the regulation of the output voltage and inductor currents in a Single Ended Primary Inductor Converter (SEPIC), operating in the continuous conduction mode (CCM) using a sliding mode controller. Owing to the time varying nature of the SEPIC converter, designing a feedback controller is a challenging task. In order to improve the dynamic performance of the SEPIC, a Sliding Mode Controller (SMC) is developed. The developed SMC is designed by using a state space average model. The performance of the developed controller with the SEPIC converter is validated at different working conditions through Matlab simulations. It is also compared with the performance while using a PI controller. The results show that the designed controller gives very good output voltage regulation under different operating conditions such as a varying input voltage, changes in the load and component variations. A 48V, 46W experimental setup for has been developed in an analog platform to validate the performance of the proposed SMC.

This study discusses the design of a parallel-operated DC-DC single-ended primary-inductor converter (SEPIC) for low-voltage application and current sharing with a constant output voltage. A coupled inductor is used for parallel-connected SEPIC topology. Generally, two separate inductors require different ripple currents, but a coupled inductor has the advantage of using the same ripple current. Furthermore, tightly coupled inductors require only half of the ripple current that separate inductors use. In this proposed work, tightly coupled inductors are used. These produce an output that is more efficient than that from separate inductors. Two SEPICs are also connected in parallel using the coupled inductors with a single common controller. An analog control circuit is designed to generate pulse width modulation (PWM) signals and to fulfill the closed-loop control function. A stable output current-sharing strategy is proposed in this system. An experimental setup is developed for a 18.5 V, 60 W parallel SEPIC (PSEPIC) converter, and the results are verified. Results indicate that the PSEPIC provides good response for the variation of input voltage and sudden change in load.

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