Membrane fouling is a critical bottleneck to the widespread adoption of membrane separation processes. It diminishes the membrane permeability and results in high operational energy costs. The current study presents optimizing the operating parameters of a novel rotating biological contactor (RBC) integrated with an external membrane (RBC + ME) that combines membrane technology with an RBC. In the RBC + ME, the membrane panel is placed external to the bioreactor. Response surface methodology (RSM) is applied to optimize the membrane permeability through three operating parameters (hydraulic retention time (HRT), rotational disk speed, and sludge retention time (SRT)). The artificial neural networks (ANN) and support vector machine (SVM) are implemented to depict the statistical modelling approach using experimental data sets. The results showed that all three operating parameters contribute significantly to the performance of the bioreactor. RSM revealed an optimum value of 40.7 rpm disk rotational speed, 18 h HRT and 12.4 d SRT, respectively. An ANN model with ten hidden layers provides the highest R2 value, while the SVM model with the Bayesian optimizer provides the highest R2. RSM, ANN, and SVM models reveal the highest R-square values of 0.97, 0.99, and 0.99, respectively. Machine learning techniques help predict the model based on the experimental results and training data sets.
Edible bird's nest (EBN) is made up of sialylated-mucin glycoprotein with various health benefits due to its high antioxidative activity. However, as a macromolecule with distinct charged sialic acid and amino acids, fractions with different charges would have varied physicochemical properties and antioxidant activity, which have not been studied. Therefore, this study aimed to fractionate and purify the enzymatic hydrolysed of cleaned EBN (EBNhc) and EBN by-product (EBNhbyp) through anion exchange chromatography (AEC), and determine their molecular weights, physicochemical properties, and antioxidative activities. Overall, 26 fractionates were collected from enzymatic hydrolysate by AEC, which were classified into 5 fractions. It was found that the positively charged fraction of EBNhc (CF 1) and EBNhbyp (DF 1) showed the significantly highest (p