Kinetic Parameter Determination for Enzyme Hydrolysis of Fish Protein Residue Using D-optimal Design

Kinetic Parameter Determination for Depolymerization of Biomass by Inverse Modeling and Metaheuristics

A computational methodology based on inverse modeling and metaheuristics is presented for determining the best parameters of kinetic models aimed to predict the behavior of biomass depolymerization processes during size scaling up. The Univariate Marginal Distribution algorithm, particle swarm optimization, and Interior-Point algorithm were applied to obtain the values of the kinetic parameters (KM and Vmax) of four mathematical models based on the Michaelis–Menten equation: (i) Traditional Michaelis–Menten, (ii) non-competitive inhibition, (iii) competitive inhibition, and (iv) substrate inhibition. The kinetic data were obtained from our own experimentation in micro-scale. The parameters obtained from an optimized micro-scale experiment were compared with a bench scale experiment (0.5 L). Regarding the metaheuristic optimizers, it is concluded that the Interior-Point algorithm is effective in solving inverse modeling problems and has the best prediction power. According to the results, the Traditional model adequately describes the micro-scale experiments. It was found that the Traditional model with optimized parameters was able to predict the behavior of the depolymerization process during size scaling up. The methodology followed in this study can be adopted as a starting point for the solution of future inverse modeling problems.

Biochemical methane potential (BMP) tests: Reducing test time by early parameter estimation

Biochemical methane potential (BMP) test is a key analytical technique to assess the implementation and optimisation of anaerobic biotechnologies. However, this technique is characterised by long testing times (from 20 to >100days), which is not suitable for waste utilities, consulting companies or plants operators whose decision-making processes cannot be held for such a long time. This study develops a statistically robust mathematical strategy using sensitivity functions for early prediction of BMP first-order model parameters, i.e. methane yield (B₀) and kinetic constant rate (k). The minimum testing time for early parameter estimation showed a potential correlation with the k value, where (i) slowly biodegradable substrates (k≤0.1d^-1) have a minimum testing times of ≥15days, (ii) moderately biodegradable substrates (0.1<k<0.2d^-1) have a minimum testing times between 8 and 15 days, and (iii) rapidly biodegradable substrates (k≥0.2d^-1) have testing times lower than 7days.

Investigating thiol-modification on hyaluronan via carbodiimide chemistry using response surface methodology

Hyaluronan (HA) is a naturally occurring glycosaminoglycan widely researched for its use as a biomaterial in tissue engineering, drug delivery, angiogenesis, and ophthalmic surgeries. The mechanical properties of this biomaterial can be altered to a required extent by chemically modifying the pendant reactive groups. However, derivatizing these polymers to a predetermined extent has been the Achilles heel for this process. In this study, we have investigated the factors controlling the derivatization of the carboxyl moieties of HA with amine containing thiol, cystamine dihydrochloride (Cys), via carbodiimide crosslinking chemistry. We used fractional factorial design to screen and identify the significant factor(s) affecting the reaction, and response surface methodology (RSM) to develop a model equation for predicting the degree of thiolation of HA. Also, we analyzed the reaction mechanism for potential side reactions. We observed that N-(3-dimethylaminopropyl)-N'-ethylcarbodiimide hydrochloride (EDC) (mole ratio with repeat unit of HA) is the significant factor controlling the degree of amidation. The quadratic equations developed from RSM predict the formulation for a desired degree of amidation of HA and percentage of potential side product. Hence, derivatizing HA to a predetermined extent with minimal side product can be achieved using the statistical design of experiments.