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The intrinsic growth, substrate uptake, and product formation biokinetic parameters were obtained for the anaerobic bacterium, Clostridium ljungdahlii, grown on synthesis gas in various pressurized batch bioreactors. A dual-substrate growth kinetic model using Luong for CO and Monod for H2 was used to describe the growth kinetics of the bacterium on these substrates. The maximum specific growth rate (μ max = 0.195 h−1) and Monod constants for CO (K s,CO = 0.855 atm) and H2 (K s,H2 = 0.412 atm) were obtained. This model also accommodated the CO inhibitory effects on cell growth at high CO partial pressures, where no growth was apparent at high dissolved CO tensions (P CO ∗ > 0.743 atm). The Volterra model, Andrews, and modified Gompertz were, respectively, adopted to describe the cell growth, substrate uptake rate, and product formation. The maximum specific CO uptake rate (q max = 34.364 mmol/gcell/h), CO inhibition constant (K I = 0.601 atm), and maximum rate of ethanol (R max = 0.172 mmol/L/h at P CO = 0.598 atm) and acetate (R max = 0.096 mmol/L/h at P CO = 0.539 atm) production were determined from the applied models.

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Polymerization process can be classified as a nonlinear type process since it exhibits a dynamic behaviour throughout the process. Therefore, it is highly complicated to obtain an accurate mechanistic model from the nonlinear process. This predicament always been a "wall" to researchers to be able to devise an optimal process model and control scheme for such a system. Neural networks have succeeded the other modelling and control methods especially in coping with nonlinear process due to their very conciliate characteristics. These characteristics are further explained in this work. The predicament that is encountered by researchers nowadays is lack of data which consequently lead to an imprecise mechanistic model that scarcely conforms to the desired process. The implementations of the neural network model not only restrict to polymerization reactor but to other difficult-to-measure parameters such as polymer quality, polymer melts index and mixture of initiators. This work is aimed to manifest ascendancy of neural networks in modelling and control of polymerization process.Le processus de polymérisation peutêtre catégorisé comme un processus de type non linéaire, puisqu'il affiche un comportement dynamique tout au long du processus. Par conséquent, il est très compliqué d'obtenir un modèle mécaniste précis du processus non linéaire. Cette situation fâcheuse a toujours représenté un « mur » pour les chercheurs qui souhaitent concevoir un schéma de contrôle et de modélisation du processus optimal pour un tel système. Les réseaux neutres ont succédé aux autres méthodes de modélisation et de contrôle, surtout pour s'occuper du processus non linéaire en raison de leurs caractéristiques très conciliantes. Ces caractéristiques sont expliquées plus en détail dans ce travail. La situation fâcheuse rencontrée par les chercheurs aujourd'hui est le manque de données, ce qui mène par conséquentà un modèle mécaniste imprécis qui està peine conforme au processus souhaité. Les mises en oeuvre du modèle des réseaux neutres se limitent non seulement au réacteur de polymérisation, mais aux autres paramètres difficilesà mesurer, comme la qualité du polymère, l'indice de fluidité du polymère et le mélange des initiateurs. Ce travail chercheà manifester l'ascendance des réseaux neutres en modélisant et contrôlant le processus de polymérisation.

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Terpenes and terpenoids are among the key impact substances in the food and fragrance industries. Equipped with pharmacological properties and applications as ideal precursors for the biotechnological production of natural aroma chemicals, interests in these compounds have been escalating. Hence, the syntheses of new derivatives that can show improved properties are often called for. Stereoselective biotransformation offers several benefits to increase the rate of production, in terms of both the percentage yield and its enantiomeric excesses. Baker's yeast (Saccharomyces cerevisiae) is broadly used as a whole cell stereospecific reduction biocatalyst, due to its capability in reducing carbonyls and carbon-carbon double bonds, which also extends its functionality as a versatile biocatalyst in terpenoid biotransformation. This review provides some insights on the development and prospects in the reductive biotransformation of monoterpenoids and sesquiterpenoids using S. cerevisiae, with an overview of strategies to overcome the common challenges in large-scale implementation.